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

There is an important need for methods to reduce radiation dose and imaging time in myocardial perfusion imaging (MPI) SPECT. Deep learning (DL) methods have demonstrated promise in predicting normal-count images from low-count images for MPI SPECT, but the methods that have been objectively evaluated on the clinical task of detecting perfusion defects have not shown improved performance compared with low-count images. To address this need, we build upon concepts from model-observer theory and our understanding of the human visual system to propose a Detection task-specific DL-based approach for denoising MPI SPECT images (DEMIST). The approach, while performing denoising, is designed to preserve features that are known to impact observer performance on detection tasks. We objectively evaluated the proposed method on the task of detecting perfusion defects using a retrospective study with anonymized clinical data in patients who underwent MPI studies (N = 338). Performance on the task of detecting perfusion defects was quantified with an anthropomorphic channelized Hotelling observer. Images denoised with DEMIST yielded significantly improved detection performance compared to the corresponding low-dose images and images denoised with a commonly used task-agnostic DL-based denoising method. Similar results were observed with stratified analysis based on patient sex and defect type. Additionally, the proposed method significantly improved performance compared to the low-dose images in terms of the task-agnostic metrics of root mean squared error and structural similarity index metric. A mathematical analysis reveals that DEMIST preserves detection-task-specific features while improving the noise properties, thus resulting in improved observer performance. The results provide strong evidence for further clinical evaluation of DEMIST to denoise low-count images in MPI SPECT.