Coronary artery disease (CAD) is the leading cause of death in the US. Medical imaging is integral to the diagnosis and management of CAD fueling the development of new technologies and applications. However, imaging of the heart continues to be a challenging task. There is always a degree of temporal blur or motion artifact, the impact and documented limitations of which remain uncertain. Additionally, patient body habitus and technical limitations may contribute to high noise and degrade spatial resolution. For imaging modalities involving ionizing radiation like CT, radiation dose is also an ever-present reality that needs to be minimized without compromising image quality. Clinical trials are the best avenue for the evaluation of imaging technologies, but the ever-expanding number of technologies and parameters make a trial for every application or protocol unfeasible, pragmatically and financially. As a result, medical imaging researchers, industry, and the FDA are increasingly moving toward computerized simulations or `virtual trials'. Virtual trials involve the use of computational tools to perform experiments entirely on the computer. Realistic patient models or phantoms are combined with validated imaging simulations to emulate imaging examinations and patient conditions. These can subsequently be used to ascertain how differing patient attributes and imaging conditions impact dose, image quality, and depiction of pre-defined known conditions. The findings can be used to prescribe specific imaging protocols and optimal scan parameters that are customized to individual patient anatomy to provide a sufficient degree of certainty for effective clinical decision-making. The goal of this project is to develop, validate, and distribute to the research community a computational platform (including a series of anatomically variable phantoms with realistic finite-element cardiac models, accurate models for modern imaging devices, and a suite of image quality metrics) to perform virtual trials in dynamic cardiac imaging. The virtual framework can be extended to any number of cardiac conditions, imaging modalities, and technologies. As a first case study in our long-term strategy, the focus of this project is on CT as it has both a great need for and great potential to provide high spatial and temporal resolution for the optimized evaluation of CAD. If CT image quality is not optimal, the evaluation of CAD, particularly the degree of stenosis and characterization of high-risk plaque features, may be compromised. The tools we develop will provide the first practical platform to characterize the precise impact of the technical aspects of CT on image quality over a wide range of patient anatomies with the view to enable optimal visualization of cardiac conditions at the lowest possible radiation dose for a given patient. The approach has great potential to significantly improve clinical investigations of heart disease, extending beyond CT imaging and CAD, paving the way towards faster translation of new cardiac imaging technologies into the clinic and more precise and personalized patient management.

Public Health Relevance

The goal of this project is to develop, validate, and distribute a suite of simulation tools including realistic dynamic cardiac models, imaging simulation software, and a suite of image quality metrics for virtual cardiac imaging trials, enabling technological evaluations and optimization that would not be feasible using real human subjects. The project further demonstrates the first application of the framework to optimize dynamic cardiac CT imaging for the assessment of coronary artery disease.

National Institute of Health (NIH)
National Heart, Lung, and Blood Institute (NHLBI)
Research Project (R01)
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Biomedical Imaging Technology Study Section (BMIT)
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Luo, James
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Duke University
Schools of Medicine
United States
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Richards, Taylor; Sturgeon, Gregory M; Ramirez-Giraldo, Juan Carlos et al. (2018) Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model. J Med Imaging (Bellingham) 5:013501