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Lyoo YW, Lee H, Lee J, Park JH, Hwang I, Chung JW, Choi SH, Yoo J, Choi KS. Deep learning enhances reliability of dynamic contrast-enhanced MRI in diffuse gliomas: bypassing post-processing and providing uncertainty maps. Eur Radiol 2025:10.1007/s00330-025-11588-z. [PMID: 40252095 DOI: 10.1007/s00330-025-11588-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2024] [Revised: 02/18/2025] [Accepted: 03/15/2025] [Indexed: 04/21/2025]
Abstract
OBJECTIVES To propose and evaluate a novel deep learning model for directly estimating pharmacokinetic (PK) parameter maps and uncertainty estimation from DCE-MRI. METHODS In this single-center study, patients with adult-type diffuse gliomas who underwent preoperative DCE-MRI from Apr 2010 to Feb 2020 were retrospectively enrolled. A spatiotemporal probabilistic model was used to create synthetic PK maps. Structural Similarity Index Measure (SSIM) to ground truth (GT) maps were calculated. Reliability was evaluated using the intraclass correlation coefficient (ICC) for synthetic and GT PK maps. For clinical validation, Area Under the Receiver Operating Characteristic Curve (AUROC) was obtained for predicting WHO low vs high grade and IDH-wildtype vs mutant. RESULTS 329 patients (mean age, 55 ± 15 years, 197 men) were eligible. Synthetic Ktrans, Vp, Ve maps showed high SSIM (0.961, 0.962, 0.890) compared to the GT maps. The ICC of PK maps was significantly higher in synthetic PK maps compared to the conventional approach: 1.00 vs 0.68 (p < 0.001) for Ktrans, 1.00 vs 0.59 (p < 0.001) for Vp, 1.00 vs 0.64 (p < 0.001) for Ve. PK values of enhancing tumor portion obtained from synthetic and GT maps were comparable in AUROC: (1) Ktrans, 0.857 vs 0.842 (p = 0.57); Vp, 0.864 vs 0.835 (p = 0.31); and Ve, 0.835 vs 0.830 (p = 0.88) for mutation prediction. (2) Ktrans, 0.934 vs 0.907 (p = 0.50); Vp, 0.927 vs 0.899 (p = 0.24); and Ve, 0.945 vs 0.910 (p = 0.24) for glioma grading. CONCLUSION Synthetic PK maps generated from DCE-MRI using a spatiotemporal probabilistic deep-learning model showed improved reliability without compromising diagnostic performance in glioma grading. KEY POINTS Question Can a deep learning model enhance the reliability of dynamic contrast-enhanced MRI (DCE-MRI) for more consistent and clinically acceptable glioma imaging? Findings A spatiotemporal deep learning model outperformed the Tofts model in Ktrans reliability and preserved diagnostic performance for IDH mutation and glioma grade, bypassing arterial input function estimation. Clinical relevance Enhancing DCE-MRI reliability with deep learning improves imaging consistency, supports molecular tumor characterization through reproducible pharmacokinetic maps, and enables personalized treatment planning, which might lead to better clinical outcomes for patients with diffuse gliomas.
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Affiliation(s)
- Young Wook Lyoo
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
| | - Haneol Lee
- Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea
| | - Junhyeok Lee
- Interdisciplinary Programs in Cancer Biology Major, Seoul National University Graduate School, Seoul, Republic of Korea
| | - Jung Hyun Park
- Department of Radiology, Seoul Metropolitan Government Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | - Inpyeong Hwang
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jin Wook Chung
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seung Hong Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jaejun Yoo
- Graduate School of Artificial Intelligence, Ulsan National Institute of Science and Technology (UNIST), Ulsan, Republic of Korea.
| | - Kyu Sung Choi
- Department of Radiology, Seoul National University Hospital, Seoul, Republic of Korea.
- Department of Radiology, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Shalom ES, Van Loo S, Khan A, Sourbron SP. Identifiability of spatiotemporal tissue perfusion models. Phys Med Biol 2024; 69:115034. [PMID: 38636525 DOI: 10.1088/1361-6560/ad4087] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 04/18/2024] [Indexed: 04/20/2024]
Abstract
Objective.Standard models for perfusion quantification in DCE-MRI produce a bias by treating voxels as isolated systems. Spatiotemporal models can remove this bias, but it is unknown whether they are fundamentally identifiable. The aim of this study is to investigate this question in silico using one-dimensional toy systems with a one-compartment blood flow model and a two-compartment perfusion model.Approach.For each of the two models, identifiability is explored theoretically and in-silico for three systems. Concentrations over space and time are simulated by forward propagation. Different levels of noise and temporal undersampling are added to investigate sensitivity to measurement error. Model parameters are fitted using a standard gradient descent algorithm, applied iteratively with a stepwise increasing time window. Model fitting is repeated with different initial values to probe uniqueness of the solution. Reconstruction accuracy is quantified for each parameter by comparison to the ground truth.Main results.Theoretical analysis shows that flows and volume fractions are only identifiable up to a constant, and that this degeneracy can be removed by proper choice of parameters. Simulations show that in all cases, the tissue concentrations can be reconstructed accurately. The one-compartment model shows accurate reconstruction of blood velocities and arterial input functions, independent of the initial values and robust to measurement error. The two-compartmental perfusion model was not fully identifiable, showing good reconstruction of arterial velocities and input functions, but multiple valid solutions for the perfusion parameters and venous velocities, and a strong sensitivity to measurement error in these parameters.Significance.These results support the use of one-compartment spatiotemporal flow models, but two-compartment perfusion models were not sufficiently identifiable. Future studies should investigate whether this degeneracy is resolved in more realistic 2D and 3D systems, by adding physically justified constraints, or by optimizing experimental parameters such as injection duration or temporal resolution.
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Affiliation(s)
- Eve S Shalom
- School of Physics and Astronomy, The University of Leeds, United Kingdom
| | - Sven Van Loo
- Department of Applied Physics, Ghent University, Belgium
| | - Amirul Khan
- School of Civil Engineering, The University of Leeds, United Kingdom
| | - Steven P Sourbron
- Division of Clinical Medicine, University of Sheffield, United Kingdom
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Shalom ES, Khan A, Van Loo S, Sourbron SP. Current status in spatiotemporal analysis of contrast-based perfusion MRI. Magn Reson Med 2024; 91:1136-1148. [PMID: 37929645 PMCID: PMC10962600 DOI: 10.1002/mrm.29906] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 10/05/2023] [Accepted: 10/10/2023] [Indexed: 11/07/2023]
Abstract
In perfusion MRI, image voxels form a spatially organized network of systems, all exchanging indicator with their immediate neighbors. Yet the current paradigm for perfusion MRI analysis treats all voxels or regions-of-interest as isolated systems supplied by a single global source. This simplification not only leads to long-recognized systematic errors but also fails to leverage the embedded spatial structure within the data. Since the early 2000s, a variety of models and implementations have been proposed to analyze systems with between-voxel interactions. In general, this leads to large and connected numerical inverse problems that are intractible with conventional computational methods. With recent advances in machine learning, however, these approaches are becoming practically feasible, opening up the way for a paradigm shift in the approach to perfusion MRI. This paper seeks to review the work in spatiotemporal modelling of perfusion MRI using a coherent, harmonized nomenclature and notation, with clear physical definitions and assumptions. The aim is to introduce clarity in the state-of-the-art of this promising new approach to perfusion MRI, and help to identify gaps of knowledge and priorities for future research.
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Affiliation(s)
- Eve S. Shalom
- School of Physics and AstronomyUniversity of LeedsLeedsUK
- Division of Clinical MedicineUniversity of SheffieldSheffieldUK
| | - Amirul Khan
- School of Civil EngineeringUniversity of LeedsLeedsUK
| | - Sven Van Loo
- School of Physics and AstronomyUniversity of LeedsLeedsUK
- Department of Applied PhysicsGhent UniversityGhentBelgium
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Sharma A, Garg A, Singh M, Sharma MC, Gupta S, Kunhiparambath H, Tripathi M, Kale SS, Bal C. Metabolic imaging in recurrent gliomas: comparative performance of 18F-FDOPA, 18F-fluorocholine and 18F-FDG PET/CT. Nucl Med Commun 2024; 45:139-147. [PMID: 38095139 DOI: 10.1097/mnm.0000000000001795] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
PURPOSE The aim of this study was to directly evaluate glucose, amino-acid and membrane metabolism in tumor cells for diagnosis and prognostication of recurrent gliomas. METHODS Fifty-five patients (median age = 36 years; 33 men) with histologically proven gliomas and suspected recurrence were prospectively recruited and underwent 18F-FDG (Fluorodeoxyglucose), 18F-FDOPA (fluorodopa) and 18F-Fluorocholine-PET/CT. Images were evaluated by two physicians visually and quantitatively [lesion-SUVmax, tumor (T) to gray-matter (G) and metabolically-active tumor volumes (MTV)]. After median follow-up of 51.5 months, recurrence was diagnosed in 49 patients. Thirty-one patients died with a median survival of 14 months. RESULTS Diagnostic-accuracies for 18F-FDOPA, 18F-Fluorocholine,18F-FDG and contrast-enhanced-MRI were 92.7% (95% CI 82.7-97.1), 81.8% (69.7-89.8), 45.5% (33.0-58.5) and 44.7% (30.2-60.3), respectively. Among the 20 lesions, missed by MRI; 18F-FDOPA, 18F-Fluorocholine and 18F-FDG were able to detect 19, 14 and 4 lesions. Corresponding area-under-the-curves (T/G ratios) were 0.817 (0.615-1.000), 0.850 (0.736-0.963) and 0.814 (0.658-0.969), when differentiating recurrence from treatment-induced changes. In univariate-survival-analysis, 18F-FDOPA-T/G, visually detectable recurrence in 18F-FDG, 18F-FDOPA-MTV, cell-lineage and treatment-type were significant parameters. In Multivariate-Cox-regression analysis, 18F-FDOPA-MTV [HR = 1.009 (1.001-1.017); P = 0.024 (~0.9% increase in hazard for every mL increase of MTV)] and cell-lineage [3.578 (1.447-8.846); P = 0.006] remained significant. 18F-FDOPA-MTV cutoff <29.59 mL predicted survival higher than 2 years. At cutoff ≥29.59 mL, HR at 2 years was 2.759 (1.310-5.810). CONCLUSION 18F-FDOPA-PET/CT can diagnose recurrence with high accuracy and MTV predicts survival. 18F-Fluorocholine is a good alternative. Higher 18F-FDG uptake is an adverse prognostic indicator.
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Affiliation(s)
- Anshul Sharma
- Department of Nuclear Medicine, All India Institute of Medical Sciences, Bilaspur, HP (Former resident at All India Institute of Medical Sciences, New-Delhi)
| | | | | | | | - Subhash Gupta
- Department of Radiation Oncology, Dr. B.R.A. Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences
| | - Haresh Kunhiparambath
- Department of Radiation Oncology, Dr. B.R.A. Institute-Rotary Cancer Hospital, All India Institute of Medical Sciences
| | - Madhavi Tripathi
- Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
| | | | - Chandrasekhar Bal
- Department of Nuclear Medicine, All India Institute of Medical Sciences, New Delhi, India
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Laothamatas I, Al Mubarak H, Reddy A, Wax R, Badani K, Taouli B, Bane O, Lewis S. Multiparametric MRI of Solid Renal Masses: Principles and Applications of Advanced Quantitative and Functional Methods for Tumor Diagnosis and Characterization. J Magn Reson Imaging 2023. [PMID: 37052601 DOI: 10.1002/jmri.28718] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Revised: 03/23/2023] [Accepted: 03/23/2023] [Indexed: 04/14/2023] Open
Abstract
Solid renal masses (SRMs) are increasingly detected and encompass both benign and malignant masses, with renal cell carcinoma (RCC) being the most common malignant SRM. Most patients with SRMs will undergo management without a priori pathologic confirmation. There is an unmet need to noninvasively diagnose and characterize RCCs, as significant variability in clinical behavior is observed and a wide range of differing management options exist. Cross-sectional imaging modalities, including magnetic resonance imaging (MRI), are increasingly used for SRM characterization. Multiparametric (mp) MRI techniques can provide insight into tumor biology by probing different physiologic/pathophysiologic processes noninvasively. These include sequences that probe tissue microstructure, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) and T1 relaxometry; oxygen metabolism (blood oxygen level dependent [BOLD-MRI]); as well as vascular flow and perfusion (dynamic contrast-enhanced MRI [DCE-MRI] and arterial spin labeling [ASL]). In this review, we will discuss each mpMRI method in terms of its principles, roles, and discuss the results of human studies for SRM assessment. Future validation of these methods may help to enable a personalized management approach for patients with SRM in the emerging era of precision medicine. EVIDENCE LEVEL: 5. TECHNICAL EFFICACY: 2.
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Affiliation(s)
- Indira Laothamatas
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Haitham Al Mubarak
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Arthi Reddy
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Rebecca Wax
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ketan Badani
- Department of Urology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Bachir Taouli
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Octavia Bane
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Sara Lewis
- Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, New York, USA
- BioMedical Engineering and Imaging Institute, Icahn School of Medicine at Mount Sinai, New York, New York, USA
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Cavalcanti YC, Oberlin T, Ferraris V, Dobigeon N, Ribeiro M, Tauber C. Compartment model-based nonlinear unmixing for kinetic analysis of dynamic PET images. Med Image Anal 2023; 84:102689. [PMID: 36502604 DOI: 10.1016/j.media.2022.102689] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2020] [Revised: 09/14/2022] [Accepted: 11/08/2022] [Indexed: 11/23/2022]
Abstract
When no arterial input function is available, quantification of dynamic PET images requires a previous step devoted to the extraction of a reference time-activity curve (TAC). Factor analysis is often applied for this purpose. This paper introduces a novel approach that conducts a new kind of nonlinear factor analysis relying on a compartment model, and computes the kinetic parameters of specific binding tissues jointly. To this end, it capitalizes on data-driven parametric imaging methods to provide a physical description of the underlying PET data, directly relating the specific binding with the kinetics of the non-specific binding in the corresponding tissues. This characterization is introduced into the factor analysis formulation to yield a novel nonlinear unmixing model designed for PET image analysis. This model also explicitly introduces global kinetic parameters that allow for a direct estimation of a binding potential that represents the ratio at equilibrium of specifically bound radioligand to the concentration of nondisplaceable radioligand in each non-specific binding tissue. The performance of the method is evaluated on synthetic and real data to demonstrate its potential interest.
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Affiliation(s)
| | | | - Vinicius Ferraris
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071, Toulouse Cedex 7, France.
| | - Nicolas Dobigeon
- University of Toulouse, IRIT/INP-ENSEEIHT, 31071, Toulouse Cedex 7, France; Institut Universitaire de France (IUF), France.
| | - Maria Ribeiro
- UMRS Inserm U930 - Université de Tours, 37032 Tours, France.
| | - Clovis Tauber
- UMRS Inserm U930 - Université de Tours, 37032 Tours, France.
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Milidonis X, Nazir MS, Chiribiri A. Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification. J Magn Reson Imaging 2022; 56:1707-1719. [PMID: 35338754 PMCID: PMC9790572 DOI: 10.1002/jmri.28180] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2022] [Revised: 03/14/2022] [Accepted: 03/15/2022] [Indexed: 01/04/2023] Open
Abstract
BACKGROUND Acquisition of magnetic resonance first-pass perfusion images is synchronized to the patient's heart rate (HR) and governs the temporal resolution. This is inherently linked to the process of myocardial blood flow (MBF) quantification and impacts MBF accuracy but to an unclear extent. PURPOSE To assess the impact of temporal resolution on quantitative perfusion and compare approaches for accounting for its variability. STUDY TYPE Prospective phantom and retrospective clinical study. POPULATION AND PHANTOM Simulations, a cardiac perfusion phantom, and 30 patients with (16, 53%) or without (14, 47%) coronary artery disease. FIELD STRENGTH/SEQUENCE 3.0 T/2D saturation recovery spoiled gradient echo sequence. ASSESSMENT Dynamic perfusion data were simulated for a range of reference MBF (1 mL/g/min-5 mL/g/min) and HR (30 bpm-150 bpm). Perfusion imaging was performed in patients and a phantom for different temporal resolutions. MBF and myocardial perfusion reserve (MPR) were quantified without correction for temporal resolution or following correction by either MBF scaling based on the sampling interval or data interpolation prior to quantification. Simulated data were quantified using Fermi deconvolution, truncated singular value decomposition, and one-compartment modeling, whereas phantom and clinical data were quantified using Fermi deconvolution alone. STATISTICAL TESTS Shapiro-Wilk tests for normality, percentage error (PE) for measuring MBF accuracy in simulations, and one-way repeated measures analysis of variance with Bonferroni correction to compare clinical MBF and MPR. Statistical significance set at P < 0.05. RESULTS For Fermi deconvolution and an example simulated 1 mL/g/min, the MBF PE without correction for temporal resolution was between 55.4% and -62.7% across 30-150 bpm. PE was between -22.2% and -6.8% following MBF scaling and between -14.2% and -14.2% following data interpolation across the same HR. An interpolated HR of 240 bpm reduced PE to ≤10%. Clinical rest and stress MBF and MPR were significantly different between analyses. DATA CONCLUSION Accurate perfusion quantification needs to account for the variability of temporal resolution, with data interpolation prior to quantification reducing MBF variability across different resolutions. LEVEL OF EVIDENCE 3 TECHNICAL EFFICACY STAGE: 1.
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Affiliation(s)
- Xenios Milidonis
- School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
| | | | - Amedeo Chiribiri
- School of Biomedical Engineering & Imaging SciencesKing's College LondonLondonUK
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KALA D, ŠULC V, OLŠEROVÁ A, SVOBODA J, PRYSIAZHNIUK Y, POŠUSTA A, KYNČL M, ŠANDA J, TOMEK A, OTÁHAL J. Evaluation of blood-brain barrier integrity by the analysis of dynamic contrast-enhanced MRI - a comparison of quantitative and semi-quantitative methods. Physiol Res 2022; 71:S259-S275. [PMID: 36647914 PMCID: PMC9906669 DOI: 10.33549/physiolres.934998] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/26/2023] Open
Abstract
Disruption of the blood-brain barrier (BBB) is a key feature of various brain disorders. To assess its integrity a parametrization of dynamic magnetic resonance imaging (DCE MRI) with a contrast agent (CA) is broadly used. Parametrization can be done quantitatively or semi-quantitatively. Quantitative methods directly describe BBB permeability but exhibit several drawbacks such as high computation demands, reproducibility issues, or low robustness. Semi-quantitative methods are fast to compute, simply mathematically described, and robust, however, they do not describe the status of BBB directly but only as a variation of CA concentration in measured tissue. Our goal was to elucidate differences between five semi-quantitative parameters: maximal intensity (Imax), normalized permeability index (NPI), and difference in DCE values between three timepoints: baseline, 5 min, and 15 min (delta5-0, delta15-0, delta15-5) and two quantitative parameters: transfer constant (Ktrans) and an extravascular fraction (Ve). For the purpose of comparison, we analyzed DCE data of four patients 12-15 days after the stroke with visible CA enhancement. Calculated parameters showed abnormalities spatially corresponding with the ischemic lesion, however, findings in individual parameters morphometrically differed. Ktrans and Ve were highly correlated. Delta5-0 and delta15-0 were prominent in regions with rapid CA enhancement and highly correlated with Ktrans. Abnormalities in delta15-5 and NPI were more homogenous with less variable values, smoother borders, and less detail than Ktrans. Moreover, only delta15-5 and NPI were able to distinguish vessels from extravascular space. Our comparison provides important knowledge for understanding and interpreting parameters derived from DCE MRI by both quantitative and semi-quantitative methods.
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Affiliation(s)
- David KALA
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic,Faculty of Electrical Engineering, Czech Technical University in Prague, Czech Republic
| | - Vlastimil ŠULC
- Department of Neurology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Anna OLŠEROVÁ
- Department of Neurology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Jan SVOBODA
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Yeva PRYSIAZHNIUK
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Antonín POŠUSTA
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic
| | - Martin KYNČL
- Department of Radiology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Jan ŠANDA
- Department of Radiology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Aleš TOMEK
- Department of Neurology, Second Faculty of Medicine, Charles University and University Hospital Motol, Prague, Czech Republic
| | - Jakub OTÁHAL
- Laboratory of Developmental Epileptology, Institute of Physiology of the Czech Academy of Sciences, Prague, Czech Republic,Department of Pathophysiology, Second Faculty of Medicine, Charles University, Czech Republic
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Sharma G, Saran S, Saxena S, Goyal T. Multiparametric evaluation of bone tumors utilising diffusion weighted imaging and dynamic contrast enhanced magnetic resonance imaging. J Clin Orthop Trauma 2022; 30:101899. [PMID: 35664690 PMCID: PMC9157202 DOI: 10.1016/j.jcot.2022.101899] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/07/2022] [Revised: 05/08/2022] [Accepted: 05/14/2022] [Indexed: 11/18/2022] Open
Abstract
AIM This study aimed to use multiparametric magnetic resonance imaging (MRI) techniques, namely, diffusion-weighted imaging (DWI) and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) to evaluate bone tumors. METHODS Thirty-three patients with primary untreated bone tumors were assessed utilizing DWI and DCE-MRI. Various parameters like ADC values from DWI and percentage peak signal intensity (%PSI), the maximum slope of increase (MSI), and time to peak signal intensity (TTP) values were assessed in different cases, and the final correlation was drawn with histopathological findings. RESULT Parameters of semi-quantitative DCE-MRI, i.e., %PSI, MSI and, TTP, correlated significantly with the histopathological characteristics of the tumor (p values < 0.001). Minimum ADC value in the tumor also showed a strong correlation with the tumor characteristic (p values < 0.001). Also, the correlation between parameters of DWI and DCI-MRI is well correlated with each other. CONCLUSION The results of this study provide grounds for the integration of multiparametric pre-treatment evaluation of bone tumors. In our study, we not only tried to utilize different parameters of functional MRI in bone tumors as well as re-explored the semi-quantitative analysis of DCE-MRI.
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Affiliation(s)
- Garima Sharma
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, India
| | - Sonal Saran
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, India
| | - Sudhir Saxena
- Department of Radiodiagnosis, All India Institute of Medical Sciences, Rishikesh, India
| | - Tarun Goyal
- Department of Orthopedics, All India Institute of Medical Sciences, Bhatinda, India
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Ottens T, Barbieri S, Orton MR, Klaassen R, van Laarhoven HW, Crezee H, Nederveen AJ, Zhen X, Gurney-Champion OJ. Deep learning DCE-MRI parameter estimation: application in pancreatic cancer. Med Image Anal 2022; 80:102512. [DOI: 10.1016/j.media.2022.102512] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 05/04/2022] [Accepted: 06/06/2022] [Indexed: 10/18/2022]
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Surrogate vascular input function measurements from the superior sagittal sinus are repeatable and provide tissue-validated kinetic parameters in brain DCE-MRI. Sci Rep 2022; 12:8737. [PMID: 35610281 PMCID: PMC9130284 DOI: 10.1038/s41598-022-12582-x] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2021] [Accepted: 04/27/2022] [Indexed: 01/08/2023] Open
Abstract
Accurate vascular input function (VIF) derivation is essential in brain dynamic contrast-enhanced (DCE) MRI. The optimum site for VIF estimation is, however, debated. This study sought to compare VIFs extracted from the internal carotid artery (ICA) and its branches with an arrival-corrected vascular output function (VOF) derived from the superior sagittal sinus (VOFSSS). DCE-MRI datasets from sixty-six patients with different brain tumours were retrospectively analysed and plasma gadolinium-based contrast agent (GBCA) concentration-time curves used to extract VOF/VIFs from the SSS, the ICA, and the middle cerebral artery. Semi-quantitative parameters across each first-pass VOF/VIF were compared and the relationship between these parameters and GBCA dose was evaluated. Through a test-retest study in 12 patients, the repeatability of each semiquantitative VOF/VIF parameter was evaluated; and through comparison with histopathological data the accuracy of kinetic parameter estimates derived using each VOF/VIF and the extended Tofts model was also assessed. VOFSSS provided a superior surrogate global input function compared to arteries, with greater contrast-to-noise (p < 0.001), higher peak (p < 0.001, repeated-measures ANOVA), and a greater sensitivity to interindividual plasma GBCA concentration. The repeatability of VOFSSS derived semi-quantitative parameters was good to excellent (ICC = 0.717-0.888) outperforming arterial based approaches. In contrast to arterial VIFs, kinetic parameters obtained using a SSS derived VOF permitted detection of intertumoural differences in both microvessel surface area and cell density within resected tissue specimens. These results support the usage of an arrival-corrected VOFSSS as a surrogate vascular input function for kinetic parameter mapping in brain DCE-MRI.
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van Herten RLM, Chiribiri A, Breeuwer M, Veta M, Scannell CM. Physics-informed neural networks for myocardial perfusion MRI quantification. Med Image Anal 2022; 78:102399. [PMID: 35299005 PMCID: PMC9051528 DOI: 10.1016/j.media.2022.102399] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 01/07/2022] [Accepted: 02/18/2022] [Indexed: 11/19/2022]
Abstract
Tracer-kinetic models allow for the quantification of kinetic parameters such as blood flow from dynamic contrast-enhanced magnetic resonance (MR) images. Fitting the observed data with multi-compartment exchange models is desirable, as they are physiologically plausible and resolve directly for blood flow and microvascular function. However, the reliability of model fitting is limited by the low signal-to-noise ratio, temporal resolution, and acquisition length. This may result in inaccurate parameter estimates. This study introduces physics-informed neural networks (PINNs) as a means to perform myocardial perfusion MR quantification, which provides a versatile scheme for the inference of kinetic parameters. These neural networks can be trained to fit the observed perfusion MR data while respecting the underlying physical conservation laws described by a multi-compartment exchange model. Here, we provide a framework for the implementation of PINNs in myocardial perfusion MR. The approach is validated both in silico and in vivo. In the in silico study, an overall decrease in mean-squared error with the ground-truth parameters was observed compared to a standard non-linear least squares fitting approach. The in vivo study demonstrates that the method produces parameter values comparable to those previously found in literature, as well as providing parameter maps which match the clinical diagnosis of patients.
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Affiliation(s)
- Rudolf L M van Herten
- Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom
| | - Marcel Breeuwer
- Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, the Netherlands; Philips Healthcare, Best, the Netherlands
| | - Mitko Veta
- Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - Cian M Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
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13
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Breit HC, Block TK, Winkel DJ, Gehweiler JE, Glessgen CG, Seifert H, Wetterauer C, Boll DT, Heye TJ. Revisiting DCE-MRI: Classification of Prostate Tissue Using Descriptive Signal Enhancement Features Derived From DCE-MRI Acquisition With High Spatiotemporal Resolution. Invest Radiol 2021; 56:553-562. [PMID: 33660631 PMCID: PMC8373655 DOI: 10.1097/rli.0000000000000772] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
METHODS A retrospective study (from January 2016 to July 2019) including 75 subjects (mean, 65 years; 46-80 years) with 2.5-second temporal resolution DCE-MRI and PIRADS 4 or 5 lesions was performed. Fifty-four subjects had biopsy-proven prostate cancer (Gleason 6, 15; Gleason 7, 20; Gleason 8, 13; Gleason 9, 6), whereas 21 subjects had negative MRI/ultrasound fusion-guided biopsies. Voxel-wise analysis of contrast signal enhancement was performed for all time points using custom-developed software, including automatic arterial input function detection. Seven descriptive parameter maps were calculated: normalized maximum signal intensity, time to start, time to maximum, time-to-maximum slope, and maximum slope with normalization on maximum signal and the arterial input function (SMN1, SMN2). The parameters were compared with ADC using multiparametric machine-learning models to determine classification accuracy. A Wilcoxon test was used for the hypothesis test and the Spearman coefficient for correlation. RESULTS There were significant differences (P < 0.05) for all 7 DCE-derived parameters between the normal peripheral zone versus PIRADS 4 or 5 lesions and the biopsy-positive versus biopsy-negative lesions. Multiparametric analysis showed better performance when combining ADC + DCE as input (accuracy/sensitivity/specificity, 97%/93%/100%) relative to ADC alone (accuracy/sensitivity/specificity, 94%/95%/95%) and to DCE alone (accuracy/sensitivity/specificity, 78%/79%/77%) in differentiating the normal peripheral zone from PIRADS lesions, biopsy-positive versus biopsy-negative lesions (accuracy/sensitivity/specificity, 68%/33%/81%), and Gleason 6 versus ≥7 prostate cancer (accuracy/sensitivity/specificity, 69%/60%/72%). CONCLUSIONS Descriptive perfusion characteristics derived from high-resolution DCE-MRI using model-free computations show significant differences between normal and cancerous tissue but do not reach the accuracy achieved with solely ADC-based classification. Combining ADC with DCE-based input features improved classification accuracy for PIRADS lesions, discrimination of biopsy-positive versus biopsy-negative lesions, and differentiation between Gleason 6 versus Gleason ≥7 lesions.
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Affiliation(s)
- Hanns C. Breit
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - David J. Winkel
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - Carl G. Glessgen
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Helge Seifert
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | | | - Daniel T. Boll
- Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Tobias J. Heye
- Department of Radiology, University Hospital Basel, Basel, Switzerland
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14
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Taxt T, Andersen E, Jiřík R. Single voxel vascular transport functions of arteries, capillaries and veins; and the associated arterial input function in dynamic susceptibility contrast magnetic resonance brain perfusion imaging. Magn Reson Imaging 2021; 84:101-114. [PMID: 34461158 DOI: 10.1016/j.mri.2021.08.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 07/13/2021] [Accepted: 08/15/2021] [Indexed: 10/20/2022]
Abstract
PURPOSE The composite vascular transport function of a brain voxel consists of one convolutional component for the arteries, one for the capillaries and one for the veins in the voxel of interest. Here, the goal is to find each of these three convolutional components and the associated arterial input function. PHARMACOKINETIC MODELLING The single voxel vascular transport functions for arteries, capillaries and veins were all modelled as causal exponential functions. Each observed multipass tissue contrast function was as a first approximation modelled as the resulting parametric composite vascular transport function convolved with a nonparametric and voxel specific multipass arterial input function. Subsequently, the residue function was used in the true perfusion equation to optimize the three parameters of the exponential functions. DECONVOLUTION METHODS For each voxel, the parameters of the three exponential functions were estimated by successive iterative blind deconvolutions using versions of the Lucy-Richardson algorithm. The final multipass arterial input function was then computed by nonblind deconvolution using the Lucy-Richardson algorithm and the estimated composite vascular transport function. RESULTS Simulations showed that the algorithm worked. The estimated mean transit time of arteries, capillaries and veins of the simulated data agreed with the known input values. For real data, the estimated capillary mean transit times agreed with known values for this parameter. The nonparametric multipass arterial input functions were used to derive the associated map of the arrival time. The arrival time map of a healthy volunteer agreed with known arterial anatomy and physiology. CONCLUSION Clinically important new voxelwise hemodynamic information for arteries, capillaries and veins separately can be estimated using multipass tissue contrast functions and the iterative blind Lucy-Richardson deconvolution algorithm.
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Affiliation(s)
- Torfinn Taxt
- Department of Biomedicine, University of Bergen, Bergen, N-5020, Norway; Department of Radiology, Haukeland University Hospital, Bergen, N-5021, Norway
| | - Erling Andersen
- Department of Clinical Engineering, Haukeland University Hospital, Bergen, N-5021, Norway
| | - Radovan Jiřík
- Institute of Scientific Instruments of the Czech Academy of Sciences, Brno, 61264, Czech Republic.
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15
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Waqar M, Lewis D, Agushi E, Gittins M, Jackson A, Coope D. Cerebral and tumoral blood flow in adult gliomas: a systematic review of results from magnetic resonance imaging. Br J Radiol 2021; 94:20201450. [PMID: 34106749 PMCID: PMC9327770 DOI: 10.1259/bjr.20201450] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Objective: Blood flow is the rate of blood movement and relevant to numerous processes, though understudied in gliomas. The aim of this review was to pool blood flow metrics obtained from MRI modalities in adult supratentorial gliomas. Methods: MEDLINE, EMBASE and the Cochrane database were queried 01/01/2000–31/12/2019. Studies measuring blood flow in adult Grade II–IV supratentorial gliomas using dynamic susceptibility contrast (DSC) MRI, dynamic contrast enhanced MRI (DCE-MRI) or arterial spin labelling (ASL) were included. Absolute and relative cerebral blood flow (CBF), peritumoral blood flow and tumoral blood flow (TBF) were reported. Results: 34 studies were included with 1415 patients and 1460 scans. The mean age was 52.4 ± 7.3 years. Most patients had glioblastoma (n = 880, 64.6%). The most common imaging modality was ASL (n = 765, 52.4%) followed by DSC (n = 538, 36.8%). Most studies were performed pre-operatively (n = 1268, 86.8%). With increasing glioma grade (II vs IV), TBF increased (70.8 vs 145.5 ml/100 g/min, p < 0.001) and CBF decreased (85.3 vs 49.6 ml/100 g/min, p < 0.001). In Grade IV gliomas, following treatment, CBF increased in ipsilateral (24.9 ± 1.2 vs 26.1 ± 0.0 ml/100 g/min, p < 0.001) and contralateral white matter (25.6 ± 0.2 vs 26.0± 0.0 ml/100 g/min, p < 0.001). Conclusion: Our findings demonstrate that increased mass effect from high-grade gliomas impairs blood flow within the surrounding brain that can improve with surgery. Advances in knowledge: This systematic review demonstrates how mass effect from brain tumours impairs blood flow in the surrounding brain parenchyma that can improve with treatment.
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Affiliation(s)
- Mueez Waqar
- Division of Informatics, Imaging and Data Sciences, Wolfson Molecular Imaging Centre, Manchester, UK.,Department of Neurosurgery, Salford Royal NHS Foundation Trust, Salford, UK
| | - Daniel Lewis
- Division of Informatics, Imaging and Data Sciences, Wolfson Molecular Imaging Centre, Manchester, UK.,Department of Neurosurgery, Salford Royal NHS Foundation Trust, Salford, UK
| | - Erjon Agushi
- Division of Informatics, Imaging and Data Sciences, Wolfson Molecular Imaging Centre, Manchester, UK.,Department of Neurosurgery, Salford Royal NHS Foundation Trust, Salford, UK
| | - Matthew Gittins
- Department of Biostatistics, Division of Population Health, Health Services Research& Primary Care, The University of Manchester, Manchester, UK
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, Wolfson Molecular Imaging Centre, Manchester, UK.,Department of Neuroradiology, Salford Royal NHS Foundation Trust, Salford, UK
| | - David Coope
- Department of Neurosurgery, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, Wolfson Molecular Imaging Centre, Manchester, UK
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16
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Wang YF, Tadimalla S, Hayden AJ, Holloway L, Haworth A. Artificial intelligence and imaging biomarkers for prostate radiation therapy during and after treatment. J Med Imaging Radiat Oncol 2021; 65:612-626. [PMID: 34060219 DOI: 10.1111/1754-9485.13242] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2021] [Revised: 04/18/2021] [Accepted: 05/02/2021] [Indexed: 12/15/2022]
Abstract
Magnetic resonance imaging (MRI) is increasingly used in the management of prostate cancer (PCa). Quantitative MRI (qMRI) parameters, derived from multi-parametric MRI, provide indirect measures of tumour characteristics such as cellularity, angiogenesis and hypoxia. Using Artificial Intelligence (AI), relevant information and patterns can be efficiently identified in these complex data to develop quantitative imaging biomarkers (QIBs) of tumour function and biology. Such QIBs have already demonstrated potential in the diagnosis and staging of PCa. In this review, we explore the role of these QIBs in monitoring treatment response during and after PCa radiotherapy (RT). Recurrence of PCa after RT is not uncommon, and early detection prior to development of metastases provides an opportunity for salvage treatments with curative intent. However, the current method of monitoring treatment response using prostate-specific antigen levels lacks specificity. QIBs, derived from qMRI and developed using AI techniques, can be used to monitor biological changes post-RT providing the potential for accurate and early diagnosis of recurrent disease.
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Affiliation(s)
- Yu-Feng Wang
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
| | - Sirisha Tadimalla
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
| | - Amy J Hayden
- Sydney West Radiation Oncology, Westmead Hospital, Wentworthville, New South Wales, Australia
- Faculty of Medicine, Western Sydney University, Sydney, New South Wales, Australia
- Faculty of Medicine, Health & Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Lois Holloway
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
- Ingham Institute for Applied Medical Research, Liverpool, New South Wales, Australia
- Liverpool and Macarthur Cancer Therapy Centre, Liverpool Hospital, Liverpool, New South Wales, Australia
| | - Annette Haworth
- Institute of Medical Physics, School of Physics, Faculty of Science, The University of Sydney, Sydney, New South Wales, Australia
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17
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Gwilliam MN, Collins DJ, Leach MO, Orton MR. Quantifying MRI T1 relaxation in flowing blood: implications for arterial input function measurement in DCE-MRI. Br J Radiol 2021; 94:20191004. [PMID: 33507818 PMCID: PMC8011233 DOI: 10.1259/bjr.20191004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
OBJECTIVES To investigate the feasibility of accurately quantifying the concentration of MRI contrast agent in flowing blood by measuring its T1 in a large vessel. Such measures are often used to obtain patient-specific arterial input functions for the accurate fitting of pharmacokinetic models to dynamic contrast enhanced MRI data. Flow is known to produce errors with this technique, but these have so far been poorly quantified and characterised in the context of pulsatile flow with a rapidly changing T1 as would be expected in vivo. METHODS A phantom was developed which used a mechanical pump to pass fluid at physiologically relevant rates. Measurements of T1 were made using high temporal resolution gradient recalled sequences suitable for DCE-MRI of both constant and pulsatile flow. These measures were used to validate a virtual phantom that was then used to simulate the expected errors in the measurement of an AIF in vivo. RESULTS The relationship between measured T1 values and flow velocity was found to be non-linear. The subsequent error in quantification of contrast agent concentration in a measured AIF was shown. CONCLUSIONS The T1 measurement of flowing blood using standard DCE- MRI sequences are subject to large measurement errors which are non-linear in relation to flow velocity. ADVANCES IN KNOWLEDGE This work qualitatively and quantitatively demonstrates the difficulties of accurately measuring the T1 of flowing blood using DCE-MRI over a wide range of physiologically realistic flow velocities and pulsatilities. Sources of error are identified and proposals made to reduce these.
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Affiliation(s)
- Matthew N Gwilliam
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - David J Collins
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Martin O Leach
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
| | - Matthew R Orton
- CRUK Cancer Imaging Centre, Institute of Cancer Research and Royal Marsden NHS Trust, London, UK
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18
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Abstract
OBJECTIVE. Diagnosing brain tumor recurrence, especially with changes that occur after treatment, is a challenge. MRI has an exceptional structural resolution, which is important from the perspective of treatment planning. However, its reliability in diagnosing recurrence is relatively lower, when compared to metabolic imaging. The latter is more sensitive to the early changes associated with recurrence and relatively immune to confounding by treatment related changes. CONCLUSION. There is no one-stop shop for the diagnosis of recurrence in brain tumors. The sensitivity of metabolic imaging is not a substitute for the resolution of the MRI, making a multi-modal approach the only way forward.
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19
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Sanders JW, Chen HSM, Johnson JM, Schomer DF, Jimenez JE, Ma J, Liu HL. Synthetic generation of DSC-MRI-derived relative CBV maps from DCE MRI of brain tumors. Magn Reson Med 2020; 85:469-479. [PMID: 32726488 DOI: 10.1002/mrm.28432] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 06/21/2020] [Accepted: 06/24/2020] [Indexed: 12/26/2022]
Abstract
PURPOSE Perfusion MRI with gadolinium-based contrast agents is useful for diagnosis and treatment response evaluation of brain tumors. Dynamic susceptibility contrast (DSC) MRI and dynamic contrast enhanced (DCE) MRI are two gadolinium-based contrast agent perfusion imaging techniques that provide complementary information about the tumor vasculature. However, each requires a separate administration of a gadolinium-based contrast agent. The purpose of this retrospective study was to determine the feasibility of synthesizing relative cerebral blood volume (rCBV) maps, as computed from DSC MRI, from DCE MRI of brain tumors. METHODS One hundred nine brain-tumor patients underwent both DCE and DSC MRI. Relative CBV maps were computed from the DSC MRI, and blood plasma volume fraction maps were computed from the DCE MRIs. Conditional generative adversarial networks were developed to synthesize rCBV maps from the DCE MRIs. Tumor-to-white matter ratios were calculated from real rCBV, synthetic rCBV, and plasma volume fraction maps and compared using correlation analysis. Real and synthetic rCBV in white and gray matter regions were also compared. RESULTS Pearson correlation analysis showed that both the tumor rCBV and tumor-to-white matter ratios in the synthetic and real rCBV maps were strongly correlated (ρ = 0.87, P < .05 and ρ = 0.86, P < .05, respectively). Tumor plasma volume fraction and real rCBV were not strongly correlated (ρ = 0.47). Bland-Altman analysis showed a mean difference between the synthetic and real rCBV tumor-to-white matter ratios of 0.20 with a 95% confidence interval of ±0.47. CONCLUSION Realistic rCBV maps can be synthesized from DCE MRI and contain quantitative information, enabling robust brain-tumor perfusion imaging of DSC and DCE parameters with a single gadolinium-based contrast agent administration.
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Affiliation(s)
- Jeremiah W Sanders
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Henry Szu-Meng Chen
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jason M Johnson
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Donald F Schomer
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jorge E Jimenez
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
| | - Jingfei Ma
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.,Medical Physics Graduate Program, The University of Texas MD Anderson Cancer Center UTHealth Graduate School of Biomedical Sciences, Houston, Texas, USA
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, Texas, USA
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20
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Bliesener Y, Acharya J, Nayak KS. Efficient DCE-MRI Parameter and Uncertainty Estimation Using a Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1712-1723. [PMID: 31794389 PMCID: PMC8887912 DOI: 10.1109/tmi.2019.2953901] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
Quantitative DCE-MRI provides voxel-wise estimates of tracer-kinetic parameters that are valuable in the assessment of health and disease. These maps suffer from many known sources of variability. This variability is expensive to compute using current methods, and is typically not reported. Here, we demonstrate a novel approach for simultaneous estimation of tracer-kinetic parameters and their uncertainty due to intrinsic characteristics of the tracer-kinetic model, with very low computation time. We train and use a neural network to estimate the approximate joint posterior distribution of tracer-kinetic parameters. Uncertainties are estimated for each voxel and are specific to the patient, exam, and lesion. We demonstrate the methods' ability to produce accurate tracer-kinetic maps. We compare predicted parameter ranges with uncertainties introduced by noise and by differences in post-processing in a digital reference object. The predicted parameter ranges correlate well with tracer-kinetic parameter ranges observed across different noise realizations and regression algorithms. We also demonstrate the value of this approach to differentiate significant from insignificant changes in brain tumor pharmacokinetics over time. This is achieved by enforcing consistency in resolving model singularities in the applied tracer-kinetic model.
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21
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Scannell CM, Chiribiri A, Villa ADM, Breeuwer M, Lee J. Hierarchical Bayesian myocardial perfusion quantification. Med Image Anal 2020; 60:101611. [PMID: 31760191 PMCID: PMC6880627 DOI: 10.1016/j.media.2019.101611] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2018] [Revised: 11/07/2019] [Accepted: 11/08/2019] [Indexed: 01/25/2023]
Abstract
Myocardial blood flow can be quantified from dynamic contrast-enhanced magnetic resonance (MR) images through the fitting of tracer-kinetic models to the observed imaging data. The use of multi-compartment exchange models is desirable as they are physiologically motivated and resolve directly for both blood flow and microvascular function. However, the parameter estimates obtained with such models can be unreliable. This is due to the complexity of the models relative to the observed data which is limited by the low signal-to-noise ratio, the temporal resolution, the length of the acquisitions and other complex imaging artefacts. In this work, a Bayesian inference scheme is proposed which allows the reliable estimation of the parameters of the two-compartment exchange model from myocardial perfusion MR data. The Bayesian scheme allows the incorporation of prior knowledge on the physiological ranges of the model parameters and facilitates the use of the additional information that neighbouring voxels are likely to have similar kinetic parameter values. Hierarchical priors are used to avoid making a priori assumptions on the health of the patients. We provide both a theoretical introduction to Bayesian inference for tracer-kinetic modelling and specific implementation details for this application. This approach is validated in both in silico and in vivo settings. In silico, there was a significant reduction in mean-squared error with the ground-truth parameters using Bayesian inference as compared to using the standard non-linear least squares fitting. When applied to patient data the Bayesian inference scheme returns parameter values that are in-line with those previously reported in the literature, as well as giving parameter maps that match the independant clinical diagnosis of those patients.
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Affiliation(s)
- Cian M Scannell
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom; The Alan Turing Institute London, United Kingdom.
| | - Amedeo Chiribiri
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
| | - Adriana D M Villa
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
| | - Marcel Breeuwer
- Philips Healthcare, Best, the Netherlands; Department of Biomedical Engineering, Medical Image Analysis group, Eindhoven University of Technology, Eindhoven, the Netherlands.
| | - Jack Lee
- School of Biomedical Engineering and Imaging Sciences, King's College London, United Kingdom.
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22
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Zhang J, Kim SG. Estimation of cellular-interstitial water exchange in dynamic contrast enhanced MRI using two flip angles. NMR IN BIOMEDICINE 2019; 32:e4135. [PMID: 31348580 PMCID: PMC6817382 DOI: 10.1002/nbm.4135] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/25/2018] [Revised: 06/11/2019] [Accepted: 06/17/2019] [Indexed: 05/10/2023]
Abstract
PURPOSE To investigate the feasibility of using multiple flip angles in dynamic contrast enhanced (DCE) MRI to reduce the uncertainty in estimation of intracellular water lifetime (τi ). METHODS Numerical simulation studies were conducted to assess the uncertainty in estimation of τi using dynamic contrast enhanced MRI with one or two flip angles. In vivo experiments with a murine brain tumor model were conducted at 7T using two flip angles. The in vivo data were used to compare τi estimation using the single-flip-angle (SFA) protocol with that using the double-flip-angle (DFA) protocol. Data analysis was conducted using the two-compartment exchange model combined with the three-site-two-exchange model for water exchange. RESULTS In the numerical simulation studies with a range of contrast kinetic parameters and signal-to-noise ratio = 20, the median bias of τi estimation decreased from 72 ms with SFA to 65 ms with DFA, and the corresponding median inter-quartile range reduced from 523 ms to 156 ms. In the in vivo studies, τi estimation with SFA was not successful in most voxels in the tumors, as the estimated τi values reached the upper limit of the parameter range (2 s). In contrast, the estimated τi values with DFA were mostly between 0.2 and 1.5 s and homogeneously distributed spatially across the tumor. The τi estimation with DFA was less sensitive to arterial input function scaling but more sensitive to pre-contrast T1 than the other contrast kinetic parameters. CONCLUSION This study results demonstrate the feasibility of using multiple flip angles to encode the post-contrast time-intensity curve with different weighting of water exchange effect to reduce the uncertainty in τi estimation.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Sungheon Gene Kim
- Center for Biomedical Imaging (CBI), Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University School of Medicine, New York, NY, United States
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23
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Antonelli M, Johnston EW, Dikaios N, Cheung KK, Sidhu HS, Appayya MB, Giganti F, Simmons LAM, Freeman A, Allen C, Ahmed HU, Atkinson D, Ourselin S, Punwani S. Machine learning classifiers can predict Gleason pattern 4 prostate cancer with greater accuracy than experienced radiologists. Eur Radiol 2019; 29:4754-4764. [PMID: 31187216 PMCID: PMC6682575 DOI: 10.1007/s00330-019-06244-2] [Citation(s) in RCA: 47] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 04/03/2019] [Accepted: 04/18/2019] [Indexed: 11/26/2022]
Abstract
OBJECTIVE The purpose of this study was: To test whether machine learning classifiers for transition zone (TZ) and peripheral zone (PZ) can correctly classify prostate tumors into those with/without a Gleason 4 component, and to compare the performance of the best performing classifiers against the opinion of three board-certified radiologists. METHODS A retrospective analysis of prospectively acquired data was performed at a single center between 2012 and 2015. Inclusion criteria were (i) 3-T mp-MRI compliant with international guidelines, (ii) Likert ≥ 3/5 lesion, (iii) transperineal template ± targeted index lesion biopsy confirming cancer ≥ Gleason 3 + 3. Index lesions from 164 men were analyzed (119 PZ, 45 TZ). Quantitative MRI and clinical features were used and zone-specific machine learning classifiers were constructed. Models were validated using a fivefold cross-validation and a temporally separated patient cohort. Classifier performance was compared against the opinion of three board-certified radiologists. RESULTS The best PZ classifier trained with prostate-specific antigen density, apparent diffusion coefficient (ADC), and maximum enhancement (ME) on DCE-MRI obtained a ROC area under the curve (AUC) of 0.83 following fivefold cross-validation. Diagnostic sensitivity at 50% threshold of specificity was higher for the best PZ model (0.93) when compared with the mean sensitivity of the three radiologists (0.72). The best TZ model used ADC and ME to obtain an AUC of 0.75 following fivefold cross-validation. This achieved higher diagnostic sensitivity at 50% threshold of specificity (0.88) than the mean sensitivity of the three radiologists (0.82). CONCLUSIONS Machine learning classifiers predict Gleason pattern 4 in prostate tumors better than radiologists. KEY POINTS • Predictive models developed from quantitative multiparametric magnetic resonance imaging regarding the characterization of prostate cancer grade should be zone-specific. • Classifiers trained differently for peripheral and transition zone can predict a Gleason 4 component with a higher performance than the subjective opinion of experienced radiologists. • Classifiers would be particularly useful in the context of active surveillance, whereby decisions regarding whether to biopsy are necessitated.
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Affiliation(s)
- Michela Antonelli
- Centre for Medical Image Computing, University College London, London, UK
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Edward W Johnston
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Nikolaos Dikaios
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - King K Cheung
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Harbir S Sidhu
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Mrishta B Appayya
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Francesco Giganti
- Department of Radiology, University College London Hospital, London, UK
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Lucy A M Simmons
- Division of Surgery and Interventional Science, University College London, London, UK
| | - Alex Freeman
- Department of Pathology, University College London Hospital, London, UK
| | - Clare Allen
- Department of Radiology, University College London Hospital, London, UK
| | - Hashim U Ahmed
- Division of Surgery and Interventional Science, University College London, London, UK
| | - David Atkinson
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Science, King's College London, London, UK
| | - Shonit Punwani
- Centre for Medical Imaging, University College London, 2nd Floor Charles Bell House, 43-45 Foley Street, London, W1W 7TS, UK.
- Department of Radiology, University College London Hospital, London, UK.
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24
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Costabile JD, Thompson JA, Alaswad E, Ormond DR. Biopsy Confirmed Glioma Recurrence Predicted by Multi-Modal Neuroimaging Metrics. J Clin Med 2019; 8:E1287. [PMID: 31450732 PMCID: PMC6780506 DOI: 10.3390/jcm8091287] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2019] [Revised: 08/13/2019] [Accepted: 08/20/2019] [Indexed: 12/21/2022] Open
Abstract
Histopathological verification is currently required to differentiate tumor recurrence from treatment effects related to adjuvant therapy in patients with glioma. To bypass the complications associated with collecting neural tissue samples, non-invasive classification methods are needed to alleviate the burden on patients while providing vital information to clinicians. However, uncertainty remains as to which tissue features on magnetic resonance imaging (MRI) are useful. The primary objective of this study was to quantitatively assess the reliability of combining MRI and diffusion tensor imaging metrics to discriminate between tumor recurrence and treatment effects in histopathologically identified biopsy samples. Additionally, this study investigates the noise adjuvant radiation therapy introduces when discriminating between tissue types. In a sample of 41 biopsy specimens, from a total of 10 patients, we derived region-of-interest samples from MRI data in the ipsilateral hemisphere that encompassed biopsies obtained during resective surgery. This study compares normalized intensity values across histopathology classifications and contralesional volumes reflected across the midline. Radiation makes noninvasive differentiation of abnormal-nontumor tissue to tumor recurrence much more difficult. This is because radiation exhibits opposing behavior on key MRI modalities: specifically, on post-contrast T1, FLAIR, and GFA. While radiation makes noninvasive differentiation of tumor recurrence more difficult, using a novel analysis of combined MRI metrics combined with clinical annotation and histopathological correlation, we observed that it is possible to successfully differentiate tumor tissue from other tissue types. Additional work will be required to expand upon these findings.
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Affiliation(s)
- Jamie D Costabile
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - John A Thompson
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
- Department of Neurology, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - Elsa Alaswad
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA
| | - D Ryan Ormond
- Department of Neurosurgery, University of Colorado School of Medicine, Aurora, CO 80045, USA.
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25
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Inglese M, Ordidge KL, Honeyfield L, Barwick TD, Aboagye EO, Waldman AD, Grech-Sollars M. Reliability of dynamic contrast-enhanced magnetic resonance imaging data in primary brain tumours: a comparison of Tofts and shutter speed models. Neuroradiology 2019; 61:1375-1386. [PMID: 31392385 PMCID: PMC6848046 DOI: 10.1007/s00234-019-02265-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 07/12/2019] [Indexed: 12/12/2022]
Abstract
Purpose The purpose of this study is to investigate the robustness of pharmacokinetic modelling of DCE-MRI brain tumour data and to ascertain reliable perfusion parameters through a model selection process and a stability test. Methods DCE-MRI data of 14 patients with primary brain tumours were analysed using the Tofts model (TM), the extended Tofts model (ETM), the shutter speed model (SSM) and the extended shutter speed model (ESSM). A no-effect model (NEM) was implemented to assess overfitting of data by the other models. For each lesion, the Akaike Information Criteria (AIC) was used to build a 3D model selection map. The variability of each pharmacokinetic parameter extracted from this map was assessed with a noise propagation procedure, resulting in voxel-wise distributions of the coefficient of variation (CV). Results The model selection map over all patients showed NEM had the best fit in 35.5% of voxels, followed by ETM (32%), TM (28.2%), SSM (4.3%) and ESSM (< 0.1%). In analysing the reliability of Ktrans, when considering regions with a CV < 20%, ≈ 25% of voxels were found to be stable across all patients. The remaining 75% of voxels were considered unreliable. Conclusions The majority of studies quantifying DCE-MRI data in brain tumours only consider a single model and whole tumour statistics for the output parameters. Appropriate model selection, considering tissue biology and its effects on blood brain barrier permeability and exchange conditions, together with an analysis on the reliability and stability of the calculated parameters, is critical in processing robust brain tumour DCE-MRI data.
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Affiliation(s)
- Marianna Inglese
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.
| | | | - Lesley Honeyfield
- Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Tara D Barwick
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
| | - Eric O Aboagye
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK
| | - Adam D Waldman
- Department of Medicine, Imperial College London, London, UK.,Centre for Clinical Brain Sciences, The University of Edinburgh, Edinburgh, UK
| | - Matthew Grech-Sollars
- Department of Surgery and Cancer, GN1 Commonwealth building, Hammersmith Hospital, Imperial College London, Du Cane Road, London, W12 0NN, UK.,Department of Imaging, Imperial College Healthcare NHS Trust, London, UK
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26
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Revisiting quantitative multi-parametric MRI of benign prostatic hyperplasia and its differentiation from transition zone cancer. Abdom Radiol (NY) 2019; 44:2233-2243. [PMID: 30955071 DOI: 10.1007/s00261-019-01936-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
PURPOSE This study investigates the multiparametric MRI (mpMRI) appearance of different types of benign prostatic hyperplasia (BPH) and whether quantitative mpMRI is effective in differentiating between prostate cancer (PCa) and BPH. MATERIALS AND METHODS Patients (n = 60) with confirmed PCa underwent preoperative 3T MRI. T2-weighted, multi-echo T2-weighted, diffusion weighted and dynamic contrast enhanced images (DCE) were obtained prior to undergoing prostatectomy. PCa and BPH (cystic, glandular or stromal) were identified in the transition zone and matched with MRI. Quantitative mpMRI metrics: T2, ADC and DCE-MRI parameters using an empirical mathematical model were measured. RESULTS ADC values were significantly lower (p < 0.001) in PCa compared to all BPH types and can differentiate between PCa and BPH with high accuracy (AUC = 0.87, p < 0.001). T2 values were significantly lower (p < 0.001) in PCa compared to cystic BPH only, while glandular (p = 0.27) and stromal BPH (p = 0.99) showed no significant difference from PCa. BPH mimics PCa in the transition zone on DCE-MRI evidenced by no significant difference between them. mpMRI values of glandular (ADC = 1.31 ± 0.22 µm2/ms, T2 = 115.7 ± 37.3 ms) and cystic BPH (ADC = 1.92 ± 0.43 µm2/ms, T2 = 242.8 ± 117.9 ms) are significantly different. There was no significant difference in ADC (p = 0.72) and T2 (p = 0.46) between glandular and stromal BPH. CONCLUSIONS Multiparametric MRI and specifically quantitative ADC values can be used for differentiating PCa and BPH, improving PCa diagnosis in the transition zone. However, DCE-MRI metrics are not effective in distinguishing PCa and BPH. Glandular BPH are not hyperintense on ADC and T2 as previously thought and have similar quantitative mpMRI measurements to stromal BPH. Glandular and cystic BPH appear differently on mpMRI and are histologically different.
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27
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Noninvasive monitoring of blood flow using a single magnetic microsphere. Sci Rep 2019; 9:5014. [PMID: 30899047 PMCID: PMC6428830 DOI: 10.1038/s41598-019-41416-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 03/07/2019] [Indexed: 11/30/2022] Open
Abstract
Noninvasive medical imaging of blood flow relies on mapping the transit of a contrast medium bolus injected intravenously. This has the draw-back that the front of the bolus widens until the tissue of interest is reached and quantitative flow parameters are not easy to obtain. Here, we introduce high resolution (millimeter/millisecond) 3D magnetic tracking of a single microsphere locally probing the flow while passing through a vessel. With this, we successfully localize and evaluate diameter constrictions in an arteria phantom after a single passage of a microsphere. We further demonstrate the potential for clinical application by tracking a microsphere smaller than a red blood cell.
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28
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Klawer EME, van Houdt PJ, Simonis FFJ, van den Berg CAT, Pos FJ, Heijmink SWTPJ, Isebaert S, Haustermans K, van der Heide UA. Improved repeatability of dynamic contrast-enhanced MRI using the complex MRI signal to derive arterial input functions: a test-retest study in prostate cancer patients. Magn Reson Med 2019; 81:3358-3369. [PMID: 30656738 PMCID: PMC6590420 DOI: 10.1002/mrm.27646] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2018] [Revised: 11/07/2018] [Accepted: 12/04/2018] [Indexed: 12/31/2022]
Abstract
Purpose The arterial input function (AIF) is a major source of uncertainty in tracer kinetic (TK) analysis of dynamic contrast‐enhanced (DCE)‐MRI data. The aim of this study was to investigate the repeatability of AIFs extracted from the complex signal and of the resulting TK parameters in prostate cancer patients. Methods Twenty‐two patients with biopsy‐proven prostate cancer underwent a 3T MRI exam twice. DCE‐MRI data were acquired with a 3D spoiled gradient echo sequence. AIFs were extracted from the magnitude of the signal (AIFMAGN), phase (AIFPHASE), and complex signal (AIFCOMPLEX). The Tofts model was applied to extract Ktrans, kep and ve. Repeatability of AIF curve characteristics and TK parameters was assessed with the within‐subject coefficient of variation (wCV). Results The wCV for peak height and full width at half maximum for AIFCOMPLEX (7% and 8%) indicated an improved repeatability compared to AIFMAGN (12% and 12%) and AIFPHASE (12% and 7%). This translated in lower wCV values for Ktrans (11%) with AIFCOMPLEX in comparison to AIFMAGN (24%) and AIFPHASE (15%). For kep, the wCV was 16% with AIFMAGN, 13% with AIFPHASE, and 13% with AIFCOMPLEX. Conclusion Repeatability of AIFPHASE and AIFCOMPLEX is higher than for AIFMAGN, resulting in a better repeatability of TK parameters. Thus, use of either AIFPHASE or AIFCOMPLEX improves the robustness of quantitative analysis of DCE‐MRI in prostate cancer.
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Affiliation(s)
- Edzo M E Klawer
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Petra J van Houdt
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Frank F J Simonis
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Cornelis A T van den Berg
- Department of Radiation Oncology, Imaging Division, University Medical Center, Utrecht, The Netherlands
| | - Floris J Pos
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
| | | | - Sofie Isebaert
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Karin Haustermans
- Department of Radiation Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium
| | - Uulke A van der Heide
- Department of Radiation Oncology, the Netherlands Cancer Institute, Amsterdam, The Netherlands
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Wahyulaksana G, Saporito S, den Boer JA, Herold IHF, Mischi M. In vitro pharmacokinetic phantom for two-compartment modeling in DCE-MRI. Phys Med Biol 2018; 63:205012. [PMID: 30238927 DOI: 10.1088/1361-6560/aae33b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an established minimally-invasive method for assessment of extravascular leakage, hemodynamics, and tissue viability. However, differences in acquisition protocols, variety of pharmacokinetic models, and uncertainty on physical sources of MR signal hamper the reliability and widespread use of DCE-MRI in clinical practice. Measurements performed in a controlled in vitro setup could be used as a basis for standardization of the acquisition procedure, as well as objective evaluation and comparison of pharmacokinetic models. In this paper, we present a novel flow phantom that mimics a two-compartmental (blood plasma and extravascular extracellular space/EES) vascular bed, enabling systemic validation of acquisition protocols. The phantom consisted of a hemodialysis filter with two compartments, separated by hollow fiber membranes. The aim of this phantom was to vary the extravasation rate by adjusting the flow in the two compartments. Contrast agent transport kinetics within the phantom was interpreted using two-compartmental pharmacokinetic models. Boluses of gadolinium-based contrast-agent were injected in a tube network connected to the hollow fiber phantom; time-intensity curves (TICs) were obtained from image series, acquired using a T1-weighted DCE-MRI sequence. Under the assumption of a linear dilution system, the TICs obtained from the input and output of the system were then analyzed by a system identification approach to estimate the trans-membrane extravasation rates in different flow conditions. To this end, model-based deconvolution was employed to determine (identify) the impulse response of the investigated dilution system. The flow rates in the EES compartment significantly and consistently influenced the estimated extravasation rates, in line with the expected trends based on simulation results. The proposed phantom can therefore be used to model a two-compartmental vascular bed and can be employed to test and optimize DCE-MRI acquisition sequences in order to determine a standardized acquisition procedure leading to consistent quantification results.
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Affiliation(s)
- Geraldi Wahyulaksana
- Department of Electrical Engineering, Eindhoven University of Technology, 5612 AZ Eindhoven, Netherlands
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MacDonald ME, Berman AJ, Mazerolle EL, Williams RJ, Pike GB. Modeling hyperoxia-induced BOLD signal dynamics to estimate cerebral blood flow, volume and mean transit time. Neuroimage 2018; 178:461-474. [DOI: 10.1016/j.neuroimage.2018.05.066] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 05/25/2018] [Accepted: 05/27/2018] [Indexed: 11/30/2022] Open
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Klawer EM, van Houdt PJ, Pos FJ, Heijmink SW, van Osch MJ, van der Heide UA. Impact of contrast agent injection duration on dynamic contrast-enhanced MRI quantification in prostate cancer. NMR IN BIOMEDICINE 2018; 31:e3946. [PMID: 29974981 PMCID: PMC6175355 DOI: 10.1002/nbm.3946] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/30/2017] [Revised: 04/12/2018] [Accepted: 04/24/2018] [Indexed: 06/08/2023]
Abstract
The volume transfer constant Ktrans , which describes the leakage of contrast agent (CA) from vasculature into tissue, is the most commonly reported quantitative parameter for dynamic contrast-enhanced (DCE-) MRI. However, the variation in reported Ktrans values between studies from different institutes is large. One of the primary sources of uncertainty is quantification of the arterial input function (AIF). The aim of this study is to determine the influence of the CA injection duration on the AIF and tracer kinetic analysis (TKA) parameters (i.e. Ktrans , kep and ve ). Thirty-one patients with prostate cancer received two DCE-MRI examinations with an injection duration of 5 s in the first examination and a prolonged injection duration in the second examination, varying between 7.5 s and 30 s. The DCE examination was carried out on a 3.0 T MRI scanner using a transversal T1 -weighted 3D spoiled gradient echo sequence (300 s duration, dynamic scan time of 2.5 s). Data of 29 of the 31 were further analysed. AIFs were determined from the phase signal in the left and right femoral arteries. Ktrans , kep and ve were estimated with the standard Tofts model for regions of healthy peripheral zone and tumour tissue. We observed a significantly smaller peak height and increased width in the AIF for injection durations of 15 s and longer. However, we did not find significant differences in Ktrans , kep or ve for the studied injection durations. The study demonstrates that the TKA parameters Ktrans , kep and ve , measured in the prostate, do not show a significant change as a function of injection duration.
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Affiliation(s)
- Edzo M.E. Klawer
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Petra J. van Houdt
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | - Floris J. Pos
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
| | | | | | - Uulke A. van der Heide
- Department of Radiation OncologyThe Netherlands Cancer InstituteAmsterdamThe Netherlands
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Bolcaen J, Descamps B, Acou M, Deblaere K, den Broecke CV, Boterberg T, Vanhove C, Goethals I. In Vivo DCE-MRI for the Discrimination Between Glioblastoma and Radiation Necrosis in Rats. Mol Imaging Biol 2018; 19:857-866. [PMID: 28303489 DOI: 10.1007/s11307-017-1071-0] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
PURPOSE In this study, the potential of semiquantitative and quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) was investigated to differentiate glioblastoma (GB) from radiation necrosis (RN) in rats. PROCEDURES F98 GB growth was seen on MRI 8-23 days post-inoculation (n = 15). RN lesions developed 6-8 months post-irradiation (n = 10). DCE-MRI was acquired using a fast low-angle shot (FLASH) sequence. Regions of interest (ROIs) encompassed peripheral contrast enhancement in GB (n = 15) and RN (n = 10) as well as central necrosis within these lesions (GB (n = 4), RN (n = 3)). Dynamic contrast-enhanced time series, obtained from the DCE-MRI data, were fitted to determine four function variables (amplitude A, offset from zero C, wash-in rate k, and wash-out rate D) as well as maximal intensity (ImaxF) and time to peak (TTPF). Secondly, maps of semiquantitative and quantitative parameters (extended Tofts model) were created using Olea Sphere (O). Semiquantitative DCE-MRI parameters included wash-inO, wash-outO, area under the curve (AUCO), maximal intensity (ImaxO), and time to peak (TTPO). Quantitative parameters included the rate constant plasma to extravascular-extracellular space (EES) (K trans), the rate constant EES to plasma (K ep), plasma volume (V p), and EES volume (V e). All (semi)quantitative parameters were compared between GB and RN using the Mann-Whitney U test. ROC analysis was performed. RESULTS Wash-in rate (k) and wash-out rate (D) were significantly higher in GB compared to RN using curve fitting (p = 0.016 and p = 0.014). TTPF and TTPO were significantly lower in GB compared to RN (p = 0.001 and p = 0.005, respectively). The highest sensitivity (87 %) and specificity (80 %) were obtained for TTPF by applying a threshold of 581 s. K trans, K ep, and V e were not significantly different between GB and RN. A trend towards higher V p values was found in GB compared to RN, indicating angiogenesis in GB (p = 0.075). CONCLUSIONS Based on our results, in a rat model of GB and RN, wash-in rate, wash-out rate, and the time to peak extracted from DCE-MRI time series data may be useful to discriminate GB from RN.
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Affiliation(s)
- Julie Bolcaen
- Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium.
| | - Benedicte Descamps
- iMinds-IBiTech-MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Marjan Acou
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | - Karel Deblaere
- Department of Radiology and Medical Imaging, Ghent University Hospital, Ghent, Belgium
| | | | - Tom Boterberg
- Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium
| | - Christian Vanhove
- iMinds-IBiTech-MEDISIP, Department of Electronics and Information Systems, Ghent University, Ghent, Belgium
| | - Ingeborg Goethals
- Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium
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Chatterjee A, He D, Fan X, Wang S, Szasz T, Yousuf A, Pineda F, Antic T, Mathew M, Karczmar GS, Oto A. Performance of Ultrafast DCE-MRI for Diagnosis of Prostate Cancer. Acad Radiol 2018; 25:349-358. [PMID: 29167070 PMCID: PMC6535050 DOI: 10.1016/j.acra.2017.10.004] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2017] [Revised: 09/22/2017] [Accepted: 10/16/2017] [Indexed: 01/19/2023]
Abstract
RATIONALE AND OBJECTIVES This study aimed to test high temporal resolution dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) for different zones of the prostate and evaluate its performance in the diagnosis of prostate cancer (PCa). Determine whether the addition of ultrafast DCE-MRI improves the performance of multiparametric MRI. MATERIALS AND METHODS Patients (n = 20) with pathologically confirmed PCa underwent preoperative 3T MRI with T2-weighted, diffusion-weighted, and high temporal resolution (~2.2 seconds) DCE-MRI using gadoterate meglumine (Guerbet, Bloomington, IN) without an endorectal coil. DCE-MRI data were analyzed by fitting signal intensity with an empirical mathematical model to obtain parameters: percent signal enhancement, enhancement rate (α), washout rate (β), initial enhancement slope, and enhancement start time along with apparent diffusion coefficient (ADC) and T2 values. Regions of interests were placed on sites of prostatectomy verified malignancy (n = 46) and normal tissue (n = 71) from different zones. RESULTS Cancer (α = 6.45 ± 4.71 s-1, β = 0.067 ± 0.042 s-1, slope = 3.78 ± 1.90 s-1) showed significantly (P <.05) faster signal enhancement and washout rates than normal tissue (α = 3.0 ± 2.1 s-1, β = 0.034 ± 0.050 s-1, slope = 1.9 ± 1.4 s-1), but showed similar percentage signal enhancement and enhancement start time. Receiver operating characteristic analysis showed area under the curve for DCE parameters was comparable to ADC and T2 in the peripheral (DCE 0.67-0.82, ADC 0.80, T2 0.89) and transition zones (DCE 0.61-0.72, ADC 0.69, T2 0.75), but higher in the central zone (DCE 0.79-0.88, ADC 0.45, T2 0.45) and anterior fibromuscular stroma (DCE 0.86-0.89, ADC 0.35, T2 0.12). Importantly, combining DCE with ADC and T2 increased area under the curve by ~30%, further improving the diagnostic accuracy of PCa detection. CONCLUSION Quantitative parameters from empirical mathematical model fits to ultrafast DCE-MRI improve diagnosis of PCa. DCE-MRI with higher temporal resolution may capture clinically useful information for PCa diagnosis that would be missed by low temporal resolution DCE-MRI. This new information could improve the performance of multiparametric MRI in PCa detection.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | | | - Aytekin Oto
- Department of Radiology, The University of Chicago, 5841 S Maryland Ave, MC 2026, Chicago, IL 60637.
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Debus C, Floca R, Nörenberg D, Abdollahi A, Ingrisch M. Impact of fitting algorithms on errors of parameter estimates in dynamic contrast-enhanced MRI. ACTA ACUST UNITED AC 2017; 62:9322-9340. [DOI: 10.1088/1361-6560/aa8989] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
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Chao SL, Metens T, Lemort M. TumourMetrics: a comprehensive clinical solution for the standardization of DCE-MRI analysis in research and routine use. Quant Imaging Med Surg 2017; 7:496-510. [PMID: 29184762 DOI: 10.21037/qims.2017.09.02] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Background A reliable analysis methodology is needed to provide valuable imaging biomarkers for clinical trials, with particular regards to dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) application using pharmacokinetic (PK) model analysis. In order to address this scientific challenge, we provided a comprehensive analysis solution that could overcome the impediments to clinical research and routine use. Methods TumourMetrics has been designed to meet the Quantitative Imaging Biomarkers Alliance (QIBA) v.1.0 profile. The quality performance was assessed using the QIBA test data and our customizable numeric phantom. The analysis workflow is made customizable to facilitate standardization of optimized protocol across centers. Results Our quantification workflow estimated the PK model parameters accurately. The method is robust, almost fully automatic and allows a direct integration of the results into the diagnostic workflow. Conclusions The analysis is easy-to-use and accessible for routine implementation of DCE-MRI into clinical practice.
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Affiliation(s)
- Shih-Li Chao
- Department of Radiology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
| | - Thierry Metens
- Department of Radiology, Hôpital Erasme CUB, Ecole Polytechnique & Faculté de Médecine Université Libre de Bruxelles, Brussels, Belgium
| | - Marc Lemort
- Department of Radiology, Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium
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Zhang J, Winters K, Reynaud O, Kim SG. Simultaneous measurement of T 1 /B 1 and pharmacokinetic model parameters using active contrast encoding (ACE)-MRI. NMR IN BIOMEDICINE 2017; 30:10.1002/nbm.3737. [PMID: 28544159 PMCID: PMC5557664 DOI: 10.1002/nbm.3737] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/23/2016] [Revised: 03/27/2017] [Accepted: 03/28/2017] [Indexed: 05/06/2023]
Abstract
The aim of this study was to assess the feasibility of combining dynamic contrast enhanced-magnetic resonance imaging (DCE-MRI) with the measurement of the radiofrequency (RF) transmit field B1 and pre-contrast longitudinal relaxation time T10 . A novel approach has been proposed to simultaneously estimate B1 and T10 from a modified DCE-MRI scan that actively encodes the washout phase of the curve with different amounts of T1 and B1 weighting using multiple flip angles and repetition times, hence referred to as active contrast encoding (ACE)-MRI. ACE-MRI aims to simultaneously measure B1 and T10 , together with contrast kinetic parameters, such as the transfer constant Ktrans , interstitial space volume fraction ve and vascular space volume fraction vp . The proposed method was tested using numerical simulations and in vivo studies with mouse models of breast cancer implanted in the flank and mammary fat pad, and glioma in the brain. In the numerical simulation study with a signal-to-noise ratio of 10, both B1 and T10 were estimated accurately with errors of 5.1 ± 3.5% and 12.3 ± 8.8% and coefficients of variation (CV) of 14.9 ± 8.6% and 15.0 ± 5.0%, respectively. Using the same ACE-MRI data, the kinetic parameters Ktrans , ve and vp were also estimated with errors of 14.2 ± 8.3% (CV = 13.5 ± 4.6%), 14.7 ± 9.9% (CV = 13.3 ± 4.5%) and 14.0 ± 9.3% (CV = 14.0 ± 4.5%), respectively. For the in vivo tumor data from 11 mice, voxel-wise comparisons between ACE-MRI and DCE-MRI methods showed that the mean differences for the five parameters were as follows: ΔKtrans = 0.006 (/min), Δve = 0.016, Δvp = 0.000, ΔB1 = -0.014 and ΔT1 = -0.085 (s), which suggests a good agreement between the two methods. When compared with separately measured B1 and T10 , and DCE-MRI estimated kinetic parameters as a reference, the mean relative errors of ACE-MRI estimation were B1 = -0.3%, T10 = -8.5%, Ktrans = 11.4%, ve = 14.5% and vp = 4.5%. This proof-of-concept study demonstrates that the proposed ACE-MRI method can be used to estimate B1 and T10 , together with contrast kinetic model parameters.
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Affiliation(s)
- Jin Zhang
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Kerryanne Winters
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Olivier Reynaud
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
| | - Sungheon Gene Kim
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, USA
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Koh TS, Hennedige TP, Thng CH, Hartono S, Ng QS. Understanding K trans: a simulation study based on a multiple-pathway model. Phys Med Biol 2017; 62:N297-N319. [PMID: 28467315 DOI: 10.1088/1361-6560/aa70c9] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
The transfer constant K trans is commonly employed in dynamic contrast-enhanced MRI studies, but the utility and interpretation of K trans as a potential biomarker of tumor vasculature remains unclear. In this study, computer simulations based on a comprehensive tracer kinetic model with multiple pathways was used to provide clarification on the interpretation and application of K trans. Tissue concentration-time curves pertaining to a wide range of transport conditions were simulated using the multiple-pathway (MP) model and fitted using the generalized kinetic (GK) and extended GK models. Relationships between K trans and plasma flow F p, vessel permeability PS and extraction rate EF p under various transport conditions were assessed by correlation and regression analysis. Results show that the MP model provides an alternative two-tier interpretation of K trans based on the vascular transit time. K trans is primarily associated with F p and EF p respectively, in the slow and rapid vascular transit states, independent of the magnitude of PS. The relative magnitudes of PS and F p only serve as secondary constraints for which K trans can be further associated with EF p and PS in the slow and rapid transit states, respectively.
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Affiliation(s)
- T S Koh
- Department of Oncologic Imaging, National Cancer Center, 169610, Singapore. Duke-NUS Graduate Medical School, 169857, Singapore
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Wilkens R, Peters DA, Nielsen AH, Hovgaard VP, Glerup H, Krogh K. Dynamic Contrast-Enhanced Magnetic Resonance Enterography and Dynamic Contrast-Enhanced Ultrasonography in Crohn's Disease: An Observational Comparison Study. Ultrasound Int Open 2017; 3:E13-E24. [PMID: 28286879 PMCID: PMC5340279 DOI: 10.1055/s-0042-123841] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/31/2016] [Revised: 09/19/2016] [Accepted: 12/04/2016] [Indexed: 12/12/2022] Open
Abstract
Purpose e Cross-sectional imaging methods are important for objective evaluationof small intestinal inflammationinCrohn'sdisease(CD).The primary aim was to compare relative parameters of intestinal perfusion between contrast-enhanced ultrasonography (CEUS) and dynamic contrast-enhanced magnetic resonance enterography (DCE-MRE) in CD. Furthermore, we aimed at testing the repeatability of regions of interest (ROIs) for CEUS. Methods This prospective study included 25 patients: 12 females (age: 37, range: 19-66) with moderate to severe CD and a bowel wall thickness>3mm evaluated with DCE-MRE and CEUS. CEUS bolus injection was performed twice for repeatability and analyzed in VueBox®. Correlations between modalities were described with Spearman's rho, limits of agreement(LoA) and intraclass correlation coefficient(ICC). ROIrepeatability for CEUS was assessed. Results s The correlation between modalities was good and very good for bowel wall thickness (ICC=0.71, P<0.001) and length of the inflamed segment (ICC=0.89, P<0.001). Moderate-weak correlations were found for the time-intensity curve parameters: peak intensity (r=0.59, P=0.006), maximum wash-in-rate (r=0.62, P=0.004), and wash-in perfusion index (r=0.47, P=0.036). Best CEUS repeatability for peak enhancement was a mean difference of 0.73 dB (95% CI: 0.17 to 1.28, P=0.01) and 95% LoA from -3.8 to 5.3 dB. Good quality of curve fit improved LoA to -2.3 to 2.8 dB. Conclusion The relative perfusion of small intestinal CD assessed with DCE-MRE and CEUS shows only a moderate correlation. Applying strict criteria for ROIs is important and allows for good CEUS repeatability.
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Affiliation(s)
- Rune Wilkens
- Divisions of Medicine and Radiology, Diagnostic Centre, Silkeborg Regional
Hospital, University Research Clinic for Innovative Diagnostic Pathways, Silkeborg,
Denmark
- Department of Hepatology and Gastroenterology, Aarhus University Hospital,
Aarhus C, Denmark
| | - David A. Peters
- Department of Clinical Engineering, Aarhus University Hospital, Aarhus N,
Denmark
| | - Agnete H. Nielsen
- Divisions of Medicine and Radiology, Diagnostic Centre, Silkeborg Regional
Hospital, University Research Clinic for Innovative Diagnostic Pathways, Silkeborg,
Denmark
| | - Valeriya P. Hovgaard
- Divisions of Medicine and Radiology, Diagnostic Centre, Silkeborg Regional
Hospital, University Research Clinic for Innovative Diagnostic Pathways, Silkeborg,
Denmark
| | - Henning Glerup
- Divisions of Medicine and Radiology, Diagnostic Centre, Silkeborg Regional
Hospital, University Research Clinic for Innovative Diagnostic Pathways, Silkeborg,
Denmark
| | - Klaus Krogh
- Department of Hepatology and Gastroenterology, Aarhus University Hospital,
Aarhus C, Denmark
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Hindel S, Söhner A, Maaß M, Sauerwein W, Möllmann D, Baba HA, Kramer M, Lüdemann L. Validation of Blood Volume Fraction Quantification with 3D Gradient Echo Dynamic Contrast-Enhanced Magnetic Resonance Imaging in Porcine Skeletal Muscle. PLoS One 2017; 12:e0170841. [PMID: 28141810 PMCID: PMC5283669 DOI: 10.1371/journal.pone.0170841] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2016] [Accepted: 01/11/2017] [Indexed: 12/16/2022] Open
Abstract
The purpose of this study was to assess the accuracy of fractional blood volume (vb) estimates in low-perfused and low-vascularized tissue using dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The results of different MRI methods were compared with histology to evaluate the accuracy of these methods under clinical conditions. vb was estimated by DCE-MRI using a 3D gradient echo sequence with k-space undersampling in five muscle groups in the hind leg of 9 female pigs. Two gadolinium-based contrast agents (CA) were used: a rapidly extravasating, extracellular, gadolinium-based, low-molecular-weight contrast agent (LMCA, gadoterate meglumine) and an extracellular, gadolinium-based, albumin-binding, slowly extravasating blood pool contrast agent (BPCA, gadofosveset trisodium). LMCA data were evaluated using the extended Tofts model (ETM) and the two-compartment exchange model (2CXM). The images acquired with administration of the BPCA were used to evaluate the accuracy of vb estimation with a bolus deconvolution technique (BD) and a method we call equilibrium MRI (EqMRI). The latter calculates the ratio of the magnitude of the relaxation rate change in the tissue curve at an approximate equilibrium state to the height of the same area of the arterial input function (AIF). Immunohistochemical staining with isolectin was used to label endothelium. A light microscope was used to estimate the fractional vascular area by relating the vascular region to the total tissue region (immunohistochemical vessel staining, IHVS). In addition, the percentage fraction of vascular volume was determined by multiplying the microvascular density (MVD) with the average estimated capillary lumen, π(d2)2 , where d = 8μm is the assumed capillary diameter (microvascular density estimation, MVDE). Except for ETM values, highly significant correlations were found between most of the MRI methods investigated. In the cranial thigh, for example, the vb medians (interquartile range, IQRs) of IHVS, MVDE, BD, EqMRI, 2CXM and ETM were vb = 0.7(0.3)%, 1.1(0.4)%, 1.1(0.4)%, 1.4(0.3)%, 1.2(1.8)% and 0.1(0.2)%, respectively. Variances, expressed by the difference between third and first quartiles (IQR) were highest for the 2CXM for all muscle groups. High correlations between the values in four muscle groups—medial, cranial, lateral thigh and lower leg - estimated with MRI and histology were found between BD and EqMRI, MVDE and 2CXM and IHVS and ETM. Except for the ETM, no significant differences between the vb medians of all MRI methods were revealed with the Wilcoxon rank sum test. The same holds for all muscle regions using the 2CXM and MVDE. Except for cranial thigh muscle, no significant difference was found between EqMRI and MVDE. And except for the cranial thigh and the lower leg muscle, there was also no significant difference between the vb medians of BD and MVDE. Overall, there was good vb agreement between histology and the BPCA MRI methods and the 2CXM LMCA approach with the exception of the ETM method. Although LMCA models have the advantage of providing excellent curve fits and can in principle determine more physiological parameters than BPCA methods, they yield more inaccurate results.
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Affiliation(s)
- Stefan Hindel
- Department of Radiotherapy, Medical Physics, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
- * E-mail:
| | - Anika Söhner
- Department of Radiotherapy, Medical Physics, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
| | - Marc Maaß
- Department of General and Visceral Surgery at Evangelical Hospital Wesel, Wesel, North Rhine-Westphalia, Germany
| | - Wolfgang Sauerwein
- Department of Radiotherapy, Medical Physics, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
| | - Dorothe Möllmann
- Department of Pathology, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
| | - Hideo Andreas Baba
- Department of Pathology, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
| | - Martin Kramer
- Hospital of Veterinary Medicine, Department of Small Animal Surgery, Justus Liebig University Giessen, Giessen, Hesse, Germany
| | - Lutz Lüdemann
- Department of Radiotherapy, Medical Physics, University Hospital Essen, Essen, North Rhine-Westphalia, Germany
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T2*-Correction in Dynamic Contrast-Enhanced Magnetic Resonance Imaging of Glioblastoma From a Half Dose of High-Relaxivity Contrast Agent. J Comput Assist Tomogr 2017; 41:916-921. [DOI: 10.1097/rct.0000000000000611] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Li CH, Chen FH, Schellingerhout D, Lin YS, Hong JH, Liu HL. Flow versus permeability weighting in estimating the forward volumetric transfer constant (K trans) obtained by DCE-MRI with contrast agents of differing molecular sizes. Magn Reson Imaging 2016; 36:105-111. [PMID: 27989901 DOI: 10.1016/j.mri.2016.10.027] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2016] [Accepted: 10/26/2016] [Indexed: 01/02/2023]
Abstract
PURPOSE To quantify the differential plasma flow- (Fp-) and permeability surface area product per unit mass of tissue- (PS-) weighting in forward volumetric transfer constant (Ktrans) estimates by using a low molecular (Gd-DTPA) versus high molecular (Gadomer) weight contrast agent in dynamic contrast enhanced (DCE) MRI. MATERIALS AND METHODS DCE MRI was performed using a 7T animal scanner in 14 C57BL/6J mice syngeneic for TRAMP tumors, by administering Gd-DTPA (0.9kD) in eight mice and Gadomer (35kD) in the remainder. The acquisition time was 10min with a sampling rate of one image every 2s. Pharmacokinetic modeling was performed to obtain Ktrans by using Extended Tofts model (ETM). In addition, the adiabatic approximation to the tissue homogeneity (AATH) model was employed to obtain the relative contributions of Fp and PS. RESULTS The Ktrans values derived from DCE-MRI with Gd-DTPA showed significant correlations with both PS (r2=0.64, p=0.009) and Fp (r2=0.57, p=0.016), whereas those with Gadomer were found only significantly correlated with PS (r2=0.96, p=0.0003) but not with Fp (r2=0.34, p=0.111). A voxel-based analysis showed that Ktrans approximated PS (<30% difference) in 78.3% of perfused tumor volume for Gadomer, but only 37.3% for Gd-DTPA. CONCLUSIONS The differential contributions of Fp and PS in estimating Ktrans values vary with the molecular weight of the contrast agent used. The macromolecular contrast agent resulted in Ktrans values that were much less dependent on flow. These findings support the use of macromolecular contrast agents for estimating tumor vessel permeability with DCE-MRI.
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Affiliation(s)
- Cheng-He Li
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Fang-Hsin Chen
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Dawid Schellingerhout
- Departments of Diagnostic Radiology and Cancer Systems Imaging, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA
| | - Yu-Shi Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Ji-Hong Hong
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan; Radiation Biology Research Center, Institute for Radiological Research, Chang Gung University, Chang Gung Memorial Hospital, Taoyuan, Taiwan; Department of Radiation Oncology, Chang Gung Memorial Hospital at Linko, Taoyuan, Taiwan
| | - Ho-Ling Liu
- Department of Imaging Physics, The University of Texas M.D. Anderson Cancer Center, Houston, TX, USA.
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Comparison of Cerebral Blood Volume and Plasma Volume in Untreated Intracranial Tumors. PLoS One 2016; 11:e0161807. [PMID: 27584684 PMCID: PMC5008702 DOI: 10.1371/journal.pone.0161807] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2016] [Accepted: 08/14/2016] [Indexed: 02/06/2023] Open
Abstract
Purpose Plasma volume and blood volume are imaging-derived parameters that are often used to evaluation intracranial tumors. Physiologically, these parameters are directly related, but their two different methods of measurements, T1-dynamic contrast enhanced (DCE)- and T2-dynamic susceptibility contrast (DSC)-MR utilize different model assumptions and approaches. This poses the question of whether the interchangeable use of T1-DCE-MRI derived fractionated plasma volume (vp) and relative cerebral blood volume (rCBV) assessed using DSC-MRI, particularly in glioblastoma, is reliable, and if this relationship can be generalized to other types of brain tumors. Our goal was to examine the hypothetical correlation between these parameters in three most common intracranial tumor types. Methods Twenty-four newly diagnosed, treatment naïve brain tumor patients, who had undergone DCE- and DSC-MRI, were classified in three histologically proven groups: glioblastoma (n = 7), meningioma (n = 9), and intraparenchymal metastases (n = 8). The rCBV was obtained from DSC after normalization with the normal-appearing anatomically symmetrical contralateral white matter. Correlations between these parameters were evaluated using Pearson (r), Spearman's (ρ) and Kendall’s tau-b (τB) rank correlation coefficient. Results The Pearson, Spearman and Kendall’s correlation between vp with rCBV were r = 0.193, ρ = 0.253 and τB = 0.33 (p-Pearson = 0.326, p-Spearman= 0.814 and p-Kendall= 0.823) in glioblastoma, r = -0.007, ρ = 0.051 and τB = 0.135 (p-Pearson = 0.970, p-Spearman= 0.765 and p-Kendall= 0.358) in meningiomas, and r = 0.289, ρ = 0.228 and τB = 0.239 (p-Pearson = 0.109, p-Spearman= 0.210 and p-Kendall= 0.095) in metastasis. Conclusion Results indicate that no correlation exists between vp with rCBV in glioblastomas, meningiomas and intraparenchymal metastatic lesions. Consequently, these parameters, as calculated in this study, should not be used interchangeably in either research or clinical practice.
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Sung YS, Park B, Choi Y, Lim HS, Woo DC, Kim KW, Kim JK. Dynamic contrast-enhanced MRI for oncology drug development. J Magn Reson Imaging 2016; 44:251-64. [PMID: 26854494 DOI: 10.1002/jmri.25173] [Citation(s) in RCA: 24] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2015] [Accepted: 01/15/2016] [Indexed: 12/17/2022] Open
Abstract
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is a promising tool for evaluating tumor vascularity, as it can provide vasculature-derived, functional, and quantitative parameters. To implement DCE-MRI parameters as biomarkers for monitoring the effect of antiangiogenic or vascular-disrupting treatment, two crucial elements of surrogate endpoint, ie, validation and qualification, should be satisfied. Although early studies have shown the accuracy and reliability of DCE-MRI parameters for evaluating treatment-driven vascular alterations, there have been an increasing number of studies demonstrating the limitations of DCE-MRI parameters as surrogate endpoints. Therefore, in order to improve the application of DCE-MRI parameters in drug development, it is necessary to establish a standardized evaluation method and to determine the correct therapeutics-oriented meaning of individual DCE-MRI parameter. In this regard, this article describes the biophysical background and data acquisition/analysis techniques of DCE-MRI while focusing on the validation and qualification issues. Specifically, the causes of disagreement and confusion encountered in the preclinical and clinical trials using DCE-MRI are presented in detail. Finally, considering these limitations, we present potential strategies to optimize implementation of DCE-MRI. J. Magn. Reson. Imaging 2016;44:251-264.
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Affiliation(s)
- Yu Sub Sung
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Bumwoo Park
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Yoonseok Choi
- Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Hyeong-Seok Lim
- Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Department of Clinical Pharmacology and Therapeutics, Ulsan University College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Dong-Cheol Woo
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Kyung Won Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
| | - Jeong Kon Kim
- Department of Radiology, Research Institute of Radiology, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea.,Center for Bioimaging of New Drug Development, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, South Korea
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Zhang Y, Kapur P, Yuan Q, Xi Y, Carvo I, Signoretti S, Dimitrov I, Cadeddu JA, Margulis V, Muradyan N, Brugarolas J, Madhuranthakam AJ, Pedrosa I. Tumor Vascularity in Renal Masses: Correlation of Arterial Spin-Labeled and Dynamic Contrast-Enhanced Magnetic Resonance Imaging Assessments. Clin Genitourin Cancer 2016; 14:e25-36. [PMID: 26422014 PMCID: PMC4698181 DOI: 10.1016/j.clgc.2015.08.007] [Citation(s) in RCA: 30] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2015] [Revised: 08/12/2015] [Accepted: 08/24/2015] [Indexed: 01/18/2023]
Abstract
UNLABELLED Arterial spin-labeled (ASL) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) have been proposed to quantitatively assess vascularity in renal cell carcinoma (RCC). However, there are intrinsic differences between these 2 imaging methods, such as the relative contribution of vascular permeability and blood flow to signal intensity for DCE MRI. We found a correlation between ASL perfusion and the DCE-derived volume transfer constant and rate constant parameters in renal masses > 2 cm in size and these measures correlated with microvessel density in clear cell RCC. BACKGROUND The objective of this study was to investigate potential correlations between perfusion using arterial spin-labeled (ASL) magnetic resonance imaging (MRI) and dynamic contrast-enhanced (DCE) MRI-derived quantitative measures of vascularity in renal masses > 2 cm and to correlate these with microvessel density (MVD) in clear cell renal cell carcinoma (ccRCC). PATIENTS AND METHODS Informed written consent was obtained from all patients before imaging in this Health Insurance Portability and Accountability Act-compliant, institutional review board-approved, prospective study. Thirty-six consecutive patients scheduled for surgery of a known renal mass > 2 cm underwent 3T ASL and DCE MRI. ASL perfusion measures (PASL) of mean, peak, and low perfusion areas within the mass were correlated to DCE-derived volume transfer constant (K(trans)), rate constant (Kep), and fractional volume of the extravascular extracellular space (Ve) in the same locations using a region of interest analysis. MRI data were correlated to MVD measures in the same tumor regions in ccRCC. Spearman correlation was used to evaluate the correlation between PASL and DCE-derived measurements, and MVD. P < .05 was considered statistically significant. RESULTS Histopathologic diagnosis was obtained in 36 patients (25 men; mean age 58 ± 12 years). PASL correlated with K(trans) (ρ = 0.48 and P = .0091 for the entire tumor and ρ = 0.43 and P = .03 for the high flow area, respectively) and Kep (ρ = 0.46 and P = .01 for the entire tumor and ρ = 0.52 and P = .008 for the high flow area, respectively). PASL (ρ = 0.66; P = .0002), K(trans) (ρ = 0.61; P = .001), and Kep (ρ = 0.64; P = .0006) also correlated with MVD in high and low perfusion areas in ccRCC. CONCLUSION PASL correlated with the DCE-derived measures of vascular permeability and flow, K(trans) and Kep, in renal masses > 2 cm in size. Both measures correlated to MVD in clear cell histology.
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Affiliation(s)
- Yue Zhang
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | - Payal Kapur
- Department of Urology, UT Southwestern Medical Center, Dallas, TX; Department of Pathology, UT Southwestern Medical Center, Dallas, TX
| | - Qing Yuan
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | - Yin Xi
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX
| | - Ingrid Carvo
- Department of Pathology, Brigham and Women's Hospital, Boston, MA
| | | | - Ivan Dimitrov
- Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX; Philips Medical Systems, Cleveland, OH
| | - Jeffrey A Cadeddu
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX; Department of Urology, UT Southwestern Medical Center, Dallas, TX
| | - Vitaly Margulis
- Department of Urology, UT Southwestern Medical Center, Dallas, TX
| | | | - James Brugarolas
- Department of Internal Medicine, UT Southwestern Medical Center, Dallas, TX; Developmental Biology, UT Southwestern Medical Center, Dallas, TX
| | - Ananth J Madhuranthakam
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX; Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX
| | - Ivan Pedrosa
- Department of Radiology, UT Southwestern Medical Center, Dallas, TX; Advanced Imaging Research Center, UT Southwestern Medical Center, Dallas, TX.
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Haeck JC, Bol K, de Ridder CMA, Brunel L, Fehrentz JA, Martinez J, van Weerden WM, Bernsen MR, de Jong M, Veenland JF. Imaging heterogeneity of peptide delivery and binding in solid tumors using SPECT imaging and MRI. EJNMMI Res 2016; 6:3. [PMID: 26769345 PMCID: PMC4713394 DOI: 10.1186/s13550-016-0160-4] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2015] [Accepted: 01/05/2016] [Indexed: 01/03/2023] Open
Abstract
Background As model system, a solid-tumor patient-derived xenograft (PDX) model characterized by high peptide receptor expression and histological tissue homogeneity was used to study radiopeptide targeting. In this solid-tumor model, high tumor uptake of targeting peptides was expected. However, in vivo SPECT images showed substantial heterogeneous radioactivity accumulation despite homogenous receptor distribution in the tumor xenografts as assessed by in vitro autoradiography. We hypothesized that delivery of peptide to the tumor cells is dictated by adequate local tumor perfusion. To study this relationship, sequential SPECT/CT and MRI were performed to assess the role of vascular functionality in radiopeptide accumulation. Methods High-resolution SPECT and dynamic contrast-enhanced (DCE)-MRI were acquired in six mice bearing PC295 PDX tumors expressing the gastrin-releasing peptide (GRP) receptor. Two hours prior to SPECT imaging, animals received 25 MBq 111In(DOTA-(βAla)2-JMV594) (25 pmol). Images were acquired using multipinhole SPECT/CT. Directly after SPECT imaging, MR images were acquired on a 7.0-T dedicated animal scanner. DCE-MR images were quantified using semi-quantitative and quantitative models. The DCE-MR and SPECT images were spatially aligned to compute the correlations between radioactivity and DCE-MRI-derived parameters over the tumor. Results Whereas histology, in vitro autoradiography, and multiple-weighted MRI scans all showed homogenous tissue characteristics, both SPECT and DCE-MRI showed heterogeneous distribution patterns throughout the tumor. The average Spearman’s correlation coefficient between SPECT and DCE-MRI ranged from 0.57 to 0.63 for the “exchange-related” DCE-MRI perfusion parameters. Conclusions A positive correlation was shown between exchange-related DCE-MRI perfusion parameters and the amount of radioactivity accumulated as measured by SPECT, demonstrating that vascular function was an important aspect of radiopeptide distribution in solid tumors. The combined use of SPECT and MRI added crucial information on the perfusion efficiency versus radiopeptide uptake in solid tumors and showed that functional tumor characteristics varied locally even when the tissue appeared homogenous on current standard assessment techniques. Electronic supplementary material The online version of this article (doi:10.1186/s13550-016-0160-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- J C Haeck
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands. .,Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands. .,Department of Nuclear Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015 GE, Rotterdam, the Netherlands.
| | - K Bol
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands.,Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands.,Department of Nuclear Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015 GE, Rotterdam, the Netherlands
| | - C M A de Ridder
- Department of Urology, Erasmus MC, Rotterdam, the Netherlands
| | - L Brunel
- IBMM, UMR 5247, CNRS, ENSCM, Faculté de Pharmacie, Université Montpellier, Montpellier, France
| | - J A Fehrentz
- IBMM, UMR 5247, CNRS, ENSCM, Faculté de Pharmacie, Université Montpellier, Montpellier, France
| | - J Martinez
- IBMM, UMR 5247, CNRS, ENSCM, Faculté de Pharmacie, Université Montpellier, Montpellier, France
| | - W M van Weerden
- Department of Urology, Erasmus MC, Rotterdam, the Netherlands
| | - M R Bernsen
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands.,Department of Nuclear Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015 GE, Rotterdam, the Netherlands
| | - M de Jong
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands.,Department of Nuclear Medicine, Erasmus MC, Dr. Molewaterplein 50, 3015 GE, Rotterdam, the Netherlands
| | - J F Veenland
- Department of Radiology, Erasmus MC, Rotterdam, the Netherlands.,Department of Medical Informatics, Erasmus MC, Rotterdam, the Netherlands
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Guo J, Glass JO, McCarville MB, Shulkin BL, Daryani VM, Stewart CF, Wu J, Mao S, Dwek JR, Fayad LM, Madewell JE, Navid F, Daw NC, Reddick WE. Assessing vascular effects of adding bevacizumab to neoadjuvant chemotherapy in osteosarcoma using DCE-MRI. Br J Cancer 2015; 113:1282-8. [PMID: 26461056 PMCID: PMC4815789 DOI: 10.1038/bjc.2015.351] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2015] [Revised: 08/21/2015] [Accepted: 09/10/2015] [Indexed: 12/12/2022] Open
Abstract
BACKGROUND The purpose of this study was to assess the impact of bevacizumab alone and in combination with cytotoxic therapy on tumour vasculature in osteosarcoma (OS) using DCE-MRI. METHODS Six DCE-MRI and three (18)F-FDG PET examinations were scheduled in 42 subjects with newly diagnosed OS to monitor the response to antiangiogenic therapy alone and in combination with cytotoxic therapy before definitive surgery (week 10). Serial DCE-MRI parameters (K(trans), v(p), and v(e)) were examined for correlation with FDG-PET (SUV(max)) and association with drug exposure, and evaluated with clinical outcome. RESULTS K(trans) (P=0.041) and v(p) (P=0.001) significantly dropped from baseline at 24 h after the first dose of bevacizumab alone, but returned to baseline by 72 h. Greater exposure to bevacizumab was correlated with larger decreases in v(p) at day 5 (P=0.04) and week 10 (P=0.02). A lower K(trans) at week 10 was associated with greater percent necrosis (P=0.024) and longer event-free survival (P=0.034). CONCLUSIONS This is the first study to demonstrate significant changes of the plasma volume fraction and vascular leakage in OS with bevacizumab alone. The combination of demonstrated associations between drug exposure and imaging metrics, and imaging metrics and patient survival during neoadjuvant therapy, provides a compelling rationale for larger studies using DCE-MRI to assess vascular effects of therapy in OS.
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Affiliation(s)
- J Guo
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN 38105-3678, USA
| | - J O Glass
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN 38105-3678, USA
| | - M B McCarville
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN 38105-3678, USA
| | - B L Shulkin
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN 38105-3678, USA
| | - V M Daryani
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - C F Stewart
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - J Wu
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - S Mao
- Department of Biostatistics, St Jude Children's Research Hospital, Memphis, TN 38105, USA
| | - J R Dwek
- Department of Radiology, Rady Children's Hospital, San Diego, CA 92123, USA
| | - L M Fayad
- The Musculoskeletal Tumor Program, The Johns Hopkins University, Baltimore, MD 21287, USA
| | - J E Madewell
- Department of Diagnostic Radiology, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - F Navid
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
- Department of Pediatrics, College of Medicine, University of Tennessee Health Science Center, Memphis, TN 38163, USA
| | - N C Daw
- Department of Oncology, St Jude Children's Research Hospital, Memphis, TN 38105, USA
- Division of Pediatrics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - W E Reddick
- Department of Diagnostic Imaging, St Jude Children's Research Hospital, 262 Danny Thomas Place, Mail Stop 220, Memphis, TN 38105-3678, USA
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Rajendran R, Liang J, Tang MYA, Henry B, Chuang KH. Optimization of arterial spin labeling MRI for quantitative tumor perfusion in a mouse xenograft model. NMR IN BIOMEDICINE 2015; 28:988-997. [PMID: 26104980 DOI: 10.1002/nbm.3330] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/05/2014] [Revised: 03/18/2015] [Accepted: 04/22/2015] [Indexed: 06/04/2023]
Abstract
Perfusion is an important biomarker of tissue function and has been associated with tumor pathophysiology such as angiogenesis and hypoxia. Arterial spin labeling (ASL) MRI allows noninvasive and quantitative imaging of perfusion; however, the application in mouse xenograft tumor models has been challenging due to the low sensitivity and high perfusion heterogeneity. In this study, flow-sensitive alternating inversion recovery (FAIR) ASL was optimized for a mouse xenograft tumor. To assess the sensitivity and reliability for measuring low perfusion, the lumbar muscle was used as a reference region. By optimizing the number of averages and inversion times, muscle perfusion as low as 32.4 ± 4.8 (mean ± standard deviation) ml/100 g/min could be measured in 20 min at 7 T with a quantification error of 14.4 ± 9.1%. Applying the optimized protocol, heterogeneous perfusion ranging from 49.5 to 211.2 ml/100 g/min in a renal carcinoma was observed. To understand the relationship with tumor pathology, global and regional tumor perfusion was compared with histological staining of blood vessels (CD34), hypoxia (CAIX) and apoptosis (TUNEL). No correlation was observed when the global tumor perfusion was compared with these pathological parameters. Regional analysis shows that areas of high perfusion had low microvessel density, which was due to larger vessel area compared with areas of low perfusion. Nonetheless, these were not correlated with hypoxia or apoptosis. The results suggest that tumor perfusion may reflect certain aspect of angiogenesis, but its relationship with other pathologies needs further investigation.
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Affiliation(s)
- Reshmi Rajendran
- Magnetic Resonance Imaging Group, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Jieming Liang
- Magnetic Resonance Imaging Group, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Mei Yee Annie Tang
- Magnetic Resonance Imaging Group, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
| | - Brian Henry
- Translational Medicine Research Centre, MSD, Singapore
| | - Kai-Hsiang Chuang
- Magnetic Resonance Imaging Group, Singapore Bioimaging Consortium, Agency for Science, Technology and Research, Singapore
- Clinical Imaging Research Centre, National University of Singapore, Singapore
- Department of Physiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
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48
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Liu HL, Chang TT, Yan FX, Li CH, Lin YS, Wong AM. Assessment of vessel permeability by combining dynamic contrast-enhanced and arterial spin labeling MRI. NMR IN BIOMEDICINE 2015; 28:642-649. [PMID: 25880892 DOI: 10.1002/nbm.3297] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2014] [Revised: 02/19/2015] [Accepted: 03/05/2015] [Indexed: 06/04/2023]
Abstract
The forward volumetric transfer constant (K(trans)), a physiological parameter extracted from dynamic contrast-enhanced (DCE) MRI, is weighted by vessel permeability and tissue blood flow. The permeability × surface area product per unit mass of tissue (PS) in brain tumors was estimated in this study by combining the blood flow obtained through pseudo-continuous arterial spin labeling (PCASL) and K(trans) obtained through DCE MRI. An analytical analysis and a numerical simulation were conducted to understand how errors in the flow and K(trans) estimates would propagate to the resulting PS. Fourteen pediatric patients with brain tumors were scanned on a clinical 3-T MRI scanner. PCASL perfusion imaging was performed using a three-dimensional (3D) fast-spin-echo readout module to determine blood flow. DCE imaging was performed using a 3D spoiled gradient-echo sequence, and the K(trans) map was obtained with the extended Tofts model. The numerical analysis demonstrated that the uncertainty of PS was predominantly dependent on that of K(trans) and was relatively insensitive to the flow. The average PS values of the whole tumors ranged from 0.006 to 0.217 min(-1), with a mean of 0.050 min(-1) among the patients. The mean K(trans) value was 18% lower than the PS value, with a maximum discrepancy of 25%. When the parametric maps were compared on a voxel-by-voxel basis, the discrepancies between PS and K(trans) appeared to be heterogeneous within the tumors. The PS values could be more than two-fold higher than the K(trans) values for voxels with high K(trans) levels. This study proposes a method that is easy to implement in clinical practice and has the potential to improve the quantification of the microvascular properties of brain tumors.
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Affiliation(s)
- Ho-Ling Liu
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Imaging Physics, University of Texas M. D. Anderson Cancer Center, Houston, TX, USA
| | - Ting-Ting Chang
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Feng-Xian Yan
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
- Department of Radiology, Taipei Medical University/Shuang-Ho Hospital, New Taipei City, Taiwan
| | - Cheng-He Li
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Yu-Shi Lin
- Department of Medical Imaging and Radiological Sciences, College of Medicine, Chang Gung University, Taoyuan, Taiwan
| | - Alex M Wong
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Keelong, Linkou Medical Center, Taiwan
- College of Medicine, Chang Gung University, Taoyuan, Taiwan
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Rukat T, Walker-Samuel S, Reinsberg SA. Dynamic contrast-enhanced MRI in mice: an investigation of model parameter uncertainties. Magn Reson Med 2015; 73:1979-87. [PMID: 25052296 DOI: 10.1002/mrm.25319] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2014] [Revised: 05/13/2014] [Accepted: 05/23/2014] [Indexed: 11/08/2022]
Abstract
PURPOSE To establish the experimental factors that dominate the uncertainty of hemodynamic parameters in commonly used pharmacokinetic models. METHODS By fitting simulation results from a multiregion tissue exchange model (Multiple path, Multiple tracer, Indicator Dilution, 4 region), the precision and accuracy of hemodynamic parameters in dynamic contrast-enhanced MRI with four tracer kinetic models is investigated. The impact of various injection rates as well as imprecise knowledge of the arterial input functions is examined. RESULTS Fast injections are beneficial for K(trans) precision within the extended Tofts model and within the two-compartment exchange model but do not affect the other models under investigation. Biases from errors in the arterial input functions are mostly consistent in size and direction for the simple and the extended Tofts model, while they are hardly predictable for the other models. Errors in the hematocrit introduce the greatest loss in parameter accuracy, amounting to an average K(trans) bias of 40% for a 30% overestimation throughout all models. CONCLUSION This simulation study allows the detailed inspection of the isolated impact from various experimental conditions on parameter uncertainty. Because parameter uncertainty comparable to human studies was found, this study represents a validation of preclinical dynamic contrast-enhanced MRI for modeling human tumor physiology.
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Affiliation(s)
- Tammo Rukat
- Department of Physics and Astronomy, University of British Columbia, Vancouver, Canada; Department of Physics, Humboldt University, Berlin, Germany
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50
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Filice S, Crisi G. Dynamic Contrast-Enhanced Perfusion MRI of High Grade Brain Gliomas Obtained with Arterial or Venous Waveform Input Function. J Neuroimaging 2015; 26:124-9. [PMID: 25923172 DOI: 10.1111/jon.12254] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2015] [Accepted: 03/26/2015] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND AND PURPOSE The aim of this study was to evaluate the differences in dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) perfusion estimates of high-grade brain gliomas (HGG) due to the use of an input function (IF) obtained respectively from arterial (AIF) and venous (VIF) approaches by two different commercially available software applications. METHODS This prospective study includes 20 patients with pathologically confirmed diagnosis of high-grade gliomas. The data source was processed by using two DCE dedicated commercial packages, both based on the extended Toft model, but the first customized to obtain input function from arterial measurement and the second from sagittal sinus sampling. The quantitative parametric perfusion maps estimated from the two software packages were compared by means of a region of interest (ROI) analysis. The resulting input functions from venous and arterial data were also compared. RESULTS No significant difference has been found between the perfusion parameters obtained with the two different software packages (P-value < .05). The comparison of the VIFs and AIFs obtained by the two packages showed no statistical differences. CONCLUSIONS Direct comparison of DCE-MRI measurements with IF generated by means of arterial or venous waveform led to no statistical difference in quantitative metrics for evaluating HGG. However, additional research involving DCE-MRI acquisition protocols and post-processing would be beneficial to further substantiate the effectiveness of venous approach as the IF method compared with arterial-based IF measurement.
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Affiliation(s)
- Silvano Filice
- Department of Medical Physics and the Department of Neuroradiology, University Hospital of Parma, Parma, Italy
| | - Girolamo Crisi
- Department of Medical Physics and the Department of Neuroradiology, University Hospital of Parma, Parma, Italy
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