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

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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 for improvement of task performance at reduced counts.