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Maruyama T, Hayashi N, Sato Y, Ogura T, Uehara M, Watanabe H, Kitoh Y. Artifact estimation network for MR images: effectiveness of batch normalization and dropout layers. BMC Med Imaging 2025; 25:144. [PMID: 40312665 PMCID: PMC12046729 DOI: 10.1186/s12880-025-01663-8] [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: 04/09/2024] [Accepted: 04/03/2025] [Indexed: 05/03/2025] Open
Abstract
BACKGROUND Magnetic resonance imaging (MRI) is an essential tool for medical diagnosis. However, artifacts may degrade images obtained through MRI, especially owing to patient movement. Existing methods that mitigate the artifact problem are subject to limitations including extended scan times. Deep learning architectures, such as U-Net, may be able to address these limitations. Optimizing deep learning networks with batch normalization (BN) and dropout layers enhances their convergence and accuracy. However, the influence of this strategy on U-Net has not been explored for artifact removal. METHODS This study developed a U-Net-based regression network for the removal of motion artifacts and investigated the impact of combining BN and dropout layers as a strategy for this purpose. A Transformer-based network from a previous study was also adopted for comparison. In total, 1200 images (with and without motion artifacts) were used to train and test three variations of U-Net. RESULTS The evaluation results demonstrated a significant improvement in network accuracy when BN and dropout layers were implemented. The peak signal-to-noise ratio of the reconstructed images was approximately doubled and the structural similarity index was improved by approximately 10% compared with those of the artifact images. CONCLUSIONS Although this study was limited to phantom images, the same strategy may be applied to more complex tasks, such as those directed at improving the quality of MR and CT images. We conclude that the accuracy of motion artifact removal can be improved by integrating BN and dropout layers into a U-Net-based network, with due consideration of the correct location and dropout rate.
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Affiliation(s)
- Tomoko Maruyama
- Division of Radiology, Shinshu University Hospital, Matsumoto, Nagano, 390-8621, Japan.
- Department of Radiological Technology, Graduate School of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan.
| | - Norio Hayashi
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan
| | - Yusuke Sato
- Department of Radiology, Gunma University Hospital, Maebashi, Gunma, 371-8511, Japan
| | - Toshihiro Ogura
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan
| | - Masumi Uehara
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan
| | - Haruyuki Watanabe
- Department of Radiological Technology, Gunma Prefectural College of Health Sciences, Maebashi, Gunma, 371-0052, Japan
| | - Yoshihiro Kitoh
- Division of Radiology, Shinshu University Hospital, Matsumoto, Nagano, 390-8621, Japan
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Sarkar A, Das A, Ram K, Ramanarayanan S, Joel SE, Sivaprakasam M. AutoDPS: An unsupervised diffusion model based method for multiple degradation removal in MRI. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2025; 263:108684. [PMID: 40023963 DOI: 10.1016/j.cmpb.2025.108684] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/18/2024] [Revised: 01/31/2025] [Accepted: 02/19/2025] [Indexed: 03/04/2025]
Abstract
BACKGROUND AND OBJECTIVE Diffusion models have demonstrated their ability in image generation and solving inverse problems like restoration. Unlike most existing deep-learning based image restoration techniques which rely on unpaired or paired data for degradation awareness, diffusion models offer an unsupervised degradation independent alternative. This is well-suited in the context of restoring artifact-corrupted Magnetic Resonance Images (MRI), where it is impractical to exactly model the degradations apriori. In MRI, multiple corruptions arise, for instance, from patient movement compounded by undersampling artifacts from the acquisition settings. METHODS To tackle this scenario, we propose AutoDPS, an unsupervised method for corruption removal in brain MRI based on Diffusion Posterior Sampling. Our method (i) performs motion-related corruption parameter estimation using a blind iterative solver, and (ii) utilizes the knowledge of the undersampling pattern when the corruption consists of both motion and undersampling artifacts. We incorporate this corruption operation during sampling to guide the generation in recovering high-quality images. RESULTS Despite being trained to denoise and tested on completely unseen corruptions, our method AutoDPS has shown ∼ 1.63 dB of improvement in PSNR over baselines for realistic 3D motion restoration and ∼ 0.5 dB of improvement for random motion with undersampling. Additionally, our experiments demonstrate AutoDPS's resilience to noise and its generalization capability under domain shift, showcasing its robustness and adaptability. CONCLUSION In this paper, we propose an unsupervised method that removes multiple corruptions, mainly motion with undersampling, in MRI images which are essential for accurate diagnosis. The experiments show promising results on realistic and composite artifacts with higher improvement margins as compared to other methods. Our code is available at https://github.com/arunima101/AutoDPS/tree/master.
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Affiliation(s)
- Arunima Sarkar
- Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India.
| | - Ayantika Das
- Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India
| | - Keerthi Ram
- Healthcare Technology Innovation Centre, IITM, Chennai 600036, Tamil Nadu, India
| | - Sriprabha Ramanarayanan
- Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India; Healthcare Technology Innovation Centre, IITM, Chennai 600036, Tamil Nadu, India
| | | | - Mohanasankar Sivaprakasam
- Department of Electrical Engineering, Indian Institute of Technology Madras (IITM), Chennai 600036, Tamil Nadu, India; Healthcare Technology Innovation Centre, IITM, Chennai 600036, Tamil Nadu, India
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Chen G, Xie H, Rao X, Liu X, Otikovs M, Frydman L, Sun P, Zhang Z, Pan F, Yang L, Zhou X, Liu M, Bao Q, Liu C. MRI Motion Correction Through Disentangled CycleGAN Based on Multi-Mask K-Space Subsampling. IEEE TRANSACTIONS ON MEDICAL IMAGING 2025; 44:1907-1921. [PMID: 40030768 DOI: 10.1109/tmi.2024.3523949] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
This work proposes a new retrospective motion correction method, termed DCGAN-MS, which employs disentangled CycleGAN based on multi-mask k-space subsampling (DCGAN-MS) to address the image domain translation challenge. The multi-mask k-space subsampling operator is utilized to decrease the complexity of motion artifacts by randomly discarding motion-affected k-space lines. The network then disentangles the subsampled, motion-corrupted images into content and artifact features using specialized encoders, and generates motion-corrected images by decoding the content features. By utilizing multi-mask k-space subsampling, motion artifact features become more sparse compared to the original image domain, enhancing the efficiency of the DCGAN-MS network. This method effectively corrects motion artifacts in clinical gadoxetic acid-enhanced human liver MRI, human brain MRI from fastMRI, and preclinical rodent brain MRI. Quantitative improvements are demonstrated with SSIM values increasing from 0.75 to 0.86 for human liver MRI with simulated motion artifacts, and from 0.72 to 0.82 for rodent brain MRI with simulated motion artifacts. Correspondingly, PSNR values increased from 26.09 to 31.09 and from 25.10 to 31.77. The method's performance was further validated on clinical and preclinical motion-corrupted MRI using the Kernel Inception Distance (KID) and Fréchet Inception Distance (FID) metrics. Additionally, ablation experiments were conducted to confirm the effectiveness of the multi-mask k-space subsampling approach.
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Fujita S, Fushimi Y, Ito R, Matsui Y, Tatsugami F, Fujioka T, Ueda D, Fujima N, Hirata K, Tsuboyama T, Nozaki T, Yanagawa M, Kamagata K, Kawamura M, Yamada A, Nakaura T, Naganawa S. Advancing clinical MRI exams with artificial intelligence: Japan's contributions and future prospects. Jpn J Radiol 2025; 43:355-364. [PMID: 39548049 PMCID: PMC11868336 DOI: 10.1007/s11604-024-01689-y] [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: 08/30/2024] [Accepted: 10/22/2024] [Indexed: 11/17/2024]
Abstract
In this narrative review, we review the applications of artificial intelligence (AI) into clinical magnetic resonance imaging (MRI) exams, with a particular focus on Japan's contributions to this field. In the first part of the review, we introduce the various applications of AI in optimizing different aspects of the MRI process, including scan protocols, patient preparation, image acquisition, image reconstruction, and postprocessing techniques. Additionally, we examine AI's growing influence in clinical decision-making, particularly in areas such as segmentation, radiation therapy planning, and reporting assistance. By emphasizing studies conducted in Japan, we highlight the nation's contributions to the advancement of AI in MRI. In the latter part of the review, we highlight the characteristics that make Japan a unique environment for the development and implementation of AI in MRI examinations. Japan's healthcare landscape is distinguished by several key factors that collectively create a fertile ground for AI research and development. Notably, Japan boasts one of the highest densities of MRI scanners per capita globally, ensuring widespread access to the exam. Japan's national health insurance system plays a pivotal role by providing MRI scans to all citizens irrespective of socioeconomic status, which facilitates the collection of inclusive and unbiased imaging data across a diverse population. Japan's extensive health screening programs, coupled with collaborative research initiatives like the Japan Medical Imaging Database (J-MID), enable the aggregation and sharing of large, high-quality datasets. With its technological expertise and healthcare infrastructure, Japan is well-positioned to make meaningful contributions to the MRI-AI domain. The collaborative efforts of researchers, clinicians, and technology experts, including those in Japan, will continue to advance the future of AI in clinical MRI, potentially leading to improvements in patient care and healthcare efficiency.
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Affiliation(s)
- Shohei Fujita
- Department of Radiology, The University of Tokyo, 7-3-1 Hongo, Bunkyo, Tokyo, Japan.
| | - Yasutaka Fushimi
- Department of Diagnostic Imaging and Nuclear Medicine, Kyoto University Graduate School of Medicine, Sakyoku, Kyoto, Japan
| | - Rintaro Ito
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Yusuke Matsui
- Department of Radiology, Faculty of Medicine, Dentistry and Pharmaceutical Sciences, Okayama University, Kita-Ku, Okayama, Japan
| | - Fuminari Tatsugami
- Department of Diagnostic Radiology, Hiroshima University, Minami-Ku, Hiroshima City, Hiroshima, Japan
| | - Tomoyuki Fujioka
- Department of Diagnostic Radiology, Tokyo Medical and Dental University, Tokyo, Japan
| | - Daiju Ueda
- Department of Artificial Intelligence, Graduate School of Medicine, Osaka Metropolitan University, Abeno-Ku, Osaka, Japan
| | - Noriyuki Fujima
- Department of Diagnostic and Interventional Radiology, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Kenji Hirata
- Department of Nuclear Medicine, Hokkaido University Hospital, Sapporo, Hokkaido, Japan
| | - Takahiro Tsuboyama
- Department of Radiology, Kobe University Graduate School of Medicine, Chuo-Ku, Kobe, Japan
| | - Taiki Nozaki
- Department of Radiology, Keio University School of Medicine, Tokyo, Japan
| | - Masahiro Yanagawa
- Department of Radiology, Osaka University Graduate School of Medicine, Suita, Osaka, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Mariko Kawamura
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
| | - Akira Yamada
- Medical Data Science Course, Shinshu University School of Medicine, Matsumoto, Nagano, Japan
| | - Takeshi Nakaura
- Department of Diagnostic Radiology, Kumamoto University Graduate School of Medicine, Kumamoto, Kumamoto, Japan
| | - Shinji Naganawa
- Department of Radiology, Nagoya University Graduate School of Medicine, Nagoya, Aichi, Japan
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Ma ZP, Zhu YM, Zhang XD, Zhao YX, Zheng W, Yuan SR, Li GY, Zhang TL. Investigating the Use of Generative Adversarial Networks-Based Deep Learning for Reducing Motion Artifacts in Cardiac Magnetic Resonance. J Multidiscip Healthc 2025; 18:787-799. [PMID: 39963324 PMCID: PMC11830935 DOI: 10.2147/jmdh.s492163] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2024] [Accepted: 01/21/2025] [Indexed: 02/20/2025] Open
Abstract
Objective To evaluate the effectiveness of deep learning technology based on generative adversarial networks (GANs) in reducing motion artifacts in cardiac magnetic resonance (CMR) cine sequences. Methods The training and testing datasets consisted of 2000 and 200 pairs of clear and blurry images, respectively, acquired through simulated motion artifacts in CMR cine sequences. These datasets were used to establish and train a deep learning GAN model. To assess the efficacy of the deep learning network in mitigating motion artifacts, 100 images with simulated motion artifacts and 37 images with real-world motion artifacts encountered in clinical practice were selected. Image quality pre- and post-optimization was assessed using metrics including Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), Leningrad Focus Measure, and a 5-point Likert scale. Results After GAN optimization, notable improvements were observed in the PSNR, SSIM, and focus measure metrics for the 100 images with simulated artifacts. These metrics increased from initial values of 23.85±2.85, 0.71±0.08, and 4.56±0.67, respectively, to 27.91±1.74, 0.83±0.05, and 7.74±0.39 post-optimization. Additionally, the subjective assessment scores significantly improved from 2.44±1.08 to 4.44±0.66 (P<0.001). For the 37 images with real-world artifacts, the Tenengrad Focus Measure showed a significant enhancement, rising from 6.06±0.91 to 10.13±0.48 after artifact removal. Subjective ratings also increased from 3.03±0.73 to 3.73±0.87 (P<0.001). Conclusion GAN-based deep learning technology effectively reduces motion artifacts present in CMR cine images, demonstrating significant potential for clinical application in optimizing CMR motion artifact management.
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Affiliation(s)
- Ze-Peng Ma
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China
- Hebei Key Laboratory of Precise Imaging of inflammation Tumors, Baoding, Hebei Province, 071000, People’s Republic of China
| | - Yue-Ming Zhu
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, 071002, People’s Republic of China
| | - Xiao-Dan Zhang
- Department of Ultrasound, Affiliated Hospital of Hebei University, Baoding, Hebei Province, 071000, People’s Republic of China
| | - Yong-Xia Zhao
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China
| | - Wei Zheng
- College of Electronic and Information Engineering, Hebei University, Baoding, Hebei Province, 071002, People’s Republic of China
| | - Shuang-Rui Yuan
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China
| | - Gao-Yang Li
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China
| | - Tian-Le Zhang
- Department of Radiology, Affiliated Hospital of Hebei University/ Clinical Medical College, Hebei University, Baoding, 071000, People’s Republic of China
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Singh R, Singh N, Kaur L. Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review. Phys Med Biol 2024; 69:23TR01. [PMID: 39569887 DOI: 10.1088/1361-6560/ad94c7] [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: 06/16/2024] [Accepted: 11/19/2024] [Indexed: 11/22/2024]
Abstract
Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.
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Affiliation(s)
- Ram Singh
- Department of Computer Science & Engineering, Punjabi University, Chandigarh Road, Patiala 147002, Punjab, India
| | - Navdeep Singh
- Department of Computer Science & Engineering, Punjabi University, Chandigarh Road, Patiala 147002, Punjab, India
| | - Lakhwinder Kaur
- Department of Computer Science & Engineering, Punjabi University, Chandigarh Road, Patiala 147002, Punjab, India
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Foti G, Longo C. Deep learning and AI in reducing magnetic resonance imaging scanning time: advantages and pitfalls in clinical practice. Pol J Radiol 2024; 89:e443-e451. [PMID: 39444654 PMCID: PMC11497590 DOI: 10.5114/pjr/192822] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2024] [Accepted: 09/03/2024] [Indexed: 10/25/2024] Open
Abstract
Magnetic resonance imaging (MRI) is a powerful imaging modality, but one of its drawbacks is its relatively long scanning time to acquire high-resolution images. Reducing the scanning time has become a critical area of focus in MRI, aiming to enhance patient comfort, reduce motion artifacts, and increase MRI throughput. In the past 5 years, artificial intelligence (AI)-based algorithms, particularly deep learning models, have been developed to reconstruct high-resolution images from significantly fewer data points. These new techniques significantly enhance MRI efficiency, improve patient comfort and lower patient motion artifacts. Improving MRI throughput with lower scanning duration increases accessibility, potentially reducing the need for additional MRI machines and associated costs. Several fields can benefit from shortened protocols, especially for routine exams. In oncologic imaging, faster MRI scans can facilitate more regular monitoring of cancer patients. In patients suffering from neurological disorders, rapid brain imaging can aid in the quick assessment of conditions like stroke, multiple sclerosis, and epilepsy, improving patient outcomes. In chronic inflammatory disease, faster imaging may help in reducing the interval between imaging to better check therapy outcomes. Additionally, reducing scanning time could effectively help MRI to play a role in emergency medicine and acute conditions such as trauma or acute ischaemic stroke. The purpose of this paper is to describe and discuss the advantages and disadvantages of introducing deep learning reconstruction techniques to reduce MRI scanning times in clinical practice.
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Affiliation(s)
- Giovanni Foti
- Department of Radiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Italy
| | - Chiara Longo
- Department of Radiology, IRCCS Sacro Cuore Don Calabria Hospital, Negrar, Italy
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Kolokythas O, Yaman Akcicek E, Akcicek H, Briller N, Rajamohan N, Yokoo T, Peeters HM, Revels JW, Moura Cunha G, Sahani DV, Mileto A. T1-weighted Motion Mitigation in Abdominal MRI: Technical Principles, Clinical Applications, Current Limitations, and Future Prospects. Radiographics 2024; 44:e230173. [PMID: 38990776 DOI: 10.1148/rg.230173] [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: 07/13/2024]
Abstract
T1-weighted (T1W) pulse sequences are an indispensable component of clinical protocols in abdominal MRI but usually require multiple breath holds (BHs) during the examination, which not all patients can sustain. Patient motion can affect the quality of T1W imaging so that key diagnostic information, such as intrinsic signal intensity and contrast enhancement image patterns, cannot be determined. Patient motion also has a negative impact on examination efficiency, as multiple acquisition attempts prolong the duration of the examination and often remain noncontributory. Techniques for mitigation of motion-related artifacts at T1W imaging include multiple arterial acquisitions within one BH; free breathing with respiratory gating or respiratory triggering; and radial imaging acquisition techniques, such as golden-angle radial k-space acquisition (stack-of-stars). While each of these techniques has inherent strengths and limitations, the selection of a specific motion-mitigation technique is based on several factors, including the clinical task under investigation, downstream technical ramifications, patient condition, and user preference. The authors review the technical principles of free-breathing motion mitigation techniques in abdominal MRI with T1W sequences, offer an overview of the established clinical applications, and outline the existing limitations of these techniques. In addition, practical guidance for abdominal MRI protocol strategies commonly encountered in clinical scenarios involving patients with limited BH abilities is rendered. Future prospects of free-breathing T1W imaging in abdominal MRI are also discussed. ©RSNA, 2024 See the invited commentary by Fraum and An in this issue.
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Affiliation(s)
- Orpheus Kolokythas
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Ebru Yaman Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Halit Akcicek
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Noah Briller
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Naveen Rajamohan
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Takeshi Yokoo
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Hans M Peeters
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Jonathan W Revels
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Guilherme Moura Cunha
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Dushyant V Sahani
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
| | - Achille Mileto
- From the Department of Radiology, University of Washington, 1959 NE Pacific St, Box 357115, Seattle, WA 98195 (O.K., N.B., G.M.C., D.V.S., A.M.); Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah (E.Y.A., H.A.); Department of Radiology, UT Southwestern Medical Center, Dallas, Tex (N.R., T.Y.); Department of MRI Development, Philips Healthcare, Best, the Netherlands (H.M.P.); Department of Radiology, New York University Langone Health-Long Island Division, New York, NY (J.W.R.)
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Mio M, Tabata N, Toyofuku T, Nakamura H. [Reduction of Motion Artifacts in Liver MRI Using Deep Learning with High-pass Filtering]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2024; 80:510-518. [PMID: 38462509 DOI: 10.6009/jjrt.2024-1408] [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] [Indexed: 03/12/2024]
Abstract
PURPOSE To investigate whether deep learning with high-pass filtering can be used to effectively reduce motion artifacts in magnetic resonance (MR) images of the liver. METHODS The subjects were 69 patients who underwent liver MR examination at our hospital. Simulated motion artifact images (SMAIs) were created from non-artifact images (NAIs) and used for deep learning. Structural similarity index measure (SSIM) and contrast ratio (CR) were used to verify the effect of reducing motion artifacts in motion artifact reduction image (MARI) output from the obtained deep learning model. In the visual assessment, reduction of motion artifacts and image sharpness were evaluated between motion artifact images (MAIs) and MARIs. RESULTS The SSIM values were 0.882 on the MARIs and 0.869 on the SMAIs. There was no statistically significant difference in CR between NAIs and MARIs. The visual assessment showed that MARIs had reduced motion artifacts and improved sharpness compared to MAIs. CONCLUSION The learning model in this study is indicated to be reduced motion artifacts without decreasing the sharpness of liver MR images.
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Affiliation(s)
- Motohira Mio
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Nariaki Tabata
- Department of Radiology, Fukuoka University Chikushi Hospital
| | - Tatsuo Toyofuku
- Department of Radiology, Fukuoka University Chikushi Hospital
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10
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Führes T, Saake M, Lorenz J, Seuss H, Bickelhaupt S, Uder M, Laun FB. Feature-guided deep learning reduces signal loss and increases lesion CNR in diffusion-weighted imaging of the liver. Z Med Phys 2024; 34:258-269. [PMID: 37543450 PMCID: PMC11156785 DOI: 10.1016/j.zemedi.2023.07.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2023] [Revised: 07/13/2023] [Accepted: 07/16/2023] [Indexed: 08/07/2023]
Abstract
PURPOSE This research aims to develop a feature-guided deep learning approach and compare it with an optimized conventional post-processing algorithm in order to enhance the image quality of diffusion-weighted liver images and, in particular, to reduce the pulsation-induced signal loss occurring predominantly in the left liver lobe. METHODS Data from 40 patients with liver lesions were used. For the conventional approach, the best-suited out of five examined algorithms was chosen. For the deep learning approach, a U-Net was trained. Instead of learning "gold-standard" target images, the network was trained to optimize four image features (lesion CNR, vessel darkness, data consistency, and pulsation artifact reduction), which could be assessed quantitatively using manually drawn ROIs. A quality score was calculated from these four features. As an additional quality assessment, three radiologists rated different features of the resulting images. RESULTS The conventional approach could substantially increase the lesion CNR and reduce the pulsation-induced signal loss. However, the vessel darkness was reduced. The deep learning approach increased the lesion CNR and reduced the signal loss to a slightly lower extent, but it could additionally increase the vessel darkness. According to the image quality score, the quality of the deep-learning images was higher than that of the images obtained using the conventional approach. The radiologist ratings were mostly consistent with the quantitative scores, but the overall quality ratings differed among the readers. CONCLUSION Unlike the conventional algorithm, the deep-learning algorithm increased the vessel darkness. Therefore, it may be a viable alternative to conventional algorithms.
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Affiliation(s)
- Tobit Führes
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
| | - Marc Saake
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Jennifer Lorenz
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Hannes Seuss
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany; Department of Radiology, Klinikum Forchheim - Fränkische Schweiz, Forchheim, Germany
| | - Sebastian Bickelhaupt
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Michael Uder
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
| | - Frederik Bernd Laun
- Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
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Spieker V, Eichhorn H, Hammernik K, Rueckert D, Preibisch C, Karampinos DC, Schnabel JA. Deep Learning for Retrospective Motion Correction in MRI: A Comprehensive Review. IEEE TRANSACTIONS ON MEDICAL IMAGING 2024; 43:846-859. [PMID: 37831582 DOI: 10.1109/tmi.2023.3323215] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/15/2023]
Abstract
Motion represents one of the major challenges in magnetic resonance imaging (MRI). Since the MR signal is acquired in frequency space, any motion of the imaged object leads to complex artefacts in the reconstructed image in addition to other MR imaging artefacts. Deep learning has been frequently proposed for motion correction at several stages of the reconstruction process. The wide range of MR acquisition sequences, anatomies and pathologies of interest, and motion patterns (rigid vs. deformable and random vs. regular) makes a comprehensive solution unlikely. To facilitate the transfer of ideas between different applications, this review provides a detailed overview of proposed methods for learning-based motion correction in MRI together with their common challenges and potentials. This review identifies differences and synergies in underlying data usage, architectures, training and evaluation strategies. We critically discuss general trends and outline future directions, with the aim to enhance interaction between different application areas and research fields.
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12
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Pan F, Fan Q, Xie H, Bai C, Zhang Z, Chen H, Yang L, Zhou X, Bao Q, Liu C. Correction of Arterial-Phase Motion Artifacts in Gadoxetic Acid-Enhanced Liver MRI Using an Innovative Unsupervised Network. Bioengineering (Basel) 2023; 10:1192. [PMID: 37892922 PMCID: PMC10604307 DOI: 10.3390/bioengineering10101192] [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: 08/16/2023] [Revised: 09/30/2023] [Accepted: 10/10/2023] [Indexed: 10/29/2023] Open
Abstract
This study aims to propose and evaluate DR-CycleGAN, a disentangled unsupervised network by introducing a novel content-consistency loss, for removing arterial-phase motion artifacts in gadoxetic acid-enhanced liver MRI examinations. From June 2020 to July 2021, gadoxetic acid-enhanced liver MRI data were retrospectively collected in this center to establish training and testing datasets. Motion artifacts were semi-quantitatively assessed using a five-point Likert scale (1 = no artifact, 2 = mild, 3 = moderate, 4 = severe, and 5 = non-diagnostic) and quantitatively evaluated using the structural similarity index (SSIM) and peak signal-to-noise ratio (PSNR). The datasets comprised a training dataset (308 examinations, including 58 examinations with artifact grade = 1 and 250 examinations with artifact grade ≥ 2), a paired test dataset (320 examinations, including 160 examinations with artifact grade = 1 and paired 160 examinations with simulated motion artifacts of grade ≥ 2), and an unpaired test dataset (474 examinations with artifact grade ranging from 1 to 5). The performance of DR-CycleGAN was evaluated and compared with a state-of-the-art network, Cycle-MedGAN V2.0. As a result, in the paired test dataset, DR-CycleGAN demonstrated significantly higher SSIM and PSNR values and lower motion artifact grades compared to Cycle-MedGAN V2.0 (0.89 ± 0.07 vs. 0.84 ± 0.09, 32.88 ± 2.11 vs. 30.81 ± 2.64, and 2.7 ± 0.7 vs. 3.0 ± 0.9, respectively; p < 0.001 each). In the unpaired test dataset, DR-CycleGAN also exhibited a superior motion artifact correction performance, resulting in a significant decrease in motion artifact grades from 2.9 ± 1.3 to 2.0 ± 0.6 compared to Cycle-MedGAN V2.0 (to 2.4 ± 0.9, p < 0.001). In conclusion, DR-CycleGAN effectively reduces motion artifacts in the arterial phase images of gadoxetic acid-enhanced liver MRI examinations, offering the potential to enhance image quality.
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Affiliation(s)
- Feng Pan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Qianqian Fan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Han Xie
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Chongxin Bai
- School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China;
| | - Zhi Zhang
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Hebing Chen
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Lian Yang
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China; (F.P.); (Q.F.); (H.C.); (L.Y.)
| | - Xin Zhou
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
- University of Chinese Academy of Sciences, Beijing 100864, China
- Optics Valley Laboratory, Wuhan 430074, China
| | - Qingjia Bao
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
| | - Chaoyang Liu
- State Key Laboratory of Magnetic Resonance and Atomic and Molecular Physics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430071, China; (H.X.); (Z.Z.); (X.Z.)
- University of Chinese Academy of Sciences, Beijing 100864, China
- Optics Valley Laboratory, Wuhan 430074, China
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Simkó A, Ruiter S, Löfstedt T, Garpebring A, Nyholm T, Bylund M, Jonsson J. Improving MR image quality with a multi-task model, using convolutional losses. BMC Med Imaging 2023; 23:148. [PMID: 37784039 PMCID: PMC10544274 DOI: 10.1186/s12880-023-01109-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/25/2023] [Indexed: 10/04/2023] Open
Abstract
PURPOSE During the acquisition of MRI data, patient-, sequence-, or hardware-related factors can introduce artefacts that degrade image quality. Four of the most significant tasks for improving MRI image quality have been bias field correction, super-resolution, motion-, and noise correction. Machine learning has achieved outstanding results in improving MR image quality for these tasks individually, yet multi-task methods are rarely explored. METHODS In this study, we developed a model to simultaneously correct for all four aforementioned artefacts using multi-task learning. Two different datasets were collected, one consisting of brain scans while the other pelvic scans, which were used to train separate models, implementing their corresponding artefact augmentations. Additionally, we explored a novel loss function that does not only aim to reconstruct the individual pixel values, but also the image gradients, to produce sharper, more realistic results. The difference between the evaluated methods was tested for significance using a Friedman test of equivalence followed by a Nemenyi post-hoc test. RESULTS Our proposed model generally outperformed other commonly-used correction methods for individual artefacts, consistently achieving equal or superior results in at least one of the evaluation metrics. For images with multiple simultaneous artefacts, we show that the performance of using a combination of models, trained to correct individual artefacts depends heavily on the order that they were applied. This is not an issue for our proposed multi-task model. The model trained using our novel convolutional loss function always outperformed the model trained with a mean squared error loss, when evaluated using Visual Information Fidelity, a quality metric connected to perceptual quality. CONCLUSION We trained two models for multi-task MRI artefact correction of brain, and pelvic scans. We used a novel loss function that significantly improves the image quality of the outputs over using mean squared error. The approach performs well on real world data, and it provides insight into which artefacts it detects and corrects for. Our proposed model and source code were made publicly available.
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Affiliation(s)
- Attila Simkó
- Department of Radiation Sciences, Umeå University, Umeå, Sweden.
| | - Simone Ruiter
- Department of Biomedical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - Tommy Löfstedt
- Department of Computing Science, Umeå University, Umeå, Sweden
| | | | - Tufve Nyholm
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Mikael Bylund
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
| | - Joakim Jonsson
- Department of Radiation Sciences, Umeå University, Umeå, Sweden
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Zhong NN, Wang HQ, Huang XY, Li ZZ, Cao LM, Huo FY, Liu B, Bu LL. Enhancing head and neck tumor management with artificial intelligence: Integration and perspectives. Semin Cancer Biol 2023; 95:52-74. [PMID: 37473825 DOI: 10.1016/j.semcancer.2023.07.002] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2023] [Revised: 07/11/2023] [Accepted: 07/15/2023] [Indexed: 07/22/2023]
Abstract
Head and neck tumors (HNTs) constitute a multifaceted ensemble of pathologies that primarily involve regions such as the oral cavity, pharynx, and nasal cavity. The intricate anatomical structure of these regions poses considerable challenges to efficacious treatment strategies. Despite the availability of myriad treatment modalities, the overall therapeutic efficacy for HNTs continues to remain subdued. In recent years, the deployment of artificial intelligence (AI) in healthcare practices has garnered noteworthy attention. AI modalities, inclusive of machine learning (ML), neural networks (NNs), and deep learning (DL), when amalgamated into the holistic management of HNTs, promise to augment the precision, safety, and efficacy of treatment regimens. The integration of AI within HNT management is intricately intertwined with domains such as medical imaging, bioinformatics, and medical robotics. This article intends to scrutinize the cutting-edge advancements and prospective applications of AI in the realm of HNTs, elucidating AI's indispensable role in prevention, diagnosis, treatment, prognostication, research, and inter-sectoral integration. The overarching objective is to stimulate scholarly discourse and invigorate insights among medical practitioners and researchers to propel further exploration, thereby facilitating superior therapeutic alternatives for patients.
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Affiliation(s)
- Nian-Nian Zhong
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Han-Qi Wang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Xin-Yue Huang
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Zi-Zhan Li
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Lei-Ming Cao
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Fang-Yi Huo
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China
| | - Bing Liu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
| | - Lin-Lin Bu
- State Key Laboratory of Oral & Maxillofacial Reconstruction and Regeneration, Key Laboratory of Oral Biomedicine Ministry of Education, Hubei Key Laboratory of Stomatology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China; Department of Oral & Maxillofacial - Head Neck Oncology, School & Hospital of Stomatology, Wuhan University, Wuhan 430079, China.
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15
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Funayama S, Motosugi U, Ichikawa S, Morisaka H, Omiya Y, Onishi H. Model-based Deep Learning Reconstruction Using a Folded Image Training Strategy for Abdominal 3D T1-weighted Imaging. Magn Reson Med Sci 2023; 22:515-526. [PMID: 36351603 PMCID: PMC10552667 DOI: 10.2463/mrms.mp.2021-0103] [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: 07/27/2021] [Accepted: 08/20/2022] [Indexed: 10/03/2023] Open
Abstract
PURPOSE To evaluate the feasibility of folded image training strategy (FITS) and the quality of images reconstructed using the improved model-based deep learning (iMoDL) network trained with FITS (FITS-iMoDL) for abdominal MR imaging. METHODS This retrospective study included abdominal 3D T1-weighted images of 122 patients. In the experimental analyses, peak SNR (PSNR) and structure similarity index (SSIM) of images reconstructed with FITS-iMoDL were compared with those with the following reconstruction methods: conventional model-based deep learning (conv-MoDL), MoDL trained with FITS (FITS-MoDL), total variation regularized compressed sensing (CS), and parallel imaging (CG-SENSE). In the clinical analysis, SNR and image contrast were measured on the reference, FITS-iMoDL, and CS images. Three radiologists evaluated the image quality using a 5-point scale to determine the mean opinion score (MOS). RESULTS The PSNR of FITS-iMoDL was significantly higher than that of FITS-MoDL, conv-MoDL, CS, and CG-SENSE (P < 0.001). The SSIM of FITS-iMoDL was significantly higher than those of the others (P < 0.001), except for FITS-MoDL (P = 0.056). In the clinical analysis, the SNR of FITS-iMoDL was significantly higher than that of the reference and CS (P < 0.0001). Image contrast was equivalent within an equivalence margin of 10% among these three image sets (P < 0.0001). MOS was significantly improved in FITS-iMoDL (P < 0.001) compared with CS images in terms of liver edge and vessels conspicuity, lesion depiction, artifacts, blurring, and overall image quality. CONCLUSION The proposed method, FITS-iMoDL, allowed a deeper MoDL reconstruction network without increasing memory consumption and improved image quality on abdominal 3D T1-weighted imaging compared with CS images.
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Affiliation(s)
- Satoshi Funayama
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Utaroh Motosugi
- Department of Radiology, Kofu-Kyoritsu Hospital, Kofu, Yamanashi, Japan
| | - Shintaro Ichikawa
- Department of Radiology, Hamamatsu University School of Medicine, Hamamatsu, Shizuoka, Japan
| | - Hiroyuki Morisaka
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Yoshie Omiya
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
| | - Hiroshi Onishi
- Department of Radiology, University of Yamanashi, Chuo, Yamanashi, Japan
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Saleh M, Virarkar M, Javadi S, Mathew M, Vulasala SSR, Son JB, Sun J, Bayram E, Wang X, Ma J, Szklaruk J, Bhosale P. A Feasibility Study on Deep Learning Reconstruction to Improve Image Quality With PROPELLER Acquisition in the Setting of T2-Weighted Gynecologic Pelvic Magnetic Resonance Imaging. J Comput Assist Tomogr 2023; 47:721-728. [PMID: 37707401 DOI: 10.1097/rct.0000000000001491] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
OBJECTIVES Evaluate deep learning (DL) to improve the image quality of the PROPELLER (Periodically Rotated Overlapping Parallel Lines with Enhanced Reconstruction technique) for 3 T magnetic resonance imaging of the female pelvis. METHODS Three radiologists prospectively and independently compared non-DL and DL PROPELLER sequences from 20 patients with a history of gynecologic malignancy. Sequences with different noise reduction factors (DL 25%, DL 50%, and DL 75%) were blindly reviewed and scored based on artifacts, noise, relative sharpness, and overall image quality. The generalized estimating equation method was used to assess the effect of methods on the Likert scales. Quantitatively, the contrast-to-noise ratio and signal-to-noise ratio (SNR) of the iliac muscle were calculated, and pairwise comparisons were performed based on a linear mixed model. P values were adjusted using the Dunnett method. Interobserver agreement was assessed using the κ statistic. P value was considered statistically significant at less than 0.05. RESULTS Qualitatively, DL 50 and DL 75 were ranked as the best sequences in 86% of cases. Images generated by the DL method were significantly better than non-DL images ( P < 0.0001). Iliacus muscle SNR on DL 50 and DL 75 was significantly better than non-DL images ( P < 0.0001). There was no difference in contrast-to-noise ratio between the DL and non-DL techniques in the iliac muscle. There was a high percent agreement (97.1%) in terms of DL sequences' superior image quality (97.1%) and sharpness (100%) relative to non-DL images. CONCLUSION The utilization of DL reconstruction improves the image quality of PROPELLER sequences with improved SNR quantitatively.
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Affiliation(s)
- Mohammed Saleh
- From the Department of Internal Medicine, University of Texas health Science Center at Houston, Houston, TX
| | - Mayur Virarkar
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Sanaz Javadi
- Department of Abdominal Imaging, University of Texas MD Anderson Cancer Center, Houston, TX
| | - Manoj Mathew
- Department of Radiology, Stanford University, Stanford, CA
| | | | | | - Jia Sun
- Biostatistics, University of Texas MD Anderson Cancer Center
| | - Ersin Bayram
- Global MR Applications and Workflow, GE Healthcare, Houston, TX
| | - Xinzeng Wang
- Global MR Applications and Workflow, GE Healthcare, Houston, TX
| | | | - Janio Szklaruk
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
| | - Priya Bhosale
- Department of Diagnostic Radiology, University of Florida College of Medicine, Jacksonville, FL
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17
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Li T, Wang J, Yang Y, Glide-Hurst CK, Wen N, Cai J. Multi-parametric MRI for radiotherapy simulation. Med Phys 2023; 50:5273-5293. [PMID: 36710376 PMCID: PMC10382603 DOI: 10.1002/mp.16256] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2021] [Revised: 09/10/2022] [Accepted: 12/06/2022] [Indexed: 01/31/2023] Open
Abstract
Magnetic resonance imaging (MRI) has become an important imaging modality in the field of radiotherapy (RT) in the past decade, especially with the development of various novel MRI and image-guidance techniques. In this review article, we will describe recent developments and discuss the applications of multi-parametric MRI (mpMRI) in RT simulation. In this review, mpMRI refers to a general and loose definition which includes various multi-contrast MRI techniques. Specifically, we will focus on the implementation, challenges, and future directions of mpMRI techniques for RT simulation.
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Affiliation(s)
- Tian Li
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
| | - Jihong Wang
- Department of Radiation Physics, Division of Radiation Oncology, MD Anderson Cancer Center, Houston, Texas, USA
| | - Yingli Yang
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Carri K Glide-Hurst
- Department of Radiation Oncology, University of Wisconsin, Madison, Wisconsin, USA
| | - Ning Wen
- Department of Radiology, Ruijin Hospital, Shanghai Jiaotong Univeristy School of Medicine, Shanghai, China
- SJTU-Ruijing-UIH Institute for Medical Imaging Technology, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, China
- The Global Institute of Future Technology, Shanghai Jiaotong University, Shanghai, China
| | - Jing Cai
- Department of Health Technology and Informatics, The Hong Kong Polytechnic University, Hong Kong, China
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Nakaura T, Kobayashi N, Yoshida N, Shiraishi K, Uetani H, Nagayama Y, Kidoh M, Hirai T. Update on the Use of Artificial Intelligence in Hepatobiliary MR Imaging. Magn Reson Med Sci 2023; 22:147-156. [PMID: 36697024 PMCID: PMC10086394 DOI: 10.2463/mrms.rev.2022-0102] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 11/08/2022] [Indexed: 01/26/2023] Open
Abstract
The application of machine learning (ML) and deep learning (DL) in radiology has expanded exponentially. In recent years, an extremely large number of studies have reported about the hepatobiliary domain. Its applications range from differential diagnosis to the diagnosis of tumor invasion and prediction of treatment response and prognosis. Moreover, it has been utilized to improve the image quality of DL reconstruction. However, most clinicians are not familiar with ML and DL, and previous studies about these concepts are relatively challenging to understand. In this review article, we aimed to explain the concepts behind ML and DL and to summarize recent achievements in their use in the hepatobiliary region.
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Affiliation(s)
- Takeshi Nakaura
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Naoki Kobayashi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Naofumi Yoshida
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Kaori Shiraishi
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Hiroyuki Uetani
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Yasunori Nagayama
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Masafumi Kidoh
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
| | - Toshinori Hirai
- Department of Diagnostic Radiology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Kumamoto, Japan
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19
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Hayashi N. [15. AI-assisted MRI Examination and Analysis]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:187-192. [PMID: 36804809 DOI: 10.6009/jjrt.2023-2154] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/21/2023]
Affiliation(s)
- Norio Hayashi
- School of Radiological Technology, Gunma Prefectural College of Health Sciences
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Chen Z, Pawar K, Ekanayake M, Pain C, Zhong S, Egan GF. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging-State-of-the-Art and Challenges. J Digit Imaging 2023; 36:204-230. [PMID: 36323914 PMCID: PMC9984670 DOI: 10.1007/s10278-022-00721-9] [Citation(s) in RCA: 32] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2022] [Revised: 09/09/2022] [Accepted: 10/17/2022] [Indexed: 11/06/2022] Open
Abstract
Magnetic resonance imaging (MRI) provides excellent soft-tissue contrast for clinical diagnoses and research which underpin many recent breakthroughs in medicine and biology. The post-processing of reconstructed MR images is often automated for incorporation into MRI scanners by the manufacturers and increasingly plays a critical role in the final image quality for clinical reporting and interpretation. For image enhancement and correction, the post-processing steps include noise reduction, image artefact correction, and image resolution improvements. With the recent success of deep learning in many research fields, there is great potential to apply deep learning for MR image enhancement, and recent publications have demonstrated promising results. Motivated by the rapidly growing literature in this area, in this review paper, we provide a comprehensive overview of deep learning-based methods for post-processing MR images to enhance image quality and correct image artefacts. We aim to provide researchers in MRI or other research fields, including computer vision and image processing, a literature survey of deep learning approaches for MR image enhancement. We discuss the current limitations of the application of artificial intelligence in MRI and highlight possible directions for future developments. In the era of deep learning, we highlight the importance of a critical appraisal of the explanatory information provided and the generalizability of deep learning algorithms in medical imaging.
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Affiliation(s)
- Zhaolin Chen
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia.
- Department of Data Science and AI, Monash University, Melbourne, VIC, Australia.
| | - Kamlesh Pawar
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
| | - Mevan Ekanayake
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Cameron Pain
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Department of Electrical and Computer Systems Engineering, Monash University, Melbourne, VIC, Australia
| | - Shenjun Zhong
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- National Imaging Facility, Brisbane, QLD, Australia
| | - Gary F Egan
- Monash Biomedical Imaging, Monash University, Melbourne, VIC, 3168, Australia
- Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
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21
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Cui L, Song Y, Wang Y, Wang R, Wu D, Xie H, Li J, Yang G. Motion artifact reduction for magnetic resonance imaging with deep learning and k-space analysis. PLoS One 2023; 18:e0278668. [PMID: 36603007 DOI: 10.1371/journal.pone.0278668] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2021] [Accepted: 11/22/2022] [Indexed: 01/06/2023] Open
Abstract
Motion artifacts deteriorate the quality of magnetic resonance (MR) images. This study proposes a new method to detect phase-encoding (PE) lines corrupted by motion and remove motion artifacts in MR images. 67 cases containing 8710 slices of axial T2-weighted images from the IXI public dataset were split into three datasets, i.e., training (50 cases/6500 slices), validation (5/650), and test (12/1560) sets. First, motion-corrupted k-spaces and images were simulated using a pseudo-random sampling order and random motion tracks. A convolutional neural network (CNN) model was trained to filter the motion-corrupted images. Then, the k-space of the filtered image was compared with the motion-corrupted k-space line-by-line, to detect the PE lines affected by motion. Finally, the unaffected PE lines were used to reconstruct the final image using compressed sensing (CS). For the simulated images with 35%, 40%, 45%, and 50% unaffected PE lines, the mean peak signal-to-noise ratio (PSNRs) of resulting images (mean±standard deviation) were 36.129±3.678, 38.646±3.526, 40.426±3.223, and 41.510±3.167, respectively, and the mean structural similarity (SSIMs) were 0.950±0.046, 0.964±0.035, 0.975±0.025, and 0.979±0.023, respectively. For images with more than 35% PE lines unaffected by motion, images reconstructed with proposed algorithm exhibited better quality than those images reconstructed with CS using 35% under-sampled data (PSNR 37.678±3.261, SSIM 0.964±0.028). It was proved that deep learning and k-space analysis can detect the k-space PE lines affected by motion and CS can be used to reconstruct images from unaffected data, effectively alleviating the motion artifacts.
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Affiliation(s)
- Long Cui
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yang Song
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Yida Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Rui Wang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Dongmei Wu
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Haibin Xie
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Jianqi Li
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Guang Yang
- Department of Physics, Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
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22
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Wu Y, Liu J, White GM, Deng J. Image-based motion artifact reduction on liver dynamic contrast enhanced MRI. Phys Med 2023; 105:102509. [PMID: 36565556 DOI: 10.1016/j.ejmp.2022.12.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/12/2021] [Revised: 10/13/2022] [Accepted: 12/12/2022] [Indexed: 12/24/2022] Open
Abstract
Liver MRI images often suffer from degraded quality due to ghosting or blurring artifacts caused by patient respiratory or bulk motion. In this study, we developed a two-stage deep learning model to reduce motion artifact on dynamic contrast enhanced (DCE) liver MRIs. The stage-I network utilized a deep residual network with a densely connected multi-resolution block (DRN-DCMB) network to remove most motion artifacts. The stage-II network applied the generative adversarial network (GAN) and perceptual loss compensation to preserve image structural features. The stage-I network served as the generator of GAN and its pretrained parameters in stage-I were further updated via backpropagation during stage-II training. The stage-I network was trained using small image patches with simulated motion artifacts including image-space rotational and translational motion, and K-space based centric and interleaved linear motion, sinusoidal, and rotational motion to mimic liver motion patterns. The stage-II network training used full-size images with the same types of simulated motion. The liver DCE-MRI image volumes without obvious motion artifacts in 10 patients were used for the training process, of which 1020 images of 8 patients were used for training and 240 images of 2 patients for validation. Finally, the whole two-stage deep learning model was tested with simulated motion images (312 clean images from 5 test patients) and patient images with real motion artifacts (28 motion images from 12 patients). The resulted images after two-stage processing demonstrated reduced motion artifacts while preserved anatomic details without image blurriness, with SSIM of 0.935 ± 0.092, MSE of 60.7 ± 9.0 × 10-3, and PSNR of 32.054 ± 2.219.
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Affiliation(s)
- Yunan Wu
- Department of Electrical Computer Engineering, Northwestern University, 633 Clark Street, Evanston, IL 60208, USA; Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Junchi Liu
- Medical Imaging Research Center and Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL 60616, USA.
| | - Gregory M White
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA.
| | - Jie Deng
- Department of Diagnostic Radiology, Rush University Medical Center, 1653 W. Congress Pkwy, Jelke Ste 181, Chicago, IL 60612, USA; Department of Radiation Oncology, UT Southwestern Medical Center, 2280 Inwood Rd, Dallas, TX 75235, USA.
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23
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Abstract
ABSTRACT Magnetic resonance neurography of the brachial plexus (BP) is challenging owing to its complex anatomy and technical obstacles around this anatomic region. Magnetic resonance techniques to improve image quality center around increasing nerve-to-background contrast ratio and mitigating imaging artifacts. General considerations include unilateral imaging of the BP at 3.0 T, appropriate selection and placement of surface coils, and optimization of pulse sequences. Technical considerations to improve nerve conspicuity include fat, vascular, and respiratory artifact suppression techniques; metal artifact reduction techniques; and 3-dimensional sequences. Specific optimization of these techniques for BP magnetic resonance neurography greatly improves image quality and diagnostic confidence to help guide nonoperative and operative management.
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24
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Kojima S. [[MRI] 3. Current Status of AI Image Reconstruction in Clinical MRI Systems]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2023; 79:1200-1209. [PMID: 37866905 DOI: 10.6009/jjrt.2023-2260] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Affiliation(s)
- Shinya Kojima
- Department of Medical Radiology, Faculty of Medical Technology, Teikyo University
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25
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Nepal P, Bagga B, Feng L, Chandarana H. Respiratory Motion Management in Abdominal MRI: Radiology In Training. Radiology 2023; 306:47-53. [PMID: 35997609 PMCID: PMC9792710 DOI: 10.1148/radiol.220448] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
A 96-year-old woman had a suboptimal evaluation of liver observations at abdominal MRI due to significant respiratory motion. State-of-the-art strategies to minimize respiratory motion during clinical abdominal MRI are discussed.
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Affiliation(s)
- Pankaj Nepal
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Barun Bagga
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Li Feng
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
| | - Hersh Chandarana
- From the Department of Radiology, Massachusetts General Hospital, 55
Fruit St, Boston, MA 02114 (P.N.); Department of Radiology, New York University
School of Medicine, New York, NY (B.B., H.C.); and Biomedical Engineering and
Imaging Institute and Department of Radiology, Icahn School of Medicine at Mount
Sinai, New York, NY (L.F.)
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26
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Wong PK, Chan IN, Yan HM, Gao S, Wong CH, Yan T, Yao L, Hu Y, Wang ZR, Yu HH. Deep learning based radiomics for gastrointestinal cancer diagnosis and treatment: A minireview. World J Gastroenterol 2022; 28:6363-6379. [PMID: 36533112 PMCID: PMC9753055 DOI: 10.3748/wjg.v28.i45.6363] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 10/25/2022] [Accepted: 11/16/2022] [Indexed: 12/02/2022] Open
Abstract
Gastrointestinal (GI) cancers are the major cause of cancer-related mortality globally. Medical imaging is an important auxiliary means for the diagnosis, assessment and prognostic prediction of GI cancers. Radiomics is an emerging and effective technology to decipher the encoded information within medical images, and traditional machine learning is the most commonly used tool. Recent advances in deep learning technology have further promoted the development of radiomics. In the field of GI cancer, although there are several surveys on radiomics, there is no specific review on the application of deep-learning-based radiomics (DLR). In this review, a search was conducted on Web of Science, PubMed, and Google Scholar with an emphasis on the application of DLR for GI cancers, including esophageal, gastric, liver, pancreatic, and colorectal cancers. Besides, the challenges and recommendations based on the findings of the review are comprehensively analyzed to advance DLR.
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Affiliation(s)
- Pak Kin Wong
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - In Neng Chan
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
| | - Hao-Ming Yan
- School of Clinical Medicine, China Medical University, Shenyang 110013, Liaoning Province, China
| | - Shan Gao
- Department of Gastroenterology, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang 441021, Hubei Province, China
| | - Chi Hong Wong
- Faculty of Medicine, Macau University of Science and Technology, Taipa 999078, Macau, China
| | - Tao Yan
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Liang Yao
- Department of Electromechanical Engineering, University of Macau, Taipa 999078, Macau, China
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Ying Hu
- Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, Guangdong Province, China
| | - Zhong-Ren Wang
- School of Mechanical Engineering, Hubei University of Arts and Science, Xiangyang 441053, Hubei Province, China
| | - Hon Ho Yu
- Department of Gastroenterology, Kiang Wu Hospital, Macau 999078, China
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27
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Penso M, Babbaro M, Moccia S, Guglielmo M, Carerj ML, Giacari CM, Chiesa M, Maragna R, Rabbat MG, Barison A, Martini N, Pepi M, Caiani EG, Pontone G. Cardiovascular magnetic resonance images with susceptibility artifacts: artificial intelligence with spatial-attention for ventricular volumes and mass assessment. J Cardiovasc Magn Reson 2022; 24:62. [PMID: 36437452 PMCID: PMC9703740 DOI: 10.1186/s12968-022-00899-5] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2022] [Accepted: 11/02/2022] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND Segmentation of cardiovascular magnetic resonance (CMR) images is an essential step for evaluating dimensional and functional ventricular parameters as ejection fraction (EF) but may be limited by artifacts, which represent the major challenge to automatically derive clinical information. The aim of this study is to investigate the accuracy of a deep learning (DL) approach for automatic segmentation of cardiac structures from CMR images characterized by magnetic susceptibility artifact in patient with cardiac implanted electronic devices (CIED). METHODS In this retrospective study, 230 patients (100 with CIED) who underwent clinically indicated CMR were used to developed and test a DL model. A novel convolutional neural network was proposed to extract the left ventricle (LV) and right (RV) ventricle endocardium and LV epicardium. In order to perform a successful segmentation, it is important the network learns to identify salient image regions even during local magnetic field inhomogeneities. The proposed network takes advantage from a spatial attention module to selectively process the most relevant information and focus on the structures of interest. To improve segmentation, especially for images with artifacts, multiple loss functions were minimized in unison. Segmentation results were assessed against manual tracings and commercial CMR analysis software cvi42(Circle Cardiovascular Imaging, Calgary, Alberta, Canada). An external dataset of 56 patients with CIED was used to assess model generalizability. RESULTS In the internal datasets, on image with artifacts, the median Dice coefficients for end-diastolic LV cavity, LV myocardium and RV cavity, were 0.93, 0.77 and 0.87 and 0.91, 0.82, and 0.83 in end-systole, respectively. The proposed method reached higher segmentation accuracy than commercial software, with performance comparable to expert inter-observer variability (bias ± 95%LoA): LVEF 1 ± 8% vs 3 ± 9%, RVEF - 2 ± 15% vs 3 ± 21%. In the external cohort, EF well correlated with manual tracing (intraclass correlation coefficient: LVEF 0.98, RVEF 0.93). The automatic approach was significant faster than manual segmentation in providing cardiac parameters (approximately 1.5 s vs 450 s). CONCLUSIONS Experimental results show that the proposed method reached promising performance in cardiac segmentation from CMR images with susceptibility artifacts and alleviates time consuming expert physician contour segmentation.
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Affiliation(s)
- Marco Penso
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Mario Babbaro
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Sara Moccia
- The BioRobotics Institute and Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna, Pisa, Italy
| | - Marco Guglielmo
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Maria Ludovica Carerj
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Biomedical Sciences and Morphological and Functional Imaging, “G. Martino” University Hospital Messina, Messina, Italy
| | - Carlo Maria Giacari
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Mattia Chiesa
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
| | - Riccardo Maragna
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Mark G. Rabbat
- Loyola University of Chicago, Chicago, IL USA
- Edward Hines Jr. VA Hospital, Hines, IL USA
| | | | | | - Mauro Pepi
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
| | - Enrico G. Caiani
- Department of Electronics, Information and Biomedical Engineering, Politecnico di Milano, Milan, Italy
- Istituto di Elettronica e di Ingegneria dell’Informazione e delle Telecomunicazioni, Consiglio Nazionale delle Ricerche, Milan, Italy
| | - Gianluca Pontone
- Cardiovascular Imaging Department, Centro Cardiologico Monzino IRCCS, Via C. Parea 4, 20138 Milan, Italy
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28
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Nárai Á, Hermann P, Auer T, Kemenczky P, Szalma J, Homolya I, Somogyi E, Vakli P, Weiss B, Vidnyánszky Z. Movement-related artefacts (MR-ART) dataset of matched motion-corrupted and clean structural MRI brain scans. Sci Data 2022; 9:630. [PMID: 36253426 PMCID: PMC9576686 DOI: 10.1038/s41597-022-01694-8] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2022] [Accepted: 09/12/2022] [Indexed: 11/10/2022] Open
Abstract
Magnetic Resonance Imaging (MRI) provides a unique opportunity to investigate neural changes in healthy and clinical conditions. Its large inherent susceptibility to motion, however, often confounds the measurement. Approaches assessing, correcting, or preventing motion corruption of MRI measurements are under active development, and such efforts can greatly benefit from carefully controlled datasets. We present a unique dataset of structural brain MRI images collected from 148 healthy adults which includes both motion-free and motion-affected data acquired from the same participants. This matched dataset allows direct evaluation of motion artefacts, their impact on derived data, and testing approaches to correct for them. Our dataset further stands out by containing images with different levels of motion artefacts from the same participants, is enriched with expert scoring characterizing the image quality from a clinical point of view and is also complemented with standard image quality metrics obtained from MRIQC. The goal of the dataset is to raise awareness of the issue and provide a useful resource to assess and improve current motion correction approaches.
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Grants
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- GINOP-2.2.1-18-2018-00001 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
- 2017-1.2.1-NKP-2017-00002 Nemzeti Kutatási, Fejlesztési és Innovációs Hivatal (NKFI Office)
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Affiliation(s)
- Ádám Nárai
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
| | - Petra Hermann
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Tibor Auer
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
- School of Psychology, University of Surrey, Guildford, United Kingdom
| | - Péter Kemenczky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - János Szalma
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - István Homolya
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Eszter Somogyi
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Pál Vakli
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Béla Weiss
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary
| | - Zoltán Vidnyánszky
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, 1117, Hungary.
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29
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Li F, Xu W, Feng Y, Wang W, Tian H, He S, Li L, Xiang B, Wang Y. Preparation of ultrasound contrast agents: The exploration of the structure-echogenicity relationship of contrast agents based on neural network model. Front Oncol 2022; 12:964314. [PMID: 36276089 PMCID: PMC9581267 DOI: 10.3389/fonc.2022.964314] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2022] [Accepted: 08/09/2022] [Indexed: 11/23/2022] Open
Abstract
There is a need to standardize the process of micro/nanobubble preparation to bring it closer to clinical translation. We explored a neural network-based model to predict the structure-echogenicity relationship for the preparation and fabrication of ultrasound-enhanced contrast agents. Seven formulations were screened, and 109 measurements were obtained. An artificial neural network-multilayer perceptron (ANN-MLP) model was used. The original data were divided into the training and testing groups, which included 73 and 36 groups of data, respectively. The hidden layer was selected from three hidden layers and included bias. The classification graph showed that the predicted values of the training and testing groups were 76.7% and 66.7%, respectively. According to the receiver operating characteristic curve, the accuracy of different imaging effects could achieve a prediction rate of 88.1–96.5%. The percentage graph showed that the data were gradually converging. The predictive analysis curves of different ultrasound effects gradually approached stable value of Gain. Normalized importance predicted contributions for the Pk1, poly-dispersity index (PDI), and intensity account were 100%, 98.5%, and 89.7%, respectively. The application of the ANN-MLP model is feasible and effective for the exploration of the synthesis process of ultrasound contrast agents. 1,2-Distearoyl-sn-glycero-3 phosphoethanolamine-N (methoxy[polyethylene glycol]-2000) (DSPE PEG-2000) correlated highly with the success rate of contrast agent synthesis.
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Affiliation(s)
- Feng Li
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Wensheng Xu
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Yujin Feng
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Wengang Wang
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Hui Tian
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Suhuan He
- The First Outpatient Department of Hebei Province, Shijiazhuang, Hebei, China
| | - Liang Li
- Department of Integrated Traditional Chinese and Western Medicine, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
| | - Bai Xiang
- Department of Pharmaceutics, School of Pharmaceutical Sciences, Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Yueheng Wang, ; Bai Xiang,
| | - Yueheng Wang
- Department of Ultrasound, The Second Hospital of Hebei Medical University, Shijiazhuang, Hebei, China
- *Correspondence: Yueheng Wang, ; Bai Xiang,
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Zhou L, Liu H, Zou YX, Zhang G, Su B, Lu L, Chen YC, Yin X, Jiang HB. Clinical validation of an AI-based motion correction reconstruction algorithm in cerebral CT. Eur Radiol 2022; 32:8550-8559. [PMID: 35678857 DOI: 10.1007/s00330-022-08883-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/25/2022] [Accepted: 05/13/2022] [Indexed: 11/04/2022]
Abstract
OBJECTIVES To evaluate the clinical performance of an artificial intelligence (AI)-based motion correction (MC) reconstruction algorithm for cerebral CT. METHODS A total of 53 cases, where motion artifacts were found in the first scan so that an immediate rescan was taken, were retrospectively enrolled. While the rescanned images were reconstructed with a hybrid iterative reconstruction (IR) algorithm (reference group), images of the first scan were reconstructed with both the hybrid IR (motion group) and the MC algorithm (MC group). Image quality was compared in terms of standard deviation (SD), signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), the mean squared error (MSE), peak signal-to-noise ratio (PSNR), structural similarity index (SSIM), and mutual information (MI), as well as subjective scores. The diagnostic performance for each case was evaluated accordingly by lesion detectability or the Alberta Stroke Program Early CT Score (ASPECTS) assessment. RESULTS Compared with the motion group, the SNR and CNR of the MC group were significantly increased. The MSE, PSNR, SSIM, and MI with respect to the reference group were improved by 44.1%, 15.8%, 7.4%, and 18.3%, respectively (all p < 0.001). Subjective image quality indicators were scored higher for the MC than the motion group (p < 0.05). Improved lesion detectability and higher AUC (0.817 vs 0.614) in the ASPECTS assessment were found for the MC to the motion group. CONCLUSIONS The AI-based MC reconstruction algorithm has been clinically validated for reducing motion artifacts and improving diagnostic performance of cerebral CT. KEY POINTS • An artificial intelligence-based motion correction (MC) reconstruction algorithm has been clinically validated in both qualitative and quantitative manner. • The MC algorithm reduces motion artifacts in cerebral CT and increases the diagnostic confidence for brain lesions. • The MC algorithm can help avoiding rescans caused by motion and improving the efficiency of cerebral CT in the emergency department.
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Affiliation(s)
- Leilei Zhou
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Hao Liu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yi-Xuan Zou
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Guozhi Zhang
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Bin Su
- United Imaging Healthcare Co., Ltd., Shanghai, China
| | - Liyan Lu
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Yu-Chen Chen
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China.
| | - Xindao Yin
- Department of Radiology, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China
| | - Hong-Bing Jiang
- Department of Medical Equipment, Nanjing First Hospital, Nanjing Medical University, No.68, Changle Road, Nanjing, 210006, China. .,Nanjing Emergency Medical Center, No. 3 Zizhulin, Nanjing, 210003, China.
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Ren Q, Zhu P, Li C, Yan M, Liu S, Zheng C, Xia X. Pretreatment Computed Tomography-Based Machine Learning Models to Predict Outcomes in Hepatocellular Carcinoma Patients who Received Combined Treatment of Trans-Arterial Chemoembolization and Tyrosine Kinase Inhibitor. Front Bioeng Biotechnol 2022; 10:872044. [PMID: 35677305 PMCID: PMC9168370 DOI: 10.3389/fbioe.2022.872044] [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: 02/09/2022] [Accepted: 04/22/2022] [Indexed: 11/15/2022] Open
Abstract
Aim: Trans-arterial chemoembolization (TACE) in combination with tyrosine kinase inhibitor (TKI) has been evidenced to improve outcomes in a portion of patients with hepatocellular carcinoma (HCC). Developing biomarkers to identify patients who might benefit from the combined treatment is needed. This study aims to investigate the efficacy of radiomics/deep learning features-based models in predicting short-term disease control and overall survival (OS) in HCC patients who received the combined treatment. Materials and Methods: A total of 103 HCC patients who received the combined treatment from Sep. 2015 to Dec. 2019 were enrolled in the study. We exacted radiomics features and deep learning features of six pre-trained convolutional neural networks (CNNs) from pretreatment computed tomography (CT) images. The robustness of features was evaluated, and those with excellent stability were used to construct predictive models by combining each of the seven feature exactors, 13 feature selection methods and 12 classifiers. The models were evaluated for predicting short-term disease by using the area under the receiver operating characteristics curve (AUC) and relative standard deviation (RSD). The optimal models were further analyzed for predictive performance on overall survival. Results: A total of the 1,092 models (156 with radiomics features and 936 with deep learning features) were constructed. Radiomics_GINI_Nearest Neighbors (RGNN) and Resnet50_MIM_Nearest Neighbors (RMNN) were identified as optimal models, with the AUC of 0.87 and 0.94, accuracy of 0.89 and 0.92, sensitivity of 0.88 and 0.97, specificity of 0.90 and 0.90, precision of 0.87 and 0.83, F1 score of 0.89 and 0.92, and RSD of 1.30 and 0.26, respectively. Kaplan-Meier survival analysis showed that RGNN and RMNN were associated with better OS (p = 0.006 for RGNN and p = 0.033 for RMNN). Conclusion: Pretreatment CT-based radiomics/deep learning models could non-invasively and efficiently predict outcomes in HCC patients who received combined therapy of TACE and TKI.
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Affiliation(s)
- Qianqian Ren
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Peng Zhu
- Department of Hepatobiliary Surgery, Wuhan No.1 Hospital, Wuhan, China
| | - Changde Li
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Meijun Yan
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Song Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Chuansheng Zheng
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
| | - Xiangwen Xia
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, China
- *Correspondence: Xiangwen Xia,
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Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography. J Comput Assist Tomogr 2022; 46:593-603. [PMID: 35617647 DOI: 10.1097/rct.0000000000001326] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
PURPOSE This study aimed to evaluate the feasibility of a deep learning method for imaging artifact and noise reduction in coronal reformation of contrast-enhanced chest computed tomography (CT). METHODS A total of 19,052 coronal reformatted chest CT images of 110 CT image sets (55 pairs of concordant 16- and 320-row CT image sets) were included and used to train a deep learning algorithm for artifact and noise correction. For internal validation, 4093 coronal reformatted CT images of 25 patients from 16-row CT images underwent correction processing. For external validation, chest CT images of 30 patients (1028 coronal reformatted CT images), acquired in other institutions using different scanners, were subjected to correction processing. For both validations, image quality was compared between original ("CTorigin") and deep learning-based corrected ("CTcorrect") CT images. Quantitative analysis for stair-step artifact (coefficient of variance of CT density on coronal reformation), image noise, signal-to-noise ratio, and contrast-to-noise ratio were evaluated. Subjective image quality scores were assigned for image contrast, artifact, and conspicuity of major structures. RESULTS CTcorrect showed significantly reduced stair-step artifact (mean coefficient of variance: CTorigin 7.35 ± 2.0 vs CTcorrect 5.17 ± 2.4, P < 0.001) and image noise and improved signal-to-noise ratio and contrast-to-noise ratio in the aorta, pulmonary artery, and liver, compared with those of CTorigin (P < 0.01). On subjective analysis, CTcorrect had higher image contrast, lower artifact, and better conspicuity than CTorigin. Most results of the external validation were consistent with those obtained from the internal validation, except for those concerning the pulmonary artery. CONCLUSIONS Deep learning-based artifact correction significantly improved the image quality of coronal reformation chest CT by reducing image noise and artifacts.
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Sombeck JT, Heye J, Kumaravelu K, Goetz SM, Peterchev AV, Grill WM, Bensmaia S, Miller LE. Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array. J Neural Eng 2022; 19:10.1088/1741-2552/ac63e8. [PMID: 35378515 PMCID: PMC9142773 DOI: 10.1088/1741-2552/ac63e8] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 04/04/2022] [Indexed: 11/12/2022]
Abstract
Objective.Persons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. ICMS that mimics naturally occurring neural activity, may allow afferent interfaces that are more informative and easier to learn than stimulation evoking unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS.Approach.Using a Utah multi-electrode array, we recorded activity evoked by both single pulses and trains of ICMS at a wide range of amplitudes and frequencies in two rhesus macaques. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings.Main results.After single pulses of stimulation, we recorded what was likely transsynaptically-evoked activity even on the stimulated electrode as early as ∼0.7 ms. This was immediately followed by suppressed neural activity lasting 10-150 ms. After trains, this long-lasting inhibition was replaced by increased firing rates for ∼100 ms. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels.Significance.The detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.
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Affiliation(s)
- Joseph T Sombeck
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
| | - Juliet Heye
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
| | - Karthik Kumaravelu
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
| | - Stefan M Goetz
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
- Department of Engineering, University of Cambridge, Cambridge, United Kingdom
| | - Angel V Peterchev
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
| | - Warren M Grill
- Department of Biomedical Engineering, Duke University, Durham, NC, United States of America
- Department of Electrical and Computer Engineering, Duke University, Durham, NC, United States of America
- Department of Neurobiology, Duke University, Durham, NC, United States of America
- Department of Neurosurgery, Duke University, Durham, NC, United States of America
- Duke Institute for Brain Sciences, Duke University, Durham, NC, United States of America
| | - Sliman Bensmaia
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, United States of America
- Committee on Computational Neuroscience, University of Chicago, Chicago, IL, United States of America
- Neuroscience Institute, University of Chicago, Chicago, IL, United States of America
| | - Lee E Miller
- Department of Biomedical Engineering, Northwestern University, Evanston, IL, United States of America
- Department of Neuroscience, Northwestern University, Chicago, IL, United States of America
- Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States of America
- Shirley Ryan AbilityLab, Chicago, IL, United States of America
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Artificial intelligence in gastrointestinal and hepatic imaging: past, present and future scopes. Clin Imaging 2022; 87:43-53. [DOI: 10.1016/j.clinimag.2022.04.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2021] [Revised: 03/09/2022] [Accepted: 04/11/2022] [Indexed: 11/19/2022]
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Deep learning reconstruction for 1.5 T cervical spine MRI: effect on interobserver agreement in the evaluation of degenerative changes. Eur Radiol 2022; 32:6118-6125. [DOI: 10.1007/s00330-022-08729-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2021] [Revised: 02/23/2022] [Accepted: 03/07/2022] [Indexed: 12/22/2022]
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Omari EA, Zhang Y, Ahunbay E, Paulson E, Amjad A, Chen X, Liang Y, Li XA. Multi parametric magnetic resonance imaging for radiation treatment planning. Med Phys 2022; 49:2836-2845. [PMID: 35170769 DOI: 10.1002/mp.15534] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2021] [Revised: 10/05/2021] [Accepted: 01/03/2022] [Indexed: 11/09/2022] Open
Abstract
In recent years, multi-parametric magnetic resonance imaging (MpMRI) has played a major role in radiation therapy treatment planning. The superior soft tissue contrast, functional or physiological imaging capabilities and the flexibility of site-specific image sequence development has placed MpMRI at the forefront. In this article, the present status of MpMRI for external beam radiation therapy planning is reviewed. Common MpMRI sequences, preprocessing and QA strategies are briefly discussed, and various image registration techniques and strategies are addressed. Image segmentation methods including automatic segmentation and deep learning techniques for organs at risk and target delineation are reviewed. Due to the advancement in MRI guided online adaptive radiotherapy, treatment planning considerations addressing MRI only planning are also discussed. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Eenas A Omari
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Zhang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ergun Ahunbay
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Eric Paulson
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Asma Amjad
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Xinfeng Chen
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - Ying Liang
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
| | - X Allen Li
- Department of Radiation Oncology, Medical College of Wisconsin, Milwaukee, WI, 53226, USA
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Manso Jimeno M, Ravi KS, Jin Z, Oyekunle D, Ogbole G, Geethanath S. ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magn Reson Imaging 2022; 89:42-48. [PMID: 35176447 DOI: 10.1016/j.mri.2022.02.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/14/2023]
Abstract
Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets: 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists' labels returned Cohen's kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.
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Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Keerthi Sravan Ravi
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Zhezhen Jin
- Mailman School of Public Health, Columbia University in the City of New York, New York, NY 10027, USA
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Godwin Ogbole
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Sairam Geethanath
- Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA.
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Pirkl CM, Cencini M, Kurzawski JW, Waldmannstetter D, Li H, Sekuboyina A, Endt S, Peretti L, Donatelli G, Pasquariello R, Costagli M, Buonincontri G, Tosetti M, Menzel MI, Menze BH. Learning residual motion correction for fast and robust 3D multiparametric MRI. Med Image Anal 2022; 77:102387. [DOI: 10.1016/j.media.2022.102387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2021] [Revised: 11/25/2021] [Accepted: 02/01/2022] [Indexed: 11/28/2022]
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Sagawa H. [11. Deep Learning in Magnetic Resonance Imaging: An Overview and Applications]. Nihon Hoshasen Gijutsu Gakkai Zasshi 2022; 78:876-881. [PMID: 35989257 DOI: 10.6009/jjrt.2022-2069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Affiliation(s)
- Hajime Sagawa
- Clinical Radiology Service, Kyoto University Hospital
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Sagawa H, Itagaki K, Matsushita T, Miyati T. Evaluation of motion artifacts in brain magnetic resonance images using convolutional neural network-based prediction of full-reference image quality assessment metrics. J Med Imaging (Bellingham) 2022; 9:015502. [PMID: 35106324 PMCID: PMC8782596 DOI: 10.1117/1.jmi.9.1.015502] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Accepted: 01/03/2022] [Indexed: 01/23/2023] Open
Abstract
Purpose: Motion artifacts in magnetic resonance (MR) images mostly undergo subjective evaluation, which is poorly reproducible, time consuming, and costly. Recently, full-reference image quality assessment (FR-IQA) metrics, such as structural similarity (SSIM), have been used, but they require a reference image and hence cannot be used to evaluate clinical images. We developed a convolutional neural network (CNN) model to quantify motion artifacts without using reference images. Approach: The brain MR images were obtained from an open dataset. The motion-corrupted images were generated retrospectively, and the peak signal-to-noise ratio, cross-correlation coefficient, and SSIM were calculated. The CNN was trained using these images and their FR-IQA metrics to predict the FR-IQA metrics without reference images. Receiver operating characteristic (ROC) curves were created for binary classification, with artifact scores < 4 indicating the need for rescanning. ROC curve analysis was performed on the binary classification of the real motion images. Results: The predicted FR-IQA metric having the highest correlation with the subjective evaluation was SSIM, which was able to classify images requiring rescanning with a sensitivity of 89.5%, specificity of 78.2%, and area under the ROC curve (AUC) of 0.930. The real motion artifacts were classified with the AUC of 0.928. Conclusions: Our CNN model predicts FR-IQA metrics with high accuracy, which enables quantitative assessment of motion artifacts in MR images without reference images. It enables classification of images requiring rescanning with a high AUC, which can improve the workflow of MR imaging examinations.
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Affiliation(s)
- Hajime Sagawa
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Address all correspondence to Hajime Sagawa,
| | - Koji Itagaki
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan
| | - Tatsuhiko Matsushita
- Kyoto University Hospital, Division of Clinical Radiology Service, Kyoto, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
| | - Tosiaki Miyati
- Kanazawa University, Graduate School of Medical Sciences, Division of Health Sciences, Kanazawa, Japan,Kanazawa University, Pharmaceutical and Health Sciences, Institute of Medical, Faculty of Health Sciences, Kanazawa, Japan
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Moummad I, Jaudet C, Lechervy A, Valable S, Raboutet C, Soilihi Z, Thariat J, Falzone N, Lacroix J, Batalla A, Corroyer-Dulmont A. The Impact of Resampling and Denoising Deep Learning Algorithms on Radiomics in Brain Metastases MRI. Cancers (Basel) 2021; 14:cancers14010036. [PMID: 35008198 PMCID: PMC8750741 DOI: 10.3390/cancers14010036] [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] [Received: 09/29/2021] [Revised: 12/14/2021] [Accepted: 12/18/2021] [Indexed: 01/06/2023] Open
Abstract
Simple Summary Due to the central role of magnetic resonance Imaging (MRI) in the management of patients with cancer, waiting lists exceed clinically relevant delays. For this reason, many research groups and MRI manufacturers develop algorithms as resampling and denoising models to allow faster acquisition time without deterioration in image quality. Whereas these algorithms are available in all new MRI, it is not clear how they will impact image features as well as the validity of statistical model of radiomics which use deep images characteristics to predict treatment outcome. The aim of this study was to develop resampling and denoising deep learning (DL) models and evaluate their impact on radiomics from post-Gd-T1w-MRI brain images with brain metastases. We show that resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast acquisition loses most of the radiomic-features and invalidates predictive radiomic models, DL models restore these parameters. Abstract Background: Magnetic resonance imaging (MRI) is predominant in the therapeutic management of cancer patients, unfortunately, patients have to wait a long time to get an appointment for examination. Therefore, new MRI devices include deep-learning (DL) solutions to save acquisition time. However, the impact of these algorithms on intensity and texture parameters has been poorly studied. The aim of this study was to evaluate the impact of resampling and denoising DL models on radiomics. Methods: Resampling and denoising DL model was developed on 14,243 T1 brain images from 1.5T-MRI. Radiomics were extracted from 40 brain metastases from 11 patients (2049 images). A total of 104 texture features of DL images were compared to original images with paired t-test, Pearson correlation and concordance-correlation-coefficient (CCC). Results: When two times shorter image acquisition shows strong disparities with the originals concerning the radiomics, with significant differences and loss of correlation of 79.81% and 48.08%, respectively. Interestingly, DL models restore textures with 46.15% of unstable parameters and 25.96% of low CCC and without difference for the first-order intensity parameters. Conclusions: Resampling and denoising DL models reconstruct low resolution and noised MRI images acquired quickly into high quality images. While fast MRI acquisition loses most of the radiomic features, DL models restore these parameters.
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Affiliation(s)
- Ilyass Moummad
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Cyril Jaudet
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Alexis Lechervy
- UMR GREYC, Normandie University, UNICAEN, ENSICAEN, CNRS, 14000 Caen, France;
| | - Samuel Valable
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
| | - Charlotte Raboutet
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Zamila Soilihi
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Juliette Thariat
- Radiotherapy Department, CLCC François Baclesse, 14000 Caen, France;
| | - Nadia Falzone
- GenesisCare Theranostics, Building 1 & 11, The Mill, 41-43 Bourke Road, Alexandria, NSW 2015, Australia;
| | - Joëlle Lacroix
- Radiology Department, CLCC François Baclesse, 14000 Caen, France; (C.R.); (J.L.)
| | - Alain Batalla
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
| | - Aurélien Corroyer-Dulmont
- Medical Physics Department, CLCC François Baclesse, 14000 Caen, France; (I.M.); (C.J.); (Z.S.); (A.B.)
- ISTCT/CERVOxy Group, Normandie University, UNICAEN, CEA, CNRS, 14000 Caen, France;
- Correspondence:
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Eldeniz C, Gan W, Chen S, Fraum TJ, Ludwig DR, Yan Y, Liu J, Vahle T, Krishnamurthy U, Kamilov US, An H. Phase2Phase: Respiratory Motion-Resolved Reconstruction of Free-Breathing Magnetic Resonance Imaging Using Deep Learning Without a Ground Truth for Improved Liver Imaging. Invest Radiol 2021; 56:809-819. [PMID: 34038064 DOI: 10.1097/rli.0000000000000792] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVES Respiratory binning of free-breathing magnetic resonance imaging data reduces motion blurring; however, it exacerbates noise and introduces severe artifacts due to undersampling. Deep neural networks can remove artifacts and noise but usually require high-quality ground truth images for training. This study aimed to develop a network that can be trained without this requirement. MATERIALS AND METHODS This retrospective study was conducted on 33 participants enrolled between November 2016 and June 2019. Free-breathing magnetic resonance imaging was performed using a radial acquisition. Self-navigation was used to bin the k-space data into 10 respiratory phases. To simulate short acquisitions, subsets of radial spokes were used in reconstructing images with multicoil nonuniform fast Fourier transform (MCNUFFT), compressed sensing (CS), and 2 deep learning methods: UNet3DPhase and Phase2Phase (P2P). UNet3DPhase was trained using a high-quality ground truth, whereas P2P was trained using noisy images with streaking artifacts. Two radiologists blinded to the reconstruction methods independently reviewed the sharpness, contrast, and artifact-freeness of the end-expiration images reconstructed from data collected at 16% of the Nyquist sampling rate. The generalized estimating equation method was used for statistical comparison. Motion vector fields were derived to examine the respiratory motion range of 4-dimensional images reconstructed using different methods. RESULTS A total of 15 healthy participants and 18 patients with hepatic malignancy (50 ± 15 years, 6 women) were enrolled. Both reviewers found that the UNet3DPhase and P2P images had higher contrast (P < 0.01) and fewer artifacts (P < 0.01) than the CS images. The UNet3DPhase and P2P images were reported to be sharper than the CS images by 1 reviewer (P < 0.01) but not by the other reviewer (P = 0.22, P = 0.18). UNet3DPhase and P2P were similar in sharpness and contrast, whereas UNet3DPhase had fewer artifacts (P < 0.01). The motion vector lengths for the MCNUFFT800 and P2P800 images were comparable (10.5 ± 4.2 mm and 9.9 ± 4.0 mm, respectively), whereas both were significantly larger than CS2000 (7.0 ± 3.9 mm; P < 0.0001) and UNnet3DPhase800 (6.9 ± 3.2; P < 0.0001) images. CONCLUSIONS Without a ground truth, P2P can reconstruct sharp, artifact-free, and high-contrast respiratory motion-resolved images from highly undersampled data. Unlike the CS and UNet3DPhase methods, P2P did not artificially reduce the respiratory motion range.
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Affiliation(s)
| | - Weijie Gan
- Department of Computer Science & Engineering
| | | | | | | | | | - Jiaming Liu
- Department of Electrical and System Engineering, Washington University in St. Louis, Missouri
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Yao S, Ye Z, Wei Y, Jiang HY, Song B. Radiomics in hepatocellular carcinoma: A state-of-the-art review. World J Gastrointest Oncol 2021; 13:1599-1615. [PMID: 34853638 PMCID: PMC8603458 DOI: 10.4251/wjgo.v13.i11.1599] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 04/22/2021] [Accepted: 08/20/2021] [Indexed: 02/06/2023] Open
Abstract
Hepatocellular carcinoma (HCC) is the most common cancer and the second major contributor to cancer-related mortality. Radiomics, a burgeoning technology that can provide invisible high-dimensional quantitative and mineable data derived from routine-acquired images, has enormous potential for HCC management from diagnosis to prognosis as well as providing contributions to the rapidly developing deep learning methodology. This article aims to review the radiomics approach and its current state-of-the-art clinical application scenario in HCC. The limitations, challenges, and thoughts on future directions are also summarized.
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Affiliation(s)
- Shan Yao
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Zheng Ye
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Yi Wei
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Han-Yu Jiang
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
| | - Bin Song
- Department of Radiology, West China Hospital, Sichuan University, Chengdu 610041, Sichuan Province, China
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Hu R, Yang R, Liu Y, Li X. Simulation and Mitigation of the Wrap-Around Artifact in the MRI Image. Front Comput Neurosci 2021; 15:746549. [PMID: 34744675 PMCID: PMC8566355 DOI: 10.3389/fncom.2021.746549] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Accepted: 09/15/2021] [Indexed: 11/13/2022] Open
Abstract
Magnetic resonance imaging (MRI) is an essential clinical imaging modality for diagnosis and medical research, while various artifacts occur during the acquisition of MRI image, resulting in severe degradation of the perceptual quality and diagnostic efficacy. To tackle such challenges, this study deals with one of the most frequent artifact sources, namely the wrap-around artifact. In particular, given that the MRI data are limited and difficult to access, we first propose a method to simulate the wrap-around artifact on the artifact-free MRI image to increase the quantity of MRI data. Then, an image restoration technique, based on the deep neural networks, is proposed for wrap-around artifact reduction and overall perceptual quality improvement. This study presents a comprehensive analysis regarding both the occurrence of and reduction in the wrap-around artifact, with the aim of facilitating the detection and mitigation of MRI artifacts in clinical situations.
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Affiliation(s)
- Runze Hu
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Rui Yang
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
| | - Yutao Liu
- School of Computer Science and Technology, Ocean University of China, Qingdao, China
| | - Xiu Li
- Department of Information Science and Technology, Tsinghua Shenzhen International Graduate School, Tsinghua University, Shenzhen, China
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Oh G, Lee JE, Ye JC. Unpaired MR Motion Artifact Deep Learning Using Outlier-Rejecting Bootstrap Aggregation. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:3125-3139. [PMID: 34133276 DOI: 10.1109/tmi.2021.3089708] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
Recently, deep learning approaches for MR motion artifact correction have been extensively studied. Although these approaches have shown high performance and lower computational complexity compared to classical methods, most of them require supervised training using paired artifact-free and artifact-corrupted images, which may prohibit its use in many important clinical applications. For example, transient severe motion (TSM) due to acute transient dyspnea in Gd-EOB-DTPA-enhanced MR is difficult to control and model for paired data generation. To address this issue, here we propose a novel unpaired deep learning scheme that does not require matched motion-free and motion artifact images. Specifically, the first step of our method is k -space random subsampling along the phase encoding direction that can remove some outliers probabilistically. In the second step, the neural network reconstructs fully sampled resolution image from a downsampled k -space data, and motion artifacts can be reduced in this step. Last, the aggregation step through averaging can further improve the results from the reconstruction network. We verify that our method can be applied for artifact correction from simulated motion as well as real motion from TSM successfully from both single and multi-coil data with and without k -space raw data, outperforming existing state-of-the-art deep learning methods.
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Hill CE, Biasiolli L, Robson MD, Grau V, Pavlides M. Emerging artificial intelligence applications in liver magnetic resonance imaging. World J Gastroenterol 2021; 27:6825-6843. [PMID: 34790009 PMCID: PMC8567471 DOI: 10.3748/wjg.v27.i40.6825] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Revised: 04/16/2021] [Accepted: 09/30/2021] [Indexed: 02/06/2023] Open
Abstract
Chronic liver diseases (CLDs) are becoming increasingly more prevalent in modern society. The use of imaging techniques for early detection, such as magnetic resonance imaging (MRI), is crucial in reducing the impact of these diseases on healthcare systems. Artificial intelligence (AI) algorithms have been shown over the past decade to excel at image-based analysis tasks such as detection and segmentation. When applied to liver MRI, they have the potential to improve clinical decision making, and increase throughput by automating analyses. With Liver diseases becoming more prevalent in society, the need to implement these techniques to utilize liver MRI to its full potential, is paramount. In this review, we report on the current methods and applications of AI methods in liver MRI, with a focus on machine learning and deep learning methods. We assess four main themes of segmentation, classification, image synthesis and artefact detection, and their respective potential in liver MRI and the wider clinic. We provide a brief explanation of some of the algorithms used and explore the current challenges affecting the field. Though there are many hurdles to overcome in implementing AI methods in the clinic, we conclude that AI methods have the potential to positively aid healthcare professionals for years to come.
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Affiliation(s)
- Charles E Hill
- Department of Engineering Science, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Luca Biasiolli
- Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
| | | | - Vicente Grau
- Department of Engineering, University of Oxford, Oxford OX3 7DQ, United Kingdom
| | - Michael Pavlides
- Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, Radcliffe Department of Medicine, University of Oxford, Oxford OX3 9DU, United Kingdom
- Translational Gastroenterology Unit, University of Oxford, Oxford OX3 9DU, United Kingdom
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford OX3 9DU, United Kingdom
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Abdi M, Feng X, Sun C, Bilchick KC, Meyer CH, Epstein FH. Suppression of artifact-generating echoes in cine DENSE using deep learning. Magn Reson Med 2021; 86:2095-2104. [PMID: 34021628 PMCID: PMC8295221 DOI: 10.1002/mrm.28832] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 03/21/2021] [Accepted: 04/17/2021] [Indexed: 11/11/2022]
Abstract
PURPOSE To use deep learning for suppression of the artifact-generating T1 -relaxation echo in cine displacement encoding with stimulated echoes (DENSE) for the purpose of reducing the scan time. METHODS A U-Net was trained to suppress the artifact-generating T1 -relaxation echo using complementary phase-cycled data as the ground truth. A data-augmentation method was developed that generates synthetic DENSE images with arbitrary displacement-encoding frequencies to suppress the T1 -relaxation echo modulated for a range of frequencies. The resulting U-Net (DAS-Net) was compared with k-space zero-filling as an alternative method. Non-phase-cycled DENSE images acquired in shorter breath-holds were processed by DAS-Net and compared with DENSE images acquired with phase cycling for the quantification of myocardial strain. RESULTS The DAS-Net method effectively suppressed the T1 -relaxation echo and its artifacts, and achieved root Mean Square(RMS) error = 5.5 ± 0.8 and structural similarity index = 0.85 ± 0.02 for DENSE images acquired with a displacement encoding frequency of 0.10 cycles/mm. The DAS-Net method outperformed zero-filling (root Mean Square error = 5.8 ± 1.5 vs 13.5 ± 1.5, DAS-Net vs zero-filling, P < .01; and structural similarity index = 0.83 ± 0.04 vs 0.66 ± 0.03, DAS-Net vs zero-filling, P < .01). Strain data for non-phase-cycled DENSE images with DAS-Net showed close agreement with strain from phase-cycled DENSE. CONCLUSION The DAS-Net method provides an effective alternative approach for suppression of the artifact-generating T1 -relaxation echo in DENSE MRI, enabling a 42% reduction in scan time compared to DENSE with phase-cycling.
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Affiliation(s)
- Mohamad Abdi
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Xue Feng
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Changyu Sun
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
| | - Kenneth C. Bilchick
- Department of Medicine, University of Virginia Health System, Charlottesville, Virginia
| | - Craig H. Meyer
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
- Departments of Radiology, University of Virginia Health System, Charlottesville, Virginia
| | - Frederick H. Epstein
- Departments of Biomedical Engineering, University of Virginia, Charlottesville, Virginia
- Departments of Radiology, University of Virginia Health System, Charlottesville, Virginia
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Li GY, Wang CY, Lv J. Current status of deep learning in abdominal image reconstruction. Artif Intell Med Imaging 2021; 2:86-94. [DOI: 10.35711/aimi.v2.i4.86] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2021] [Revised: 06/24/2021] [Accepted: 08/17/2021] [Indexed: 02/06/2023] Open
Affiliation(s)
- Guang-Yuan Li
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
| | - Cheng-Yan Wang
- Human Phenome Institute, Fudan University, Shanghai 201203, China
| | - Jun Lv
- School of Computer and Control Engineering, Yantai University, Yantai 264000, Shandong Province, China
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50
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Lyu Q, Shan H, Xie Y, Kwan AC, Otaki Y, Kuronuma K, Li D, Wang G. Cine Cardiac MRI Motion Artifact Reduction Using a Recurrent Neural Network. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2170-2181. [PMID: 33856986 PMCID: PMC8376223 DOI: 10.1109/tmi.2021.3073381] [Citation(s) in RCA: 24] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/17/2023]
Abstract
Cine cardiac magnetic resonance imaging (MRI) is widely used for the diagnosis of cardiac diseases thanks to its ability to present cardiovascular features in excellent contrast. As compared to computed tomography (CT), MRI, however, requires a long scan time, which inevitably induces motion artifacts and causes patients' discomfort. Thus, there has been a strong clinical motivation to develop techniques to reduce both the scan time and motion artifacts. Given its successful applications in other medical imaging tasks such as MRI super-resolution and CT metal artifact reduction, deep learning is a promising approach for cardiac MRI motion artifact reduction. In this paper, we propose a novel recurrent generative adversarial network model for cardiac MRI motion artifact reduction. This model utilizes bi-directional convolutional long short-term memory (ConvLSTM) and multi-scale convolutions to improve the performance of the proposed network, in which bi-directional ConvLSTMs handle long-range temporal features while multi-scale convolutions gather both local and global features. We demonstrate a decent generalizability of the proposed method thanks to the novel architecture of our deep network that captures the essential relationship of cardiovascular dynamics. Indeed, our extensive experiments show that our method achieves better image quality for cine cardiac MRI images than existing state-of-the-art methods. In addition, our method can generate reliable missing intermediate frames based on their adjacent frames, improving the temporal resolution of cine cardiac MRI sequences.
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