# Optimization of diagnostic accuracy, radiation dose, and patient throughput for cardiac SPECT via advanced and clinically practical cardiac-respiratory motion correction and deep learning

> **NIH NIH R01** · ILLINOIS INSTITUTE OF TECHNOLOGY · 2020 · $786,840

## Abstract

Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is widely used
to detect and evaluate coronary artery disease. The goal of this project is to reduce the radiation dose and/or
scan time of SPECT MPI by a combined factor of 16x, while maintaining or increasing diagnostic accuracy. This
would enable SPECT MPI to be performed, e.g., with 4x reduced radiation dose and 4x shorter scan time (~2.5
minutes) than typical protocols. Radiation dose in SPECT MPI has been recognized as an important
issue, accounting for ~25% of all radiation exposure to patients in medical imaging. Dose reduction
particularly addresses the increased prevalence of obese patients (who receive higher dose) and younger
cardiac patients (whose radiation risk is higher due to longer life expectancy). Reduction in scan time
would improve comfort for elderly and infirm cardiac patients, while mitigating body-motion image
artifacts and reducing healthcare costs by increasing clinical throughput. We will reduce dose and scan time
through innovative image reconstruction methods that involve little or no cost and require no additional
patient setup steps. We will employ new respiratory and cardiac motion compensation to reduce image
artifacts, as well as new deep learning techniques, which will be used for both respiratory-signal estimation
and high-performance denoising. We will methodically optimize these techniques and then validate our
algorithms in multicenter clinical reader studies.
SA1: Develop clinically practical respiratory motion surrogates for low-count studies. T1: Perfect data-
driven respiratory surrogate estimation; T2: Optimize data-driven surrogate estimation at reduced counts; T3:
Develop and clinically validate depth-sensing cameras for respiratory and body-motion surrogate estimation;
T4: Generalization of data-driven surrogate estimation to SPECT systems not having a CT.
SA2: Develop deep-learning reconstruction methods and optimize for diagnostic accuracy and dose/scan
time. T1: Post-reconstruction DL denoising algorithms for 3D perfusion images for reduced-count and standard-
count studies; T2: DL denoising algorithms for 4D cardiac-gated studies; T3: 4D reconstruction with embedded
DL denoising, cardiac motion estimation and correction; and T4: DL reconstruction methods with both RMC
and CMC, with projection data binned using respiratory surrogate signals derived in SA1.
SA3: Perform multicenter clinical reader studies (6 clinicians, 3 institutions) to validate the new
algorithms and compare to current clinically-available methods based on diagnostic performance and
repeatability in assessing both perfusion and wall motion defects. T1: In comparison to baseline clinical
reconstruction, evaluate added benefit of: a) including attenuation and scatter correction, and b) additionally
including RMC; T2: Validate DL for improvement of perfusion and function (wall motion) task performance at
full-count levels; and T3: Validate DL fo...

## Key facts

- **NIH application ID:** 10072432
- **Project number:** 1R01HL154687-01
- **Recipient organization:** ILLINOIS INSTITUTE OF TECHNOLOGY
- **Principal Investigator:** Michael A King
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $786,840
- **Award type:** 1
- **Project period:** 2020-07-01 → 2024-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10072432

## Citation

> US National Institutes of Health, RePORTER application 10072432, Optimization of diagnostic accuracy, radiation dose, and patient throughput for cardiac SPECT via advanced and clinically practical cardiac-respiratory motion correction and deep learning (1R01HL154687-01). Retrieved via AI Analytics 2026-05-21 from https://api.ai-analytics.org/grant/nih/10072432. Licensed CC0.

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