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Deep-learning (DL)-based methods have shown significant promise in denoisingmyocardial perfusion SPECT images acquired at low dose. For clinicalapplication of these methods, evaluation on clinical tasks is crucial.Typically, these methods are designed to minimize some fidelity-based criterionbetween the predicted denoised image and some reference normal-dose image.However, while promising, studies have shown that these methods may havelimited impact on the performance of clinical tasks in SPECT. To address thisissue, we use concepts from the literature on model observers and ourunderstanding of the human visual system to propose a DL-based denoisingapproach designed to preserve observer-related information for detection tasks.The proposed method was objectively evaluated on the task of detectingperfusion defect in myocardial perfusion SPECT images using a retrospectivestudy with anonymized clinical data. Our results demonstrate that the proposedmethod yields improved performance on this detection task compared to usinglow-dose images. The results show that by preserving task-specific information,DL may provide a mechanism to improve observer performance in low-dosemyocardial perfusion SPECT.

A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT