Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?

Abstract

Myocardial perfusion imaging using single-photon emission computed tomography (SPECT), or myocardial perfusion SPECT (MPS) is a widely used clinical imaging modality for the diagnosis of coronary artery disease. Current clinical protocols for acquiring and reconstructing MPS images are similar for most patients. However, for patients with outlier anatomical characteristics, such as large breasts, images acquired using conventional protocols are often sub-optimal in quality, leading to degraded diagnostic accuracy. Solutions to improve image quality for these patients outside of increased dose or total acquisition time remain challenging. Thus, there is an important need for new methodologies to improve image quality for such patients. One approach to improving this performance is adapting the image acquisition protocol specific to each patient. For this study, we first designed and implemented a personalized patient-specific protocol-optimization strategy, which we term precision SPECT (PRESPECT). This strategy integrates ideal observer theory with the constraints of tomographic reconstruction to optimize the acquisition time for each projection view, such that MPS defect detection performance is maximized. We performed a clinically realistic simulation study on patients with outlier anatomies on the task of detecting perfusion defects on various realizations of low-dose scans by an anthropomorphic channelized Hotelling observer. Our results show that using PRESPECT led to improved performance on the defect detection task for the considered patients. These results provide evidence that personalization of MPS acquisition protocol has the potential to improve defect detection performance, motivating further research to design optimal patient-specific acquisition and reconstruction protocols for MPS, as well as developing similar approaches for other medical imaging modalities.

Locations

• arXiv (Cornell University) - View - PDF