Medical Imaging Technology Research Articles

UTAC-Net: A Semantic Segmentation Model for Computer-Aided Diagnosis for Ischemic Region Based on Nuclear Medicine Cerebral Perfusion Imaging

Cerebral ischemia has a high morbidity and disability rate. Clinical diagnosis is mainly made by radiologists manually reviewing cerebral perfusion images to determine whether cerebral ischemia is present. The number of patients with cerebral ischemia has risen dramatically in recent years, which has brought a huge workload for radiologists. In order to improve the efficiency of diagnosis, we develop a neural network for segmenting cerebral ischemia regions in perfusion images. Combining deep learning with medical imaging technology, we propose a segmentation network, UTAC-Net, based on U-Net and Transformer, which includes a contour-aware module and an attention branching fusion module, to achieve accurate segmentation of cerebral ischemic regions and correct identification of ischemic locations. Cerebral ischemia datasets are scarce, so we built a relevant dataset. The results on the self-built dataset show that UTAC-Net is superior to other networks, with the mDice of UTAC-Net increasing by 9.16% and mIoU increasing by 14.06% compared with U-Net. The output results meet the needs of aided diagnosis as judged by radiologists. Experiments have demonstrated that our algorithm has higher segmentation accuracy than other algorithms and better assists radiologists in the initial diagnosis, thereby reducing radiologists’ workload and improving diagnostic efficiency.

BeFOI: A Novel Method Based on Conditional Diffusion Model for Medical Image Denoising

The progress in medical imaging technology highlights the importance of image quality for effective diagnosis and treatment. Yet, noise during capture and transmission can compromise image accuracy and reliability, complicating clinical decisions. The rising interest in diffusion models has led to their exploration of denoising images. We present Be-FOI (Better Fluoro Images), a weakly supervised model that uses cine images to denoise fluoroscopic images, both DR types. Trained through precise noise estimation and simulation, BeFOI employs Markov chains to denoise using only the fluoroscopic image as guidance. Our tests show that BeFOI outperforms other methods, reducing noise and enhancing clarity and diagnostic utility, making it an effective post-processing tool for medical images.

Advantages of Nail Surgical Procedures Using a 3D Biomodel

<b><i>Introduction:</i></b> 3D biomodels represent a cutting-edge advancement in medical imaging technology. The incorporation of 3D technologies in dermatology through the acquisition of onychological images, 3D reconstruction, and development of customized equipment to assist in surgeries demonstrated reduction in operating times and improved surgical outcomes. Additionally, the use of 3D printing in surgical simulation provided a safe environment for training and education. This article explores the application of 3D biomodels in dermatology, focusing on three clinical cases involving nail tumors. <b><i>Case Presentation:</i></b> In case 1, a glomus tumor was visualized in 3D, guiding the creation of a personalized surgical device. The minimally invasive surgery, facilitated by the biomodel, resulted in successful tumor removal. Case 2, featuring a subungual keratoacanthoma, utilized 3D biomodels for conservative surgery planning, anatomical comprehension, and patient communication. Case 3 involved a longitudinal groove, where biomodels aided in precise lesion localization and surgical planning. <b><i>Conclusion:</i></b> The integration of virtual and physical anatomical biomodels proves valuable in surgical dermatology, contributing to enhanced treatment quality, patient safety, and medical education.

Clinical application and development of OCT and OCTA in fundus diseases

Optical coherence tomography (OCT), which uses light wave as the imaging method, is a new medical imaging technology based on optical coherence. Optical coherence tomography angiography (OCTA) is a non-invasive imaging method gradually emerging in recent years. This technique can detect flowing blood and evaluate tissue morphology by detecting normal flow of red blood cells in blood vessels. Because of the advantages of high resolution, easy operation and three-dimensional imaging, it is widely used in the diagnosis and evaluation of fundus diseases (such as diabetic retinopathy, choroidal neovascularization, retinal vein occlusion, etc.). This article reviews the application status and progress of OCT and OCTA in clinical diagnosis and treatment of fundus diseases.

A review of self-supervised, generative, and few-shot deep learning methods for data-limited magnetic resonance imaging segmentation.

Magnetic resonance imaging (MRI) is a ubiquitous medical imaging technology with applications in disease diagnostics, intervention, and treatment planning. Accurate MRI segmentation is critical for diagnosing abnormalities, monitoring diseases, and deciding on a course of treatment. With the advent of advanced deep learning frameworks, fully automated and accurate MRI segmentation is advancing. Traditional supervised deep learning techniques have advanced tremendously, reaching clinical-level accuracy in the field of segmentation. However, these algorithms still require a large amount of annotated data, which is oftentimes unavailable or impractical. One way to circumvent this issue is to utilize algorithms that exploit a limited amount of labeled data. This paper aims to review such state-of-the-art algorithms that use a limited number of annotated samples. We explain the fundamental principles of self-supervised learning, generative models, few-shot learning, and semi-supervised learning and summarize their applications in cardiac, abdomen, and brain MRI segmentation. Throughout this review, we highlight algorithms that can be employed based on the quantity of annotated data available. We also present a comprehensive list of notable publicly available MRI segmentation datasets. To conclude, we discuss possible future directions of the field-including emerging algorithms, such as contrastive language-image pretraining, and potential combinations across the methods discussed-that can further increase the efficacy of image segmentation with limited labels.

AI Empowered of Advancements in Microbial and Tumor Cell Image Labeling for Enhanced Medical Insights

The traditional diagnosis model of tumor pathology depends on the experience of the doctor, which is inevitably subjective. With the rapid development of modern medical imaging, medical imaging technology has formed a medical imaging system composed of UI, CT, CR, DR, MRI, PET, PET-CT, digital subtraction angiography and PACS by traditional single ordinary X-ray and angiography. The continuous enrichment of imaging technology has changed medical imaging from "auxiliary examination means" to the most important clinical diagnosis and differential diagnosis method in modern medicine. With the application of digital scan sections in clinicopathology, the computer technology of artificial intelligence (AI) assisted diagnosis has developed rapidly in the analysis of tumor tissue images. This paper summarizes the application progress of AI in tumor pathology, and describes the exploration and application of AI in the field of quantitative analysis of histopathological molecular markers closely related to clinical diagnosis and treatment in recent years, so as to provide useful reference for the development of tumor intelligent diagnosis and treatment model.

Mimicking Motor Proteins: Wall-Guided Self-Navigation of Microwheels.

Untethered micro/nanorobots (MNRs) show great promise in biomedicine. However, high-precision targeted in vivo navigation of MNRs into both deep and tiny microtube networks comes with big challenges because the present medical imaging cannot simultaneously meet the requirements of high resolution, high penetration depth, and high real-time performance. Inspired by intracellular motor proteins that transport cargo along cytoskeletal tracks, this study proposed a microtube inwall-guided targeted self-navigation strategy of magnetic microwheels (μ-wheels) that relies only on interactions with a microtube inwall, compared to conventional techniques that rely on real-time imaging and tracking of MNRs. By presetting the direction of the rotating magnetic field, the μ-wheel realized targeted navigation along the inwall. The propulsion principles behind it are elaborated. The targeted self-navigation of the μ-wheels in three-dimensional microtube networks, a spiral microtube, and an intrahepatic bile duct of a pig was conducted. Lastly, based on the strategy, a practical tumor early detection method was proposed and verified by means of magnetic resonance imaging. The microtube inwall-guided targeted self-navigation strategy reduces the dependence of in vivo targeted navigation of MNRs on the real-time performance of medical imaging technology and greatly contributes to the development of MNRs in biomedical applications.

Three-dimensional visualization of thyroid ultrasound images based on multi-scale features fusion and hierarchical attention.

Ultrasound three-dimensional visualization, a cutting-edge technology in medical imaging, enhances diagnostic accuracy by providing a more comprehensive and readable portrayal of anatomical structures compared to traditional two-dimensional ultrasound. Crucial to this visualization is the segmentation of multiple targets. However, challenges like noise interference, inaccurate boundaries, and difficulties in segmenting small structures exist in the multi-target segmentation of ultrasound images. This study, using neck ultrasound images, concentrates on researching multi-target segmentation methods for the thyroid and surrounding tissues. We improved the Unet++ to propose PA-Unet++ to enhance the multi-target segmentation accuracy of the thyroid and its surrounding tissues by addressing ultrasound noise interference. This involves integrating multi-scale feature information using a pyramid pooling module to facilitate segmentation of structures of various sizes. Additionally, an attention gate mechanism is applied to each decoding layer to progressively highlight target tissues and suppress the impact of background pixels. Video data obtained from 2D ultrasound thyroid serial scans served as the dataset for this paper.4600 images containing 23,000 annotated regions were divided into training and test sets at a ratio of 9:1, the results showed that: compared with the results of U-net++, the Dice of our model increased from 78.78% to 81.88% (+ 3.10%), the mIOU increased from 73.44% to 80.35% (+ 6.91%), and the PA index increased from 92.95% to 94.79% (+ 1.84%). Accurate segmentation is fundamental for various clinical applications, including disease diagnosis, treatment planning, and monitoring. This study will have a positive impact on the improvement of 3D visualization capabilities and clinical decision-making and research in the context of ultrasound image.

Machine Learning-Based Medical Imaging Detection and Diagnostic Assistance

With the development of medical technology, medical imaging technology plays an increasingly important role in diagnosing and monitoring diseases. At the same time, machine learning technology has shown strong capabilities in data processing and analysis, providing new possibilities for the detection and auxiliary diagnosis of medical images. This paper will deeply discuss the application of machine learning in medical images, especially the application of deep learning in medical images, and how to achieve effective medical image detection and auxiliary diagnosis through steps such as preprocessing, feature extraction, model training and optimization.

Knowledge mapping of application of image-guided surgery in prostate cancer: a bibliometric analysis (2013-2023).

Image-guided surgery (IGS) refers to surgery navigated by medical imaging technology, helping doctors better clarify tumor boundaries, identify metastatic lymph nodes and preserve surrounding healthy tissue function. Recent studies have provided expectable momentum of the application of IGS in prostate cancer (PCa). We aim to comprehensively construct a bibliometric analysis of the application of IGS in PCa. We searched publications related to application of IGS in PCa from 2013 to 2023 on the web of science core collection (WoSCC) databases. VOSviewer, CiteSpace and R package "bibliometrix" were used for bibliometric analysis. 2, 389 articles from 75 countries and 2, 883 institutions led by the United States were included. The number of publications related to the application of IGS in PCa kept high in the last decade. Johns Hopkins University is the top research institutions. Journal of Nuclear Medicine has the highest popularity as the selection of journal and co-cited journal. Pomper Martin G. had published the most paper. Ali Afshar-Oromieh was co-cited most frequently. The clinical efficacy of PSMA-PET/CT in PCa diagnosis and treatment are main topics in this research field, with emerging focuses on the use of fluorescence imaging guidance technology in PCa. "PSMA" and "PET/CT" are the main keywords as long-term research hotspots. This study is the first bibliometric analysis of researches on application of IGS in PCa with 3 recognized bibliometric software, providing an objective description and comprehensive guidance for the future relevant investigations.

Integrating hybrid transfer learning with attention-enhanced deep learning models to improve breast cancer diagnosis.

Cancer, with its high fatality rate, instills fear in countless individuals worldwide. However, effective diagnosis and treatment can often lead to a successful cure. Computer-assisted diagnostics, especially in the context of deep learning, have become prominent methods for primary screening of various diseases, including cancer. Deep learning, an artificial intelligence technique that enables computers to reason like humans, has recently gained significant attention. This study focuses on training a deep neural network to predict breast cancer. With the advancements in medical imaging technologies such as X-ray, magnetic resonance imaging (MRI), and computed tomography (CT) scans, deep learning has become essential in analyzing and managing extensive image datasets. The objective of this research is to propose a deep-learning model for the identification and categorization of breast tumors. The system's performance was evaluated using the breast cancer identification (BreakHis) classification datasets from the Kaggle repository and the Wisconsin Breast Cancer Dataset (WBC) from the UCI repository. The study's findings demonstrated an impressive accuracy rate of 100%, surpassing other state-of-the-art approaches. The suggested model was thoroughly evaluated using F1-score, recall, precision, and accuracy metrics on the WBC dataset. Training, validation, and testing were conducted using pre-processed datasets, leading to remarkable results of 99.8% recall rate, 99.06% F1-score, and 100% accuracy rate on the BreakHis dataset. Similarly, on the WBC dataset, the model achieved a 99% accuracy rate, a 98.7% recall rate, and a 99.03% F1-score. These outcomes highlight the potential of deep learning models in accurately diagnosing breast cancer. Based on our research, it is evident that the proposed system outperforms existing approaches in this field.

New Vessel Extraction Method by Using Skew Normal Distribution for MRA Images

Vascular-related diseases pose significant public health challenges and are a leading cause of mortality and disability. Understanding the complex structure of the vascular system and its processes is crucial for addressing these issues. Recent advancements in medical imaging technology have enabled the generation of high-resolution 3D images of vascular structures, leading to a diverse array of methods for vascular extraction. While previous research has often assumed a normal distribution of image data, this paper introduces a novel vessel extraction method that utilizes the skew normal distribution for more accurate probability distribution modeling. The proposed method begins with a preprocessing step to enhance vessel structures and reduce noise in Magnetic Resonance Angiography (MRA) images. The skew normal distribution, known for its ability to model skewed data, is then employed to characterize the intensity distribution of vessels. By estimating the parameters of the skew normal distribution using the Expectation-Maximization (EM) algorithm, the method effectively separates vessel pixels from the background and non-vessel regions. To extract vessels, a thresholding technique is applied based on the estimated skew normal distribution parameters. This segmentation process enables accurate vessel extraction, particularly in detecting thin vessels and enhancing the delineation of vascular edges with low contrast. Experimental evaluations on a diverse set of MRA images demonstrate the superior performance of the proposed method compared to previous approaches in terms of accuracy and computational efficiency. The presented vessel extraction method holds promise for improving the diagnosis and treatment of vascular-related diseases. By leveraging the skew normal distribution, it provides accurate and efficient vessel segmentation, contributing to the advancement of vascular imaging in the field of medical image analysis.

Development and validation of an ultrasound‑based radiomics nomogram to predict lymph node status in patients with high-grade serous ovarian cancer: a retrospective analysis

BackgroundDespite advances in medical imaging technology, the accurate preoperative prediction of lymph node status remains challenging in ovarian cancer. This retrospective study aimed to investigate the feasibility of using ultrasound-based radiomics combined with preoperative clinical characteristics to predict lymph node metastasis (LNM) in patients with high-grade serous ovarian cancer (HGSOC).ResultsPatients with 401 HGSOC lesions from two institutions were enrolled: institution 1 for the training cohort (n = 322) and institution 2 for the external test cohort (n = 79). Radiomics features were extracted from the three preoperative ultrasound images of each lesion. During feature selection, primary screening was first performed using the sample variance F-value, followed by recursive feature elimination (RFE) to filter out the 12 most significant features for predicting LNM. The radscore derived from these 12 radiomic features and three clinical characteristics were used to construct a combined model and nomogram to predict LNM, and subsequent 10-fold cross-validation was performed. In the test phase, the three models were tested with external test cohort. The radiomics model had an area under the curve (AUC) of 0.899 (95% confidence interval [CI]: 0.864–0.933) in the training cohort and 0.855 (95%CI: 0.774–0.935) in the test cohort. The combined model showed good calibration and discrimination in the training cohort (AUC = 0.930) and test cohort (AUC = 0.881), which were superior to those of the radiomic and clinical models alone.ConclusionsThe nomogram consisting of the radscore and preoperative clinical characteristics showed good diagnostic performance in predicting LNM in patients with HGSOC. It may be used as a noninvasive method for assessing the lymph node status in these patients.

PREDICTING CARDIAC ISSUES FROM ECHOCARDIOGRAMS: A LITERATURE REVIEW USING DEEP LEARNING AND MACHINE LEARNING TECHNIQUES

Cardiovascular disease (CVD) has a substantial impact on overall health, well-being, and life-expectancy. Echocardiography is a widely used imaging technique in cardiovascular medicine, utilizing various medical imaging technology to visualize heart chambers and valve’s motion activity. In order to diagnose and treat complicated cardiovascular problems, it takes high-resolution images of the heart and its surroundings. However, it has limitations such as long procedure times, multiple measurement values, complex analyses, individualized assessments, operator subjectivity, and wide observation ranges. This makes it challenging for sonographers to accurately detect and diagnose heart diseases. In recent days, Deep Learning (DL) is increasingly used in clinical computer-assisted systems for disease detection, feature segmentation, functional evaluation, and diagnosis. It is an alternate technique for accurate detection and treatment of cardiovascular disorders; it improves the diagnostic capacities of echocardiography by identifying pathological conditions, extracting anatomically significant data, measuring cardio-motion, and calculating echo image quality. This paper presents a detailed review of various DL frameworks developed to analyse different cardiac views using echocardiography for improving the prediction and diagnosis of CVD. At the outset, a variety of echocardiography systems linked with DL-based segmentation and classification are reviewed briefly. Afterwards, a comparison research is carried out to gain insight into the shortcomings of those algorithms and provide a fresh approach to improve the accuracy of cardiac view categorization in echocardiography systems.

Enhancing Airway Assessment with a Secure Hybrid Network-Blockchain System for CT & CBCT Image Evaluation

Our investigation explored the intricacies of airway evaluation through Cone-Beam Computed Tomography (CBCT) and Computed Tomography (CT) images. By employing innovative data augmentation strategies, we expanded our dataset significantly, enabling a more comprehensive analysis of airway characteristics. The utility of these techniques was evident in their ability to yield a diverse array of synthetic images, each representing different airway scenarios with high fidelity. A notable outcome of our study was the effective categorization of the initial image as "Class II" under the Mallampati Classification system. The augmented images further enhanced our understanding by exhibiting a spectrum of airway parameters. Moreover, our approach included training a Recurrent Neural Network (RNN) model on a dataset of CT images. This model, fortified with pseudo-labels created via K-means clustering, showcased its proficiency by accurately predicting airway assessment categories in various test scenarios. These results underscore the model's potential as a tool for swift and precise airway evaluation in clinical settings, marking a significant advancement in medical imaging technologies.

Impact of electrical aging on x-ray image quality and dose rate with vertically aligned carbon nanotube based cold cathode electron beam (C-beam)

The adoption of vertically aligned carbon nanotubes (VACNTs) as electron emitters in x-ray generation has opened a new path for medical imaging technology advancement. With their outstanding electron emission capabilities, VACNTs provide a distinct advantage in miniaturizing and improving the performance of x-ray devices. This research focuses on the effect of electrical aging on x-ray imaging quality and the dose rate while using VACNTs as the electron source. The study includes a thorough examination of the electrical aging effects on VACNT-based x-ray systems, with an emphasis on changes in emission characteristics, beam stability, and the resulting variations in x-ray output. Experiment results show that electrical aging has a considerable impact on the performance of VACNT-based x-ray sources, with visible changes in electron emission parameters and subsequent consequences on x-ray imaging quality. Furthermore, the study investigates the relationship between electrical aging and the x-ray dose rate, providing vital insights into radiation exposure optimization in medical diagnostics.

Enhancing Nuclear Medicine Practice in Saudi Arabia: Advocating for Comprehensive Guidelines and Local Diagnostic Reference Levels.

Diagnostic reference levels(DRLs) were initially introducedbythe International Commission on Radiation Protection (ICRP). It refers to the measured quantity ofadministered activity (MBq) in nuclear medicine imaging studies and is a type of investigation level. DRL is recommended to prevent excessive radiation exposure to patients while maintaining adequate image quality. It should not be implemented as a dose constraint or dose limit.The Saudi Food and Drug Authority (SFDA) is the primary government body responsible for reporting national diagnostic reference levels (NDRLs) for diagnostic medical imaging technologies in Saudi Arabia. Only NDRLs for computed tomography, general X-ray, and mammography have been published and enforced locally.This study aims to establish local DRLs for nuclear medicine imaging procedures at King Abdulaziz University Hospital, Saudi Arabia, preparing for compliance proof once required by local authorities. Data were collected from all machines, and sixcommon protocols were studied, with datafrom 50 patientsof standard body size for each identified protocol. The study was conducted retrospectively, and the 50thpercentile was then calculated for each scan. Both protocols for renal scansadministered the lowest doses to patients (130 MBq and 148 MBq), respectively. The highest dose administered to patients was found to be in bone scans (1110 MBq). The study establishes local DRLs for nuclear medicine imaging in our institution. Median activity dosages in renal, thyroid, and parathyroid imaging were comparable to locally and internationally published DRLs. However, they are higher in cardiac and bone imaging comparedto local Saudi DRL and DRL in the European Union and the USA, likely due to the adopted protocols. These highlight the need for modifying the protocols to fulfill optimization efforts. These findings serve as a foundation for compliance with future regulatory requirements, ensuring patient safety and maintaining imaging quality in Saudi healthcare.

Adaptive Gaussian Wiener Filter for CT-Scan Images with Gaussian Noise Variance

Medical imaging plays an important role in modern healthcare, with Computed Tomography (CT) being essential for high-resolution cross-sectional imaging. However, Gaussian noise often occurs within the CT scan images and makes it difficult for image interpretation and reduces the diagnostic accuracy, creating a significant obstacle to fully utilizing CT scanning technology. Existing denoising techniques have a hard time balance between noise reduction and preserving the important image details, failing to enable the optimal diagnostic precision. This study introduces Adaptive Gaussian Wiener Filter (AGWF), a novel filter aims to denoise CT scan images that have been corrupted with various Gaussian noise variance without compromising the image details. The AGWF combines the Gaussian filter for initial noise reduction, followed by the implementation of Wiener filter, which can adaptively estimate noise variance and signal power in localized regions. This approach not only outperforms other existing techniques but also showcases a remarkable balance between noise reduction and image detail preservation. The experiment evaluates 300 images from the dataset and each image is corrupted with Gaussian noise variance to ensure a comprehensive evaluation of the AGWF’s performance. The evaluation indicated that AGWF can improve the Signal-to-Noise Ratio (SNR) value and reduce the Root Mean Square Error (RMSE) and Mean Square Error (MSE) value, showing a qualitative improvement in CT scan imagery. The proposed method holds promising potential for advancing medical imaging technology with the implementation of deep learning.

Classification of the Relative Position between the Third Molar and the Inferior Alveolar Nerve Using a Convolutional Neural Network Based on Transfer Learning

In recent years, there has been a significant increase in collaboration between medical imaging and artificial intelligence technology. The use of automated techniques for detecting medical symptoms has become increasingly prevalent. However, there has been a lack of research on the relationship between impacted teeth and the inferior alveolar nerve (IAN) in DPR images. The severe compression of teeth against the IAN may necessitate the requirement for nerve canal treatment. To reduce the occurrence of such events, this study aims to develop an auxiliary detection system capable of precisely locating the relative positions of the IAN and impacted teeth through object detection and image enhancement. This system is designed to shorten the duration of examinations for dentists while concurrently mitigating the chances of diagnostic errors. The innovations in this research are as follows: (1) using YOLO_v4 to identify impacted teeth and the IAN in DPR images achieves an accuracy of 88%. However, the developed algorithm in this study achieves an accuracy of 93%. (2) Image enhancement is utilized in this study to expand the dataset, with an accuracy of up to 2~3% enhancement in detecting diseases. (3) The segmentation technique proposed in this study surpasses previous methods by achieving 6% higher accuracy in dental diagnosis.

Development of physiologically-informed computational coronary artery plaques for use in virtual imaging trials.

As a leading cause of death, worldwide, cardiovascular disease is of great clinical importance. Among cardiovascular diseases, coronary artery disease (CAD) is a key contributor, and it is the attributed cause of death for 10% of all deaths annually. The prevalence of CAD is commensurate with the rise in new medical imaging technologies intended to aid in its diagnosis and treatment. The necessary clinical trials required to validate and optimize these technologies require a large cohort of carefully controlled patients, considerable time to complete, and can be prohibitively expensive. A safer, faster, less expensive alternative is using virtual imaging trials (VITs), utilizing virtual patients or phantoms combined with accurate computer models of imaging devices. In this work, we develop realistic, physiologically-informed models for coronary plaques for application in cardiac imaging VITs. Histology images of plaques at micron-level resolution were used to train a deep convolutional generative adversarial network (DC-GAN) to create a library of anatomically variable plaque models with clinical anatomical realism. The stability of each plaque was evaluated by finite element analysis (FEA) in which plaque components and vessels were meshed as volumes, modeled as specialized tissues, and subjected to the range of normal coronary blood pressures. To demonstrate the utility of the plaque models, we combined them with the whole-body XCAT computational phantom to perform initial simulations comparing standard energy-integrating detector (EID) CT with photon-counting detector (PCD) CT. Our results show the network is capable of generating realistic, anatomically variable plaques. Our simulation results provide an initial demonstration of the utility of the generated plaque models as targets to compare different imaging devices. Vast, realistic, and variable CAD pathologies can be generated to incorporate into computational phantoms for VITs. There they can serve as a known truth from which to optimize and evaluate cardiac imaging technologies quantitatively.