Enhanced CT simulation using realistic vascular flow dynamics
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2024-04-01
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As medical technologies advance with increasing speed, virtual imaging trials (VITs) are emerging as a crucial tool in the evaluation and optimization of new imaging techniques. Widely used in many VITs is the four-dimensional extended cardiac-torso (XCAT) phantom, a comprehensive computational model that accurately represents human anatomy and physiology. While the XCAT phantom offers a powerful tool for imaging research, it offers only a limited model of blood flow to compartmentalized organs, potentially limiting the realism and clinical applicability of contrast-enhanced scan simulations. This study bridges that gap by combining realistic CT simulation with an accurate model of blood flow dynamics to enable more realistic simulations of contrast-enhanced imaging. To achieve this, a validated one-dimensional blood flow simulator, HARVEY1D, was used to model flow throughout the vessels of the XCAT phantom. DukeSim, a validated CT simulation platform, was then modified to incorporate the resulting flow into its simulations, thus enabling the generaon of simulated CT scans reflective of real-world blood-based contrast-enhanced imaging scenarios. To demonstrate the utility of this pipeline in an initial application to cardiac imaging, three heart models were studied: a non-diseased model, a 50% stenosis model, and an 80% stenosis model. Three seconds of contrast propagation were tracked in each heart model, and CT scans corresponding to two timepoints were simulated. Results demonstrated that the presence of stenosis significantly impacted blood flow, with greater resistance to blood flow leading to altered flow patterns visible in the simulated CT images. This work showcases a pipeline that leverages both computational fluid dynamics and medical imaging simulations to enhance the realism of virtual imaging trials and facilitate the evaluation, optimization, and development of diagnostic tools for contrast-enhanced imaging.
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Tanade, Cyrus, Nicholas Felice, Ehsan Samei, Amanda Randles and W Paul Segars (2024). Enhanced CT simulation using realistic vascular flow dynamics. Medical Imaging 2024: Physics of Medical Imaging, 12925. pp. 399–404. 10.1117/12.3006531 Retrieved from https://hdl.handle.net/10161/30512.
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Ehsan Samei
Dr. Ehsan Samei, PhD, DABR, FAAPM, FSPIE, FAIMBE, FIOMP, FACR is a Persian-American medical physicist. He is the Reed and Martha Rice Distinguished Professor of Radiology, and Professor of Medical Physics, Biomedical Engineering, Physics, and Electrical and Computer Engineering at Duke University. He serves as the Chief Imaging Physicist for Duke University Health System, the Director of the Carl E Ravin Advanced Imaging Laboratories and the Center for Virtual Imaging Trials (CVIT), and co-PI of one the five Centers of Excellence in Regulatory Science and Innovation (CERSI), Triangle CERSI. He is certified by the American Board of Radiology, recognized as a Distinguished Investigator by the Academy of Radiology Research, and awarded Fellow by five professional organization. He founded/co-founded the Duke Medical Physics Program, the Duke Imaging Physics Residency Program, the Duke Clinical Imaging Physics Group, the Center for Virtual Imaging Trials, and the Society of Directors of Academic Medical Physics Programs (SDAMPP). He has held senior leadership positions in the AAPM, SPIE, SDAMPP, and RSNA, including election to the presidency of the SEAAPM (2010-2011), SDAMPP (2011), and AAPM (2023).
Dr. Samei's scientific expertise include x-ray imaging, theoretical imaging models, simulation methods, and experimental techniques in medical image formation, quantification, and perception. His research aims to bridge the gap between scientific scholarship and clinical practice, in the meaningful realization of translational research, and in clinical processes that are informed by scientific evidence. He has advanced image quality and safety metrics and radiometrics that are clinically relevant and that can be used to design, optimize, and monitor interpretive and quantitative performance of imaging techniques. These have been implemented in advanced imaging performance characterization, procedural optimization, and clinical dose and quality analytics. His most recent research interests have been virtual clinical trial across a broad spectrum of oncologic, pulmonary, cardiac, and vascular diseases, and developing methodological advances that provide smart fusions of principle-informed and AI-based, data-informed approaches to scientific inquiry.
Dr. Samei has mentored over 140 trainees (graduate and postgraduate). He has more than 1400 scientific publications including more than 360 referred journal articles, 600 conference presentations, and 4 books. Citations to his work is reflected in an h-index of 74 and a Weighted Relative Citation Ratio of 613. His laboratory of over 20 researchers has been supported continuously over two decades by 44 extramural grants, culminating in a NIH Program Project grant in 2021 to establish the national Center for Virtual Imaging Trials (CVIT), joining a small number of prominent Biomedical Technology Research Centers across the nation. In 2023, he, along with 3 other PIs, was awarded to lead one of five national Centers of Excellence in Regulatory Science and Innovation (Triangle CERSI) by the FDA.
Amanda Randles
My research in biomedical simulation and high-performance computing focuses on the development of new computational tools that we use to provide insight into the localization and development of human diseases ranging from atherosclerosis to cancer.
William Paul Segars
Our current research involves the use of computer-generated phantoms and simulation techniques to investigate and optimize medical imaging systems and methods. Medical imaging simulation involves virtual experiments carried out entirely on the computer using computational models for the patients as well as the imaging devices. Simulation is a powerful tool for characterizing, evaluating, and optimizing medical imaging systems. A vital aspect of simulation is to have realistic models of the subject's anatomy as well as accurate models for the physics of the imaging process. Without this, the results of the simulation may not be indicative of what would occur in actual clinical studies and would, therefore, have limited practical value. We are leading the development of realistic simulation tools for use toward human and small animal imaging research.
These tools have a wide variety of applications in many different imaging modalities to investigate the effects of anatomical, physiological, physical, and instrumentational factors on medical imaging and to research new image acquisition strategies, image processing and reconstruction methods, and image visualization and interpretation techniques. We are currently applying them to the field of x-ray CT. The motivation for this work is the lack of sufficiently rigorous methods for optimizing the image quality and radiation dose in x-ray CT to the clinical needs of a given procedure. The danger of unnecessary radiation exposure from CT applications, especially for pediatrics, is just now being addressed. Optimization is essential in order for new and emerging CT applications to be truly useful and not represent a danger to the patient. Given the relatively high radiation doses required of current CT systems, thorough optimization is unlikely to ever be done in live patients. It would be prohibitively expensive to fabricate physical phantoms to simulate a realistic range of patient sizes and clinical needs especially when physiologic motion needs to be considered. The only practical approach to the optimization problem is through the use of realistic computer simulation tools developed in our work.
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