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