This study aims to evaluate the image quality of computed tomography (CT) angiograms obtained using a virtual contrast augmentation technique, adding an iodine map to the contrast-enhanced images, obtained through a digital subtraction technique, reducing the dose of intravenous iodinated contrast medium compared to the conventional institutional standard technique.

This prospective investigation enrolled patients previously diagnosed with abdominal aortic aneurysms, who underwent two successive computed tomography angiographies (CTAs) of the aorta. One CTA was performed employing the virtual contrast enhancement boost technique with a 40% decrease in the intravenous contrast medium dose, while the other adhered to the conventional protocol without any reduction in contrast medium volume. After imaging, both qualitative and quantitative assessments were conducted to evaluate the opacification level of the aorta and its branching vessels.

The study analyzed 28 examinations from 14 subjects (two per participant). Qualitative analysis revealed that image quality was deemed adequate for diagnostic purposes, with nearly all arterial segments in both protocols rated as good or excellent. Although no statistically significant differences were detected, the average attenuation across all arterial segments in the angiograms exhibited a trend towards higher values in the virtual contrast enhancement boost technique, compared to the conventional protocol. Similarly, the signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) demonstrated comparable levels between the two methodologies.

The application of virtual contrast enhancement in the acquisition of CT angiograms of the aorta maintains comparable image quality, while reducing the dosage of intravenous contrast medium by 40%. This approach suggests a potential for optimizing contrast use in vascular imaging, aligning with goals related to patient safety and radiological efficiency.

The objective of the work is to develop algorithms for accurate and stable reconstruction of multiple basis images and virtual monochromatic images (VMIs) in dual-energy CT (DECT).

The reconstruction task remains challenging in DECT imaging cases with basis regions containing up to 3 basis materials, because the number of voxel values, i.e., unknowns, within the basis regions can be considerably larger than the number of measurements. We exploit the volume-conservation (VC) constraint to augment data and develop an optimization-based algorithm for accurate and stable reconstruction of multiple basis images and VMIs in the cases.

Numerical studies are conducted involving simulated and real data for the cases with basis regions containing up to 3 basis materials; and the study result reveals that the algorithm developed can accurately and stably reconstruct multiple basis images and VMIs in DECT.

The exploitation of the VC constraint enables the algorithm proposed for accurately and stably reconstructing multiple basis images in DECT.

The work reveals that it is possible to obtain accurate VMIs and contrast-concentration estimations in DECT with basis regions containing up to 3 basis materials. The algorithm can be extended for obtaining multiple basis images in photon-counting CT and also be applied to dual-energy data collected only over limited-angular range and/or at sparse views.

Ischemic stroke, accounting for approximately 80% of all stroke cases, remains a leading cause of death and disability worldwide. Effective management of ischemic stroke is heavily influenced by its etiology, which can range from large-artery atherosclerosis and cardiac embolism to cerebral small-vessel occlusions and cryptogenic strokes. Cardioembolic stroke, which makes up about 30% of ischemic strokes, often leads to more severe symptoms and worse outcomes, necessitating anticoagulation therapy for prevention. Cryptogenic strokes, comprising over 25% of ischemic strokes, pose significant challenges for treatment and prevention due to their elusive nature. Thorough investigation of cardioembolic sources during the acute phase of stroke is crucial. While transthoracic and transesophageal echocardiography are traditional methods for detecting intracardiac thrombi and patent foramen ovale (PFO), cardiac CT has emerged as a non-invasive, efficient alternative. Cardiac CT can effectively visualize intracardiac thrombi, PFO, valvular abnormalities, tumors, and complex aortic plaques. This review discusses the potential applications of cardiac CT in ischemic stroke, emphasizing its role in identifying stroke etiology, predicting stroke risk, and assessing patient prognosis. The integration of advanced imaging technologies and artificial intelligence further enhances its diagnostic accuracy and clinical utility, promising to improve outcomes and reduce the healthcare burden associated with ischemic stroke.

Calcific aortic valve stenosis (CAVS) is a common cardiovascular condition associated with significant adverse events and high mortality rates. Unfortunately, there are currently no effective pharmacological treatments to halt or prevent its progression. Through our analysis of global trends and treatment strategies, we have identified valuable insights and promising therapeutic possibilities. Additionally, by utilizing bibliometric and visualization techniques, we provide a comprehensive overview of the current research landscape in this field.

According to our design idea, we used the Web of Science database to select publications on aortic stenosis and related treatments. Through our VOSviewer and CiteSpace analysis, a total of 787 articles have been analyzed by September 2024. We also summarize and explore the most prolific authors, the most prolific countries, and the journals and institutions that publish the most articles.

A visual analysis of the collected articles reveals that Canada and the United States have the highest publication volumes in this field. Among institutions, Harvard University in the U.S. leads in publication count, followed by Laval University in Canada and the University of California in the U.S. The top three research hotspots are stenosis, calcification, and progression. The journal with the highest number of publications in this area is Frontiers in Cardiovascular Medicine, followed by Catheterization and Cardiovascular Interventions and Arteriosclerosis, Thrombosis and Vascular Biology. Furthermore, research on CAVS treatment spans various directions and focuses, including therapeutic approaches, pathogenesis, and diagnostic methods.

Research into CAVS treatment has advanced significantly over the years. While interventional and surgical valve replacement remains the mainstay treatments for aortic stenosis, they are insufficient to fully meet the needs of the patient. Emerging priorities now focus on improving diagnostics, exploring innovative therapies, uncovering disease mechanisms, and developing novel drugs. These findings highlight the evolving demands in this field and underscore the need for continued research to address these challenges.

Cardiovascular diseases (CVDs) are the leading cause of death globally, and almost one-half of all adults in the United States have at least one form of heart disease. This review focused on advanced technologies, genetic variables in CVD, and biomaterials used for organ-independent cardiovascular repair systems.

A variety of implantable and wearable devices, including biosensor-equipped cardiovascular stents and biocompatible cardiac patches, have been developed and evaluated. The incorporation of those strategies will hold a bright future in the management of CVD in advanced clinical practice.

This study employed widely used academic search systems, such as Google Scholar, PubMed, and Web of Science. Recent progress in diagnostic and treatment methods against CVD, as described in the content, are extensively examined. The innovative bioengineering, gene delivery, cell biology, and artificial intelligence-based technologies that will continuously revolutionize biomedical devices for cardiovascular repair and regeneration are also discussed. The novel, balanced, contemporary, query-based method adapted in this manuscript defined the extent to which an updated literature review could efficiently provide research on the evidence-based, comprehensive applicability of cardiovascular devices for clinical treatment against CVD.

Advanced technologies along with artificial intelligence-based telehealth will be essential to create efficient implantable biomedical devices, including cardiovascular stents. The proper statistical approaches along with results from clinical studies including model-based risk probability prediction from genetic and physiological variables are integral for monitoring and treatment of CVD risk.

To overcome the current obstacles in cardiac repair and regeneration and achieve successful therapeutic applications, future interdisciplinary collaborative work is essential. Novel cardiovascular devices and their targeted treatments will accomplish enhanced health care delivery and improved therapeutic efficacy against CVD. As the review articles contain comprehensive sources for state-of-the-art evidence for clinicians, these high-quality reviews will serve as a first outline of the updated progress on cardiovascular devices before undertaking clinical studies.

In patients who have undergone ischemic stroke therapy, retained iodine-based contrast can resemble acute intracranial hemorrhage (ICH) on standard computed tomography (CT). The purpose of this study is to determine the accuracy of commercially available artificial intelligence software for differentiating hemorrhage from contrast in such cases.

A total of 45 CT scans analyzed by Aidoc software that also included dual-energy iodine subtraction maps from dual energy CT from 23 unique patients (12 male, 11 female, age range 30-99 years, mean age 67.6 years, standard deviation 18.5 years) following recent ischemic stroke therapy were retrospectively reviewed for the presence of hemorrhage versus retained contrast material.

The sensitivity and specificity of the model in detecting acute intracranial hemorrhage as opposed to contrast were 51.7 and 50.0%, respectively. The positive and negative predictive values were 65.2 and 36.4%, respectively.

The current Aidoc software is not optimized for differentiating between acute hemorrhage and retained contrast on CT. This justifies the development of a more robust artificial intelligence model trained to differentiate between ICH and iodine contrast based on both DECT and standard CT images.

Photon counting computed tomography (PCCT) employs direct and spectrally resolved counting of individual x-ray quanta, enhancing image quality compared to the standard energy-integrating CT (EICT).

To evaluate the quantitative improvements in CT image quality metrics by comparing the first medical PCCT with a state-of-the-art EICT.

The PCCT versus EICT noise improvement ratio R was derived from the quantum statistics of the measurement process and measured across the clinical x-ray flux range for both systems. Detector and system modulation transfer functions (MTFs) were obtained using tilted-slit and wire phantom measurements. Image root mean square (RMS) noise, noise power spectrum (NPS), and x-ray patient dose were compared using a CatPhan phantom at two identical clinical target resolutions.

The measurement of the PCCT noise improvement ratio R showed an elimination of electronic noise and a 10% noise transfer advantage. The PCCT detector MTF exhibited 3x higher angular resolution limits in comparison to EICT and close to ideal sinc behavior due to the electromagnetic formation of pixels in the PCCT semiconductor detector. This translated to 3.5x enhancements in CT system MTF ratios at 10 LP/cm, reflecting a significant improvement in millimeter range CT imaging. Both the improved quantum detection and the system MTF ratio improvement contribute to the measured 3x enhancements in image NPS at 10 LP/cm for identical image target resolution. An improvement of up to 1.7x in RMS image noise was observed accordingly. For low and ultra-low dose imaging with image filtering, dose efficiency increased between 2x and 10x, demonstrating the PCCT's capability to advance CT ultra-low dose imaging.

The direct counting detection in PCCT has been shown to significantly improve sinogram noise and detector MTF ratios compared to energy integrating EICT. The observed translations into CT system MTF, image NPS, image noise, and dose ratios reflect a paradigm shift for CT image quality and dose efficiency.

To perform a systematic review and meta-analysis of the diagnostic performance of contrast enhancement to differentiate benign and malignant renal lesions using CT and MRI.

A systematic literature search of databases was performed between January 1, 1980 and September 26, 2022. We included studies reporting the accuracy of CE thresholds on CT and MRI indeterminate renal lesions, with pathologic examination and follow-up as the reference standard. Studies meeting the inclusion criteria underwent quality assessment with the Cochrane recommendation for diagnostic accuracy study Quality Assessment 2. We excluded studies with high risk of bias. Summary estimates of diagnostic performance were obtained with the bivariate Bayesian model for CT and MRI. Effects of different thresholds and index test modalities were investigated through subgroup analysis.

Eleven studies (1372 patients) using CT and six studies (218 patients) using MRI were included. Of the eleven studies, 15 parts from 9 studies were considered for the CT meta-analysis, and 6 parts from 3 studies for the MRI meta-analysis. Diagnostic performance meta-analysis on enhancement found a 96% summary sensitivity (95% CI 92, 98) and a 92% summary specificity (95% CI 85, 96) in 2056 renal lesions for CT; and 82% summary sensitivity (95% CI 65, 89) and an 89% summary specificity (95% CI 77, 95) in 634 lesions for MRI.

CT and MRI have high accuracy to determine enhancement and classify renal lesions, and both modalities can be used with confidence for this purpose. There are still some controversies about the optimal thresholds. Future research should evaluate outcomes and decision-making pathways to determine whether basing clinical decisions on a specific threshold on CT and MRI would do more harm than good.

This proof-of-concept study explores quantitative imaging of articular cartilage using photon-counting detector computed tomography (PCD-CT) with a dual-contrast agent approach, comparing it to clinical dual-energy CT (DECT). The diffusion of cationic iodinated CA4 + and non-ionic gadolinium-based gadoteridol contrast agents into ex vivo bovine medial tibial plateau cartilage was tracked over 72 h. Continuous maps of the contrast agents' diffusion were created, and correlations with biomechanical indentation parameters (equilibrium and instantaneous moduli, and relaxation time constants) were examined at 28 specific locations. Cartilage at each location was analyzed as full-thickness to ensure a fair comparison, and calibration-based material decomposition was employed for concentration estimation. Both DECT and PCD-CT exhibit strong correlations between CA4 + content and biomechanical parameters, with PCD-CT showing superior significance, especially at later time points. DECT lacks significant correlations with gadoteridol-related parameters, while PCD-CT identifies noteworthy correlations between gadoteridol diffusion and biomechanical parameters. In summary, the experimental PCD-CT setup demonstrates superior accuracy and sensitivity in concentration estimation, suggesting its potential as a more effective tool for quantitatively assessing articular cartilage condition compared to a conventional clinical DECT scanner.

The purpose of this study was to accurately estimate proton stopping power ratio (SPR), relative electron density ρ e, effective atomic number (Z eff), and mean excitation energy (I) using one simple robust model and design a machine learning algorithm that will lead to automation.

Empirical relationships between computed tomography (CT) number and SPR, ρ e (Z eff) and I were used to formulate a model that predicts all the four parameters using linear attenuation coefficients which can be converted to CT numbers. The results of these models were compared with the results of other existing models. Thirty-three ICRU human tissues were used as modeling data and 12 Gammex inserts as testing data for the machine learning algorithm designed. More ways of tissue classification were introduced to improve accuracy. In the examples, the dual energy methods were implemented using 80 kVp and 150 kVP/Sn.

The proposed method gave modeling root mean square error (RMSE) near 1% at maximum for the case of SPR and ρ e for both single and dual-energy CT approaches considered with modeling RMSE of 0.32% for ρ e and 0.38% for SPR as modeling RMSE with room for improvement (this can be done by adjusting the model number of terms as well as the parameters). The method was able to achieve modeling RMSE of 1.11% for I and 1.66% for Z ef f. The mean error for all the estimated quantities was near 0.00%. In most cases, the proposed method has lower testing RMSE and mean error compare to the other methods presented in the study.

The proposed method proves to be more flexible and robust among all presented methods since it has lower testing error in most cases and can be improved based on data using the machine learning algorithm. The algorithm can also improve estimation by adjusting the model as well as aid in automation and it's easy to implement.

Metal implants may affect the image quality, iodine concentration (IC), and CT Hounsfield unit (HU) quantification accuracy.

To investigate the quantitative accuracy of IC and HU from dual-layer spectral detector (DLCT) in the presence of metal artifacts.

An experimental cylindrical phantom containing eight iodine inserts and two metal inserts was designed. The phantom underwent scanning at three radiation dose levels and two tube voltage settings. A set of conventional images (CIs), virtual monoenergetic images (VMIs), and iodine concentration maps (ICMs) were generated and measured for all the eight iodine inserts. Quantitative indicators of mean absolute percentage error (MAPE), artifact index (AI), contrast-to-noise ratio (CNR), signal-to-noise ratio (SNR), and standard deviation (SD) on CIs and VMIs were calculated for IC and HU. Subjective score evaluation was also conducted.

The MAPEiodine values of all regions of interest across different scanning configurations were all <5%. Almost all APEiodine values were <5%, indicating that metal artifacts had little impact on IC measurements. When the tube voltage was fixed, the SD value of attenuation decreased with the increase of the tube current; this is also true when the tube current was fixed. The middle energy reconstructions seemed to give a good balance between reducing artifacts and improving contrast.

VMIs from DLCT can reduce metal artifacts, the accuracy of IC quantification is not sensitive to imaging parameters. In summary, metal implants exhibit minimal impact on image quality and IC quantification accuracy in reconstructed images from DLCT.

To determine the efficacy and safety of virtual unenhanced imaging by comparing the attenuation values of virtual and true unenhanced images acquired using third-generation dual-source dual-energy computed tomography (dsDECT).

Single-energy non-contrast and dual-energy arterial and venous phase images of 97 patients who underwent triphasic abdominal computed tomography (CT) were included in this retrospective study. Virtual unenhanced images were generated for the arterial (a) and venous (v) phases using two dsDECT algorithms. The attenuation values were measured on the true and virtual unenhanced images of the liver, spleen, kidney, gallbladder, paraspinal muscle, aorta, subcutaneous fat, retroperitoneal fat, and renal cysts.

A statistically significant difference was observed between the attenuation values ​​of true and virtual unenhanced images for all tissues (p < 0.001-0.025), except the venous phase virtual unenhanced images of the kidney, renal cysts, and gallbladder (p = 0.061-0.325). The proportion of cases with differences of ≥ 10 Hounsfield unit (HU) in the attenuation values between the virtual and true unenhanced images ranged from 3% to 8% for renal parenchyma, renal cysts, and gallbladder using this algorithm; however, this proportion was up to 90% for adipose tissue. No significant correlation was observed between the body mass index and attenuation differences between the true and virtual unenhanced images, except for those of the aorta and paraspinal muscle.

Virtual unenhanced images acquired using third-generation dsDECT cannot replace true unenhanced images in clinical practice owing to the difference between the attenuation values and variability of attenuation between true and virtual unenhanced images.

To conduct a bibliometric analysis of the prospects and obstacles associated with dual- and multi-energy CT in thoracic disease, emphasizing its current standing, advantages, and areas requiring attention.

The Web of Science Core Collection was queried for relevant publications in dual- and multi-energy CT and thoracic applications without a limit on publication date or language. The Bibliometrix packages, VOSviewer, and CiteSpace were used for data analysis. Bibliometric techniques utilized were co-authorship analyses, trend topics, thematic map analyses, thematic evolution analyses, source's production over time, corresponding author's countries, and a treemap of authors' keywords.

A total of 1992 publications and 7200 authors from 313 different sources were examined in this study. The first available document was published in November 1982, and the most cited article was cited 1200 times. Siemens AG in Germany emerged as the most prominent author affiliation, with a total of 221 published articles. The most represented scientific journals were the "European Radiology" (181 articles, h-index = 46), followed by the "European Journal of Radiology" (148 articles, h-index = 34). Most of the papers were from Germany, the USA, or China. Both the keyword and topic analyses showed the history of dual- and multi-energy CT and the evolution of its application hotspots in the chest.

Our study illustrates the latest advances in dual- and multi-energy CT and its increasingly prominent applications in the chest, especially in lung parenchymal diseases and coronary artery diseases. Photon-counting CT and artificial intelligence will be the emerging hot technologies that continue to develop in the future.

This study aims to provide valuable insights into energy-based imaging in chest disease, validating the clinical application of multi-energy CT together with photon-counting CT and effectively increasing utilization in clinical practice.

Bibliometric analysis is fundamental to understanding the current and future state of dual- and multi-energy CT. Research trends and leading topics included coronary artery disease, pulmonary embolism, and radiation dose. All analyses indicate a growing interest in the use of energy-based imaging techniques for thoracic applications.

Conventional single-energy CT can only provide a raw estimation of electron density (ED) for dose calculation by developing a calibration curve that simply maps the HU values to ED values through their correlations. Spectral CT, also known as dual-energy CT (DECT) or multi-energy CT, can generate a series of quantitative maps, such as ED maps. Using spectral CT for radiotherapy simulations can directly acquire ED information without developing specific calibration curves. The purpose of this study is to assess the feasibility of utilizing electron density (ED) maps generated by a novel dual-layer detector spectral CT simulator for dose calculation in radiotherapy treatment plans.

30 patients from head&neck, chest, and pelvic treatment sites were selected retrospectively, and all of them underwent spectral CT simulation. Treatment plans based on conventional CT images were transplanted to ED maps with the same structure set, including planning target volume (PTV) and organs at risk (OARs), and the dose distributions were then recalculated. The differences in dose and volume histogram (DVH) parameters of the PTV and OARs between the two types of plans were analyzed and compared. Besides, gamma analysis between these plans was performed by using MEPHYSTO Navigator software.

In terms of PTV, the homogeneity index (HI), gradient index (GI), D2%, D98%, and Dmean showed no significant difference between conventional plans and ED plans. For OARs, statistically significant differences were observed in parotids D50%, brainstem in head&neck plans, spinal cord in chest plans and rectum D50% in pelvic plans, whereas the variance remained minor. For the rest, the DVH parameters exhibited no significant difference between conventional plans and ED plans. All of the mean gamma passing rates (GPRs) of gamma analysis were higher than 90%.

Compared to conventional treatment plans relying on CT images, plans utilizing ED maps demonstrated similar dosimetric quality. However, the latter approach enables direct utilization in dose calculation without the requirements of establishing and selecting a specific Hounsfield unit (HU) to ED calibration curve, providing an advantage in clinical applications.

We develop optimization-based algorithms to accurately reconstruct multiple ( 2) basis images directly from dual-energy (DE) data in CT.

In medical and industrial CT imaging, some basis materials such as bone, metals, and contrast agents of interest are confined often spatially within regions in the image. Exploiting this observation, we develop an optimization-based algorithm to reconstruct, directly from DE data, basis-region images from which multiple ( 2) basis images and virtual monochromatic images (VMIs) can be obtained over the entire image array.

We conduct experimental studies using simulated and real DE data in CT, and evaluate basis images and VMIs obtained in terms of visual inspection and quantitative metrics. The study results reveal that the algorithm developed can accurately and robustly reconstruct multiple ( 2) basis images directly from DE data.

The developed algorithm can yield accurate multiple ( 2) basis images, VMIs, and physical quantities of interest from DE data in CT.

The work may provide insights into the development of practical procedures for reconstructing multiple basis images, VMIs, and physical quantities from DE data in applications. The work can be extended to reconstruct multiple basis images in multi-spectral or photon-counting CT.

Dual-energy CBCT imaging plays a crucial role in advanced imaging applications due to its ability to quantify material components. Although there are multiple established systems for dual-energy imaging, they often come with high deployment costs. This study investigates the feasibility of dual-energy material decomposition on a less expensive dual-source CBCT system. Due to the specific geometry of dual-source CBCT, cone-beam (CB) artifacts seriously degrade the material decomposition quality when the direct inversion method is adopted. In this paper, a novel dual-source intra-guided material decomposition framework is proposed to suppress CB artifacts in the material maps. Firstly, a dual-source two-pass algorithm is developed for artifact pre-correction during image reconstruction. Then, the structure of small cone-angle regions from one X-ray source is utilized to guide cone beam artifact removal in large cone-angle regions from another source. Finally, direct matrix inverse material decomposition is applied to the filtered image. Extensive experiments have been conducted on the authentic human phantom and demonstrate that the proposed framework achieves excellent performance in cone beam artifact reduction compared to other methods, and significantly enhances the quality of the material maps.

The split filter CT can filter X-ray beam. Theoretically, the split filter CT not only provides a good low-energy beam, but also provides a more robust CT value. The aim of this study was to compare conventional single-energy computed tomography (SECT) and twin-beam dual-energy (TBDE) CT regarding the quantitative consistency and stabilities of HU measurements at different abdominal organs. Forty-four patients were prospectively enrolled to randomly receive SECT and TBDE protocols at either body part of a thorax-abdominal examination. Their overlapping scan coverage was subjected to further image analysis. For TBDE scans, composed images(c-images) and virtual monoenergetic images (VMIs) at 60, 70, 80, and 90 kiloelectron volt (keV) were reconstructed. The attenuations were measured at 5 abdominal organs and compared between SECT and TBDE to characterize quantitative consistency by intraclass correlation coefficients (ICCs), whereas their standard deviations were used to assess the Hounsfield Unit (HU) stability. The c-images, 70 keV and 80 keV VMIs from TBDE provided consistent HU values (all ICCs > 0.8) with the SECT measurements; moreover, these TBDE images had superior HU stability over SECT images in all abdominal measurements except for fat tissue. The best HU stability can be achieved in 80 keV VMIs with the lowest noise level. The c-images and VMIs derived from TBDE can produce consistent values as SECT. The 80 keV images displayed better HU stability and a lower noise level across various abdominal organs.

Photon-counting CT systems generally allow for acquiring multiple spectral datasets and thus for decomposing CT images into multiple materials. We introduce a prior knowledge-free deterministic material decomposition approach for quantifying three material concentrations on a commercial photon-counting CT system based on a single CT scan. We acquired two phantom measurement series: one to calibrate and one to test the algorithm. For evaluation, we used an anthropomorphic abdominal phantom with inserts of either aqueous iodine solution, aqueous tungsten solution, or water. Material CT numbers were predicted based on a polynomial in the following parameters: Water-equivalent object diameter, object center-to-isocenter distance, voxel-to-isocenter distance, voxel-to-object center distance, and X-ray tube current. The material decomposition was performed as a generalized least-squares estimation. The algorithm provided material maps of iodine, tungsten, and water with average estimation errors of 4% in the contrast agent maps and 1% in the water map with respect to the material concentrations in the inserts. The contrast-to-noise ratio in the iodine and tungsten map was 36% and 16% compared to the noise-minimal threshold image. We were able to decompose four spectral images into iodine, tungsten, and water.

The purpose of this study was to evaluate a three-material decomposition algorithm for hepatic fat quantification using a dual-layer computed tomography (DL-CT) and MRI as reference standard on a large patient cohort.

A total of 104 patients were retrospectively included in our study, i.e., each patient had an MRI exam and a DL-CT exam in our institution within a maximum of 31 days. Four regions of interest (ROIs) were positioned blindly and similarly in the liver, by two independent readers on DL-CT and MRI images. For DL-CT exams, all imaging phases were included. Fat fraction agreement between CT and MRI was performed using intraclass correlation coefficients (ICC), determination coefficients R2, and Bland-Altman plots. Diagnostic performance was determined using sensitivity, specificity, and positive and negative predictive values. The cutoff for steatosis was 5%.

Correlation between MRI and CT data was excellent for all perfusion phases with ICC calculated at 0.99 for each phase. Determination coefficients R2 were also good for all perfusion phases (about 0.95 for all phases). Performance of DL-CT in the diagnosis of hepatic steatosis was good with sensitivity between 89 and 91% and specificity ranging from 75 to 80%, depending on the perfusion phase. The positive predictive value was ranging from 78 to 93% and the negative predictive value from 82 to 86%.

Multi-material decomposition in DL-CT allows quantification of hepatic fat fraction with a good correlation to MRI data.

The use of DL-CT allows for detection of hepatic steatosis. This is especially interesting as an opportunistic finding CT performed for other reasons, as early detection can help prevent or slowdown the development of liver metabolic disease.

• Hepatic fat fractions provided by the dual-layer CT algorithm is strongly correlated with that measured on MRI. • Dual-layer CT is accurate to detect hepatic steatosis ≥ 5%. • Dual-layer CT allows opportunistic detection of steatosis, on CT scan performed for various indications.

Dual-energy computed tomography (DECT) has emerged as a valuable imaging modality in the diagnosis and management of various cerebrovascular pathologies, including subarachnoid hemorrhage, intracranial hemorrhage, and acute ischemic stroke. This article reviews the principles of DECT and its applications in the evaluation and management of these conditions. The authors discuss the advantages of DECT over conventional computed tomography, as well as its limitations, and provide an overview of current research and future directions in the field.

The purpose of this study was to develop a simple flexible method for accurate estimation of stopping power ratio (SPR) and mean excitation energy (I) using relative electron density (ρ e).

The model was formulated using empirical relationships between SPR, mean excitation energy I, and relative electron density. Some examples were implemented, and a comparison was carried out using other existing methods. The needed coefficients in the model were estimated using optimization tools. Basis vector method (BVM) and Hunemohr and Saito (H-S) method were applied to estimate the ρ e used in the application section. 80 kVp and 150 kVpSn were used as low and high energy, respectively, for the implementation of dual-energy methods.

All the examples of the proposed method considered have modeling error that is ≤0.32% and testing root mean square error (RMSE) ≤0.92% for SPR with a mean error close to 0.00%. The method was able to achieve modeling RMSE of 2.12% for mean excitation energy with room for improvement. Similar or better results were achieved in application to BVM.

The method showed robustness in application by achieving lower testing error than other presented methods in most cases. It achieved accurate estimation which can be improved using the machine learning algorithm since it is flexible to implement in terms of the function (model) degree and tissue classification.

Due to technical constraints, dual-source dual-energy CT scans may lack spectral information in the periphery of the patient.

Here, we propose a deep learning-based iterative reconstruction to recover the missing spectral information outside the field of measurement (FOM) of the second source-detector pair.

In today's Siemens dual-source CT systems, one source-detector pair (referred to as A) typically has a FOM of about 50 cm, while the FOM of the other pair (referred to as B) is limited by technical constraints to a diameter of about 35 cm. As a result, dual-energy applications are currently only available within the small FOM, limiting their use for larger patients. To derive a reconstruction at B's energy for the entire patient cross-section, we propose a deep learning-based iterative reconstruction. Starting with A's reconstruction as initial estimate, it employs a neural network in each iteration to refine the current estimate according to a raw data fidelity measure. Here, the corresponding mapping is trained using simulated chest, abdomen, and pelvis scans based on a data set containing 70 full body CT scans. Finally, the proposed approach is tested on simulated and measured dual-source dual-energy scans and compared against existing reference approaches.

For all test cases, the proposed approach was able to provide artifact-free CT reconstructions of B for the entire patient cross-section. Considering simulated data, the remaining error of the reconstructions is between 10 and 17 HU on average, which is about half as low as the reference approaches. A similar performance with an average error of 8 HU could be achieved for real phantom measurements.

The proposed approach is able to recover missing dual-energy information for patients exceeding the small 35 cm FOM of dual-source CT systems. Therefore, it potentially allows to extend dual-energy applications to the entire-patient cross section.

Photon-counting CT (PCCT) is an emerging advanced CT technology that differs from conventional CT in its ability to directly convert incident x-ray photon energies into electrical signals. The detector design also permits substantial improvements in spatial resolution and radiation dose efficiency and allows for concurrent high-pitch and high-temporal-resolution multienergy imaging. This review summarizes (a) key differences in PCCT image acquisition and image reconstruction compared with conventional CT; (b) early evidence for the clinical benefit of PCCT for high-spatial-resolution diagnostic tasks in thoracic imaging, such as assessment of airway and parenchymal diseases, as well as benefits of high-pitch and multienergy scanning; (c) anticipated radiation dose reduction, depending on the diagnostic task, and increased utility for routine low-dose thoracic CT imaging; (d) adaptations for thoracic imaging in children; (e) potential for further quantitation of thoracic diseases; and (f) limitations and trade-offs. Moreover, important points for conducting and interpreting clinical studies examining the benefit of PCCT relative to conventional CT and integration of PCCT systems into multivendor, multispecialty radiology practices are discussed.

Objective. Dual spectral computed tomography (DSCT) is a very challenging problem in the field of imaging. Due to the nonlinearity of its mathematical model, the images reconstructed by the conventional CT usually suffer from the beam hardening artifacts. Additionally, several existing DSCT methods rely heavily on the information of the spectra, which is often not readily available in applications. To address this problem, in this study, we aim to develop a novel approach to improve the DSCT reconstruction performance.Approach. A model-based direct inversion network (MDIN) is proposed for DSCT, which can directly predict the basis material images from the collected polychromatic projections. The all operations are performed in the network, requiring neither the conventional algorithms nor the information of the spectra. It can be viewed as an approximation to the inverse procedure of DSCT imaging model. The MDIN is composed of projection pre-decomposition module (PD-module), domain transformation layer (DT-layer), and image post-decomposition module (ID-module). The PD-module first performs the pre-decomposition on the polychromatic projections that consists of a series of stacked one-dimensional convolution layers. The DT-layer is designed to obtain the preliminary decomposed results, which has the characteristics of sparsely connected and learnable parameters. And the ID-module uses a deep neural network to further decompose the reconstructed results of the DT-layer so as to achieve higher-quality basis material images.Main results. Numerical experiments demonstrate that the proposed MDIN has significant advantages in substance decomposition, artifact reduction and noise suppression compared to other methods in the DSCT reconstruction.Significance. The proposed method has a flexible applicability, which can be extended to other CT problems, such as multi-spectral CT and low dose CT.

Spectral computed tomography (CT) has recently emerged as an advanced version of medical CT and significantly improves conventional (single-energy) CT. Spectral CT has two main forms: dual-energy computed tomography (DECT) and photon-counting computed tomography (PCCT), which offer image improvement, material decomposition, and feature quantification relative to conventional CT. However, the inherent challenges of spectral CT, evidenced by data and image artifacts, remain a bottleneck for clinical applications. To address these problems, machine learning techniques have been widely applied to spectral CT. In this review, we present the state-of-the-art data-driven techniques for spectral CT.

Dual-energy computed tomography (DECT) is an advanced imaging technique that acquires data using two distinct X-ray energy spectra, typically at 80 and 140 kVp, to differentiate materials based on their atomic number and electron density. This capability allows for the enhanced visualisation of various pathologies, including bone marrow oedema (BMO), by providing high-resolution images with notable energy spectral separation while maintaining radiation doses comparable to conventional CT. DECT's ability to create colour-coded virtual non-calcium (VNCa) images has proven particularly valuable in detecting traumatic bone marrow lesions (BMLs) and subtle fractures, offering a reliable alternative or complement to MRI. DECT has emerged as a significant tool in the detection and characterisation of bone marrow pathologies, especially in traumatic injuries. Its ability to generate high-resolution images and distinguish between different tissue types makes it a valuable asset in clinical diagnostics. With its comparable diagnostic accuracy to MRI and the added advantage of reduced examination time and increased availability, DECT represents a promising advancement in the imaging of BMO and related conditions.

The development of photon-counting CT systems has focused on semiconductor detectors like cadmium zinc telluride (CZT) and cadmium telluride (CdTe). However, these detectors face high costs and charge-sharing issues, distorting the energy spectrum. Indirect detection using Yttrium Orthosilicate (YSO) scintillators with silicon photomultiplier (SiPM) offers a cost-effective alternative with high detection efficiency, low dark count rate, and high sensor gain.

This work aims to demonstrate the feasibility of the YSO/SiPM detector (DexScanner L103) based on the Multi-Voltage Threshold (MVT) sampling method as a photon-counting CT detector by evaluating the synthesis error of virtual monochromatic images.

In this study, we developed a proof-of-concept benchtop photon-counting CT system, and employed a direct method for empirical virtual monochromatic image synthesis (EVMIS) by polynomial fitting under the principle of least square deviation without X-ray spectral information. The accuracy of the empirical energy calibration techniques was evaluated by comparing the reconstructed and actual attenuation coefficients of calibration and test materials using mean relative error (MRE) and mean square error (MSE).

In dual-material imaging experiments, the overall average synthesis error for three monoenergetic images of distinct materials is 2.53% ±2.43%. Similarly, in K-edge imaging experiments encompassing four materials, the overall average synthesis error for three monoenergetic images is 4.04% ±2.63%. In rat biological soft-tissue imaging experiments, we further predicted the densities of various rat tissues as follows: bone density is 1.41±0.07 g/cm3, adipose tissue density is 0.91±0.06 g/cm3, heart tissue density is 1.09±0.04 g/cm3, and lung tissue density is 0.32±0.07 g/cm3. Those results showed that the reconstructed virtual monochromatic images had good conformance for each material.

This study indicates the SiPM-based photon-counting detector could be used for monochromatic image synthesis and is a promising method for developing spectral computed tomography systems.

Photon counting detector (PCD) CT represents the newest advance in CT technology, with improved radiation dose efficiency, increased spatial resolution, inherent spectral imaging capabilities, and the ability to eliminate electronic noise. Its design fundamentally differs from conventional energy integrating detector CT because photons are directly converted to electrical signal in a single step. Rather than converting X-rays to visible light and having an output signal that is a summation of energies, PCD directly counts each photon and records its individual energy information. The current commercially available PCD-CT utilizes a dual-source CT geometry, which allows 66 ms cardiac temporal resolution and high-pitch (up to 3.2) scanning. This can greatly benefit pediatric patients by facilitating high quality fast scanning to allow sedation-free imaging. The energy-resolving nature of the utilized PCDs allows "always-on" dual-energy imaging capabilities, such as the creation of virtual monoenergetic, virtual non-contrast, virtual non-calcium, and other material-specific images. These features may be combined with high-resolution imaging, made possible by the decreased size of individual detector elements and the absence of interelement septa. This work reviews the foundational concepts associated with PCD-CT and presents examples to highlight the benefits of PCD-CT in the pediatric population.

To evaluate the depiction of wrist tendons in virtual monochromatic images (VMIs) during a dual-energy CT (DE-CT) with the VMI image of conventional equivalent to 120 kVp.

Using Catphan600 and phantom analysis software for CT evaluation, measurements of VMI in a DE-CT were performed corresponding to the tube voltages of single-energy CT at 120 kVp. Using a Discovery CT750 HD CT scanner (GE Healthcare) with DE-CT technology, 73 patients were scanned. We calculated the CT number, image noise, visual score, and contrast noise ratio (CNR) at the extensor pollicis tendon, extensor digitorum tendon, and flexor tendon in 11 VMIs from the DE-CT and VMI image of conventional equivalent to 120 kVp. The results from the optimal VMIs were then compared with that of the VMI image of the conventional equivalent to 120 kVp.

The highest CT number and CNR for the tendon were for the 140 keV VMI in the DE-CT compared to the other energy levels. There were significantly higher CT numbers, CNR values, and visual scores for each tendon at 140 keV VMI with the DE-CT (p < 0.01) compared with a VMI image of conventional equivalent to 120 kVp.

Energy level of the VMIs during DE-CT for the best wrist tendon delineation was 140 keV. This value of 140 keV for the DE-CT was significantly higher than the CT number and CNR for the extensor pollicis, extensor digitorum, and flexor tendon compared with a VMI image of conventional equivalent to 120 kVp.

Involuntary motion of the heart remains a challenge for cardiac computed tomography (CT) imaging. Although the electrocardiogram (ECG) gating strategy is widely adopted to perform CT scans at the quasi-quiescent cardiac phase, motion-induced artifacts are still unavoidable for patients with high heart rates or irregular rhythms. Dynamic cardiac CT, which provides functional information of the heart, suffers even more severe motion artifacts. In this paper, we develop a deep learning based framework for motion artifact reduction in dynamic cardiac CT. First, we build a PAD (Pseudo All-phase clinical-Dataset) based on a whole-heart motion model and single-phase cardiac CT images. This dataset provides dynamic CT images with realistic-looking motion artifacts that help to develop data-driven approaches. Second, we formulate the problem of motion artifact reduction as a video deblurring task according to its dynamic nature. A novel TT U-Net (Temporal Transformer U-Net) is proposed to excavate the spatiotemporal features for better motion artifact reduction. The self-attention mechanism along the temporal dimension effectively encodes motion information and thus aids image recovery. Experiments show that the TT U-Net trained on the proposed PAD performs well on clinical CT scans, which substantiates the effectiveness and fine generalization ability of our method. The source code, trained models, and dynamic demo will be available at https://github.com/ivy9092111111/TT-U-Net.

Computed tomography (CT) has seen remarkable developments in the past several decades, radically transforming the role of imaging in day-to-day clinical practice. Dual-energy CT (DECT), an exciting innovation introduced in the early part of this century, has widened the scope of CT, opening new opportunities due to its ability to provide superior tissue characterization. The introduction of photon-counting CT (PCCT) heralds a paradigm shift in CT scanner technology representing another significant milestone in CT innovation. PCCT offers several advantages over DECT, such as improved spectral resolution, enhanced tissue characterization, reduced image artifacts, and improved image quality.

Iodixanol is an iso-osmolar non-ionic dimeric hydrophilic contrast agent with a higher viscosity than the monomeric agents. It is the only Food and Drug Administration (FDA)-approved iso-osmolar agent in the United States, and it is the only contrast agent with an FDA-approved indication for use in cardiac computed tomographic angiography (CCTA), to assist in the diagnostic evaluation of patients with suspected coronary artery disease. In clinical studies, it has been noted to have fewer side effects and similar image quality when compared to low-osmolar contrast media. This can be attributed to the pharmacological properties of iodixanol. These contrast agents are used for coronary computed tomography angiography and cardiac catheterization. In this article, the use, tolerability, and efficacy of iodixanol are reviewed, specifically evaluating the use of CCTA and coronary angiography, including outcome studies, randomized trials, and comparisons to other contrast agents.

Gastrointestinal (GI) cancer is the leading cause of cancer-related deaths worldwide. Computed tomography (CT) is an important auxiliary tool for the diagnosis, evaluation, and prognosis prediction of gastrointestinal tumors. Spectral CT is another major CT revolution after spiral CT and multidetector CT. Compared to traditional CT which only provides single-parameter anatomical diagnostic mode imaging, spectral CT can achieve multi-parameter imaging and provide a wealth of image information to optimize disease diagnosis. In recent years, with the rapid development and application of spectral CT, more and more studies on the application of spectral CT in the characterization of GI tumors have been published. For this review, we obtained a substantial volume of literature, focusing on spectral CT imaging of gastrointestinal cancers, including esophageal, stomach, colorectal, liver, and pancreatic cancers. We found that spectral CT can not only accurately stage gastrointestinal tumors before operation but also distinguish benign and malignant GI tumors with improved image quality, and effectively evaluate the therapeutic response and prognosis of the lesions. In addition, this paper also discusses the limitations and prospects of using spectral CT in GI cancer diagnosis and treatment.

Atherosclerosis, a leading cause of mortality and morbidity worldwide, involves inflammatory processes that result in plaque formation and calcification. The early detection of the molecular changes underlying these processes is crucial for effective disease management. This study utilized positron emission tomography/computed tomography (PET/CT) with [18F] sodium fluoride (NaF) as a tracer to visualize active calcification and inflammation at the molecular level. Our aim was to investigate the association between cardiovascular risk factors and [18F] NaF uptake in the left and right common carotid arteries (LCC and RCC). A cohort of 102 subjects, comprising both at-risk individuals and healthy controls, underwent [18F] NaF PET/CT imaging. The results revealed significant correlations between [18F] NaF uptake and cardiovascular risk factors such as age (β = 0.005, 95% CI 0.003-0.008, p < 0.01 in LCC and β = 0.006, 95% CI 0.004-0.009, p < 0.01 in RCC), male gender (β = -0.08, 95% CI -0.173--0.002, p = 0.04 in LCC and β = -0.13, 95% CI -0.21--0.06, p < 0.01 in RCC), BMI (β = 0.02, 95% CI 0.01-0.03, p < 0.01 in LCC and β = 0.02, 95% CI 0.01-0.03, p < 0.01 in RCC), fibrinogen (β = 0.006, 95% CI 0.0009-0.01, p = 0.02 in LCC and β = 0.005, 95% CI 0.001-0.01, p = 0.01), HDL cholesterol (β = 0.13, 95% CI 0.04-0.21, p < 0.01 in RCC only), and CRP (β = -0.01, 95% CI -0.02-0.001, p = 0.03 in RCC only). Subjects at risk showed a higher [18F] NaF uptake compared to healthy controls (one-way ANOVA; p = 0.02 in LCC and p = 0.04 in RCC), and uptake increased with estimated cardiovascular risk (one-way ANOVA, p < 0.01 in LCC only). These findings underscore the potential of [18F] NaF PET/CT as a sensitive tool for the early detection of atherosclerotic plaque, assessment of cardiovascular risk, and monitoring of disease progression. Further research is needed to validate the technique's predictive value and its potential impact on clinical outcomes.

Photon-counting CT (PCCT) has higher spatial resolution that conventional EID CT which improves imaging of stationary coronary plaques and stents.. In this work, we evaluated the relationship between higher spatial resolution and motion acquisition on an investigational PCCT system.

An investigational photon-counting CT scanner (Siemens CounT) with ECG gating was used to image a coronary tree phantom with models of healthy, stenotic, and stented arteries using a motion simulator. Images were acquired with matched clinical parameters at rest and 60 beats per minute. An additional set of high dose stationary images were averaged to generate a motion-free, reduced noise reference. Scans were completed at standard (0.5 mm2) and high-resolution (0.25 mm2). Motion images were reconstructed at multiple phases. Regions of interest were drawn around vessels and segmented. Percentage difference from the reference standard was evaluated for vessel diameter and circularity. Mutual information between the reference and stationary and motion datasets was used as a measure of volumetric similarity.

The stenotic vessel showed the most variation from the reference when compared to healthy or stented vessels. Compared to standard resolution, high-resolution images had lower bias for diameter (-0.012 ± 0.19% vs -0.052 ± 0.14%) and lower variability for circularity (-0.13 ± 0.138% vs -0.12 ± 0.144%). Both differences were found to be statistically significant. High-resolution images had a slightly lower mutual information (1.28) than standard resolution (1.31).

The higher spatial resolution enabled by photon-counting CT can be harnessed for cardiac imaging as the benefits of high spatial resolution acquisitions remain relevant in the presence of motion.

In 1971, the first patient CT examination by Ambrose and Hounsfield paved the way for not only volumetric imaging of the brain but of the entire body. From the initial 5-minute scan for a 180° rotation to today's 0.24-second scan for a 360° rotation, CT technology continues to reinvent itself. This article describes key historical milestones in CT technology from the earliest days of CT to the present, with a look toward the future of this essential imaging modality. After a review of the beginnings of CT and its early adoption, the technical steps taken to decrease scan times-both per image and per examination-are reviewed. Novel geometries such as electron-beam CT and dual-source CT have also been developed in the quest for ever-faster scans and better in-plane temporal resolution. The focus of the past 2 decades on radiation dose optimization and management led to changes in how exposure parameters such as tube current and tube potential are prescribed such that today, examinations are more customized to the specific patient and diagnostic task than ever before. In the mid-2000s, CT expanded its reach from gray-scale to color with the clinical introduction of dual-energy CT. Today's most recent technical innovation-photon-counting CT-offers greater capabilities in multienergy CT as well as spatial resolution as good as 125 μm. Finally, artificial intelligence is poised to impact both the creation and processing of CT images, as well as automating many tasks to provide greater accuracy and reproducibility in quantitative applications.

We evaluate stability of spectral results at different heart rates, acquisition modes, and cardiac phases in first-generation clinical dual-source photon-counting CT (PCCT). A cardiac motion simulator with a coronary stenosis mimicking a 50% eccentric calcium plaque was scanned at five different heart rates (0, 60-100 bpm) with the three available cardiac scan modes (high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, retrospectively ECG-gated spiral). Subsequently, full width half max (FWHM) of the stenosis, Dice score (DSC) for the stenosed region, and eccentricity of the non-stenosed region were calculated for virtual monoenergetic images (VMI) at 50, 70, and 150 keV and iodine density maps at both diastole and systole. FWHM averaged differences of - 0.20, - 0.28, and - 0.15 mm relative to static FWHM at VMI 150 keV across acquisition parameters for high pitch prospectively ECG-triggered spiral, prospectively ECG-triggered axial, and retrospectively ECG-gated spiral scans, respectively. Additionally, there was no effect of heart rate and acquisition mode on FWHM at diastole (p-values < 0.001). DSC demonstrated similarity among parameters with standard deviations of 0.08, 0.09, 0.11, and 0.08 for VMI 50, 70, and 150 keV, and iodine density maps, respectively, with insignificant differences at diastole (p-values < 0.01). Similarly, eccentricity illustrated small differences across heart rate and acquisition mode for each spectral result. Consistency of spectral results at different heart rates and acquisition modes for different cardiac phase demonstrates the added benefit of spectral results from PCCT to dual-source CT to further increase confidence in quantification and advance cardiovascular diagnostics.

To intra-individually compare the objective and subjective image quality of coronary computed tomography angiography (CCTA) between photon-counting detector CT (PCD-CT) and energy-integrating detector CT (EID-CT).

Consecutive patients undergoing clinically indicated CCTA on an EID-CT system were prospectively enrolled for a research CCTA performed on a PCD-CT system within 30 days. Polychromatic images were reconstructed for both EID- and PCD-CT, while virtual monoenergetic images (VMI) were generated at 40, 45, 50, 55, 60 and 70 keV for PCD-CT. Two blinded readers calculated contrast-to-noise ratio (CNR) for each major coronary artery and rated image noise, vessel attenuation, vessel sharpness, and overall quality on a 1-5 Likert scale. Patients were then stratified by body mass index (BMI) [high (>30 kg/m2) vs low (<30 kg/m2)] for subgroup analysis.

A total of 20 patients (67.5 ± 9.0 years, 75% male) were included in the study. Compared with EID-CT, coronary artery CNR values from PCD-CT monoenergetic and polychromatic reconstructions were all significantly higher than CNR values from EID-CT, with incrementally greater differences in obese subjects (all p < 0.008). Subjective image noise and sharpness were also significantly higher for all VMI reconstructions compared to EID-CT (all p < 0.008). All subjective scores were significantly higher for 55, 60, and 70 keV PCD-CT than EID-CT values (all p < 0.05).

The improved objective and subjective image quality of PCD-CT compared to EID-CT may provide better visualization of the coronary arteries for a wide array of patients, especially those with a high BMI.

With advanced x-ray source and detector technologies being continuously developed, non-traditional CT geometries have been widely explored. Generalized-Equiangular Geometry CT (GEGCT) architecture, in which an x-ray source might be positioned radially far away from the focus of arced detector array that is equiangularly spaced, is of importance in many novel CT systems and designs.

GEGCT, unfortunately, has no theoretically exact and shift-invariant analytical image reconstruction algorithm in general. In this study, to obtain fast and accurate reconstruction from GEGCT and to promote its system design and optimization, an in-depth investigation on a group of approximate Filtered Back-Projection (FBP) algorithms with a variety of weighting strategies has been conducted.

The architecture of GEGCT is first presented and characterized by using a normalized-radial-offset distance (NROD). Next, shift-invariant weighted FBP-type algorithms are derived in a unified framework, with pre-filtering, filtering, and post-filtering weights, for both fixed and dynamic NROD configurations. Three viable weighting strategies are then presented including a classic one developed by Besson in the literature and two new ones generated from a curvature fitting and from an empirical formula, where all of the three weights can be expressed as certain functions of NROD. After that, an analysis of reconstruction accuracy is conducted with a wide range of NROD. Finally, the weighted FBP algorithm for GEGCT is extended to a three-dimensional form in the case of cone-beam scan with a cylindrical detector array.

Theoretical analysis and numerical study show that weights in the shift-invariant FBP algorithms can guarantee highly accurate reconstruction for GEGCT. A simulation of Shepp-Logan phantom and a GEGCT scan of lung mimicked by using a clinical lung CT dataset both demonstrate that FBP reconstructions with Besson and polynomial weights can achieve excellent image quality, with Peak Signal to Noise Ratio and Structural Similarity being at the same level as that from the standard equiangular fan-beam CT scan. Reconstruction of a cylinder object with multiple contrasts from simulated GEGCT scan with dynamic NROD is also highly consistent with fixed ones when using the Besson and polynomial weights, with root mean square error less than 7 hounsfield units, demonstrating the robustness and flexibility of the presented FBP algorithms. In terms of resolution, the direct FBP methods for GEGCT could achieve 1.35 lp/mm of spatial resolution at 10% modulation transfer functions point, higher than that of the rebinning method which can only reach 1.14 lp/mm. Moreover, 3D reconstructions of a disc phantom reveal that a greater value of NROD for GEGCT will bring less cone beam artifacts as expected.

We propose the concept of GEGCT and investigate the feasibility of using shift-invariant weighted FBP-type algorithms for reconstruction from GEGCT data without rebinning. A comprehensive analysis and phantom studies have been conducted to validate the effectiveness of proposed weighting strategies in a wide range of NROD for GEGCT with fixed and dynamic NROD.

CT is one of the most widely used modalities for musculoskeletal imaging. Recent advancements in the field include the introduction of four-dimensional CT, which captures a CT image during motion; cone-beam CT, which uses flat-panel detectors to capture the lower extremities in weight-bearing mode; and dual-energy CT, which operates at two different x-ray potentials to improve the contrast resolution to facilitate the assessment of tissue material compositions such as tophaceous gout deposits and bone marrow edema. Most recently, photon-counting CT (PCCT) has been introduced. PCCT is a technique that uses photon-counting detectors to produce an image with higher spatial and contrast resolution than conventional multidetector CT systems. In addition, postprocessing techniques such as three-dimensional printing and cinematic rendering have used CT data to improve the generation of both physical and digital anatomic models. Last, advancements in the application of artificial intelligence to CT imaging have enabled the automatic evaluation of musculoskeletal pathologies. In this review, the authors discuss the current state of the above CT technologies, their respective advantages and disadvantages, and their projected future directions for various musculoskeletal applications.

To solve the problems of spectral tomography, an X-ray optical scheme was proposed, using a crystal analyzer in Laue geometry between the sample and the detector, which allowed for the selection of predetermined pairs of wavelengths from the incident polychromatic radiation to obtain projection images. On a laboratory X-ray microtomography setup, an experiment was carried out for the first time where a mixture of micro-granules of sodium chloride NaCl, silver behenate AgC₂₂H₄₃O₂, and lithium niobate LiNbO₃ was used as a test sample to identify their spatial arrangement. The elements were chosen based on the presence of absorption edges in two of the elements in the energy range of the polychromatic spectrum of the probing radiation. The method of projection distortion correction was used to preprocess the obtained projections. To interpret the obtained reconstruction results, the segmentation method based on the analysis of joint histograms was used. This allowed us to identify each of the three substances. To compare the results obtained, additional "reference" tomographic measurements were performed: one in polychromatic and two in monochromatic (MoK_α-, MoK_β-lines) modes. It took three times less time for the tomographic experiment with the crystal analyzer, while the reconstruction accuracy was comparable to that of the "reference" tomography.

Dual-energy computed tomography (DECT) can improve the differentiation of material by using two different X-ray energy spectra, and may provide new imaging techniques to diagnostic radiology to overcome the limitations of conventional CT in characterizing tissue. Some techniques have used dual-energy imaging, which mainly includes dual-sourced, rapid kVp switching, dual-layer detectors, and split-filter imaging. In iodine images, images of the lung's perfused blood volume (PBV) based on DECT have been applied in patients with pulmonary embolism to obtain both images of the PE occluding the pulmonary artery and the consequent perfusion defects in the lung's parenchyma. PBV images of the lung also have the potential to indicate the severity of PE, including chronic thromboembolic pulmonary hypertension. Virtual monochromatic imaging can improve the accuracy of diagnosing pulmonary vascular diseases by optimizing kiloelectronvolt settings for various purposes. Iodine images also could provide a new approach in the area of thoracic oncology, for example, for the characterization of pulmonary nodules and mediastinal lymph nodes. DECT-based lung ventilation imaging is also available with noble gases with high atomic numbers, such as xenon, which is similar to iodine. A ventilation map of the lung can be used to image various pulmonary diseases such as chronic obstructive pulmonary disease.

Computed tomography (CT) images display anatomic structures across 3 dimensions and are highly quantitative; they are the reference standard for 3-dimensional geometric measurements and are used for 3-dimensional printing of anatomic models and custom implants, as well as for radiation therapy treatment planning. The pixel intensity in CT images represents the linear x-ray attenuation coefficient of the imaged materials after linearly scaling the coefficients into a quantity known as CT numbers that is conveyed in Hounsfield units. When measured with the same scanner model, acquisition, and reconstruction parameters, the mean CT number of a material is highly reproducible, and quantitative applications of CT scanning that rely on the measured CT number, such as for assessing bone mineral density or coronary artery calcification, are well established. However, the strong dependence of CT numbers on x-ray beam spectra limits quantitative applications and standardization from achieving robust widespread success. This article reviews several quantitative applications of CT and the challenges they face, and describes the benefits brought by photon-counting detector (PCD) CT technology. The discussed benefits of PCD-CT include that it is inherently multienergy, expands material decomposition capabilities, and improves spatial resolution and geometric quantification. Further, the utility of virtual monoenergetic images to standardize CT numbers is discussed, as virtual monoenergetic images can be the default image type in PCD-CT due to the full-time spectral nature of the technology.

The recent technological developments in photon-counting detector computed tomography (PCD-CT) and the introduction of the first commercially available clinical PCD-CT unit open up new exciting opportunities for contrast media research. With PCD-CT, the efficacy of available iodine-based contrast media improves, allowing for a reduction of iodine dosage or, on the other hand, an improvement of image quality in low contrast indications. Virtual monoenergetic image reconstructions are routinely available and enable the virtual monoenergetic image energy to be adapted to the diagnostic task.A key property of PCD-CT is the ability of spectral separation in combination with improved material decomposition. Thus, the discrimination of contrast media from intrinsic or pathological tissues and the discrimination of 2 or more contrasting elements that characterize different tissues are attractive fields for contrast media research. For these approaches, K-edge imaging in combination with high atomic number elements such as the lanthanides, tungsten, tantalum, or bismuth plays a central role.The purpose of this article is to present an overview of innovative contrast media concepts that use high atomic number elements. The emphasis is on improving contrast enhancement for cardiovascular plaque imaging, stent visualization, and exploring new approaches using 2 contrasting elements. Along with the published research, new experimental findings with a contrast medium that incorporates tungsten are included.Both the literature review and the new experimental data demonstrate the great potential and feasibility for new contrast media to significantly increase diagnostic performance and to enable new clinical fields and indications in combination with PCD-CT.

The science and technology of X-ray optics have come far, enabling the focusing of X-rays for applications in high-resolution X-ray spectroscopy, imaging, and irradiation. In spite of this, many forms of tailoring waves that had substantial impact on applications in the optical regime have remained out of reach in the X-ray regime. This disparity fundamentally arises from the tendency of refractive indices of all materials to approach unity at high frequencies, making X-ray-optical components such as lenses and mirrors much harder to create and often less efficient. Here, we propose a new concept for X-ray focusing based on inducing a curved wavefront into the X-ray generation process, resulting in the intrinsic focusing of X-ray waves. This concept can be seen as effectively integrating the optics to be part of the emission mechanism, thus bypassing the efficiency limits imposed by X-ray optical components, enabling the creation of nanobeams with nanoscale focal spot sizes and micrometer-scale focal lengths. Specifically, we implement this concept by designing aperiodic vdW heterostructures that shape X-rays when driven by free electrons. The parameters of the focused hotspot, such as lateral size and focal depth, are tunable as a function of an interlayer spacing chirp and electron energy. Looking forward, ongoing advances in the creation of many-layer vdW heterostructures open unprecedented horizons of focusing and arbitrary shaping of X-ray nanobeams.

(1) Background: Photon-counting detector (PCD) CT offers a wide variety of kernels and sharpness levels for image reconstruction. The aim of this retrospective study was to determine optimal settings for coronary CT angiography (CCTA). (2) Methods: Thirty patients (eight female, mean age 63 ± 13 years) underwent PCD-CCTA in a high-pitch mode. Images were reconstructed using three different kernels and four sharpness levels (Br36/40/44/48, Bv36/40/44/48, and Qr36/40/44/48). To analyze objective image quality, the attenuation, image noise, contrast-to-noise ratio (CNR), and vessel sharpness were quantified in proximal and distal coronaries. For subjective image quality, two blinded readers assessed image noise, visually sharp reproduction of coronaries, and the overall image quality using a five-point Likert scale. (3) Results: Attenuation, image noise, CNR, and vessel sharpness significantly differed across kernels (all p < 0.001), with the Br-kernel reaching the highest attenuation. With increasing kernel sharpness, image noise and vessel sharpness increased, whereas CNR continuously decreased. Reconstruction with Br-kernel generally had the highest CNR (Br > Bv > Qr), except Bv-kernel had a superior CNR at sharpness level 40. Bv-kernel had significantly higher vessel sharpness than Br- and Qr-kernel (p < 0.001). Subjective image quality was rated best for kernels Bv40 and Bv36, followed by Br36 and Qr36. (4) Conclusion: Reconstructions with kernel Bv40 are beneficial to achieve optimal image quality in spectral high-pitch CCTA using PCD-CT.