# Precision Cardiac CT:  Development of a Computational Platform for Optimizing Imaging

> **NIH NIH R01** · DUKE UNIVERSITY · 2020 · $663,328

## Abstract

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 ...

## Key facts

- **NIH application ID:** 9888402
- **Project number:** 5R01HL131753-04
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Ehsan Samei
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $663,328
- **Award type:** 5
- **Project period:** 2017-03-15 → 2023-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/9888402

## Citation

> US National Institutes of Health, RePORTER application 9888402, Precision Cardiac CT:  Development of a Computational Platform for Optimizing Imaging (5R01HL131753-04). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/9888402. Licensed CC0.

---

*[NIH grants dataset](/datasets/nih-grants) · CC0 1.0*