ArticleProceedings of SPIE--the International Society for Optical Engineering2023
A task-specific deep-learning-based denoising approach for myocardial perfusion SPECT.
Article in Proceedings of SPIE--the International Society for Optical Engineering, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Sparse-view reconstruction and noise reduction in 99m Tc-prostate specific membrane antigen prostate imaging.Nuclear medicine communications · 2026Article
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Shorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views.EJNMMI physics · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Can patient-specific acquisition protocol improve performance on defect detection task in myocardial perfusion SPECT?Proceedings of SPIE--the International Society for Optical Engineering · 2024Article
- Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.Medical physics · 2023Article
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4 authors.
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Abstract
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.
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