ArticleFrontiers in medicine2023
Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.EJNMMI physics · 2026Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- Recovering Image Quality in Low-Dose Pediatric Renal Scintigraphy Using Deep Learning.Journal of imaging · 2025Article
- Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.EJNMMI physics · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.ArXiv · 2024Article
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Authors and funding
7 authors.
Funding
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Abstract
Purpose: Deep learning-based denoising is promising for myocardial perfusion (MP) SPECT. However, conventional convolutional neural network (CNN)-based methods use fixed-sized convolutional kernels to convolute one region within the receptive field at a time, which would be ineffective for learning the feature dependencies across large regions. The attention mechanism (Att) is able to learn the relationships between the local receptive field and other voxels in the image. In this study, we propose a 3D attention-guided generative adversarial network (AttGAN) for denoising fast MP-SPECT images. Methods: Fifty patients who underwent 1184 MBq Results: All quantitative indices of AttGAN-based networks are superior to cGAN and Unet on all acquisition time images. AttGAN-def further improves AttGAN performance. The mean absolute error of PDS by AttcGAN-def was 1.60 on acquisition time of 1 s/prj, as compared to 2.36, 2.76, and 3.02 by AttGAN, cGAN, and Unet. Conclusion: Denoising based on AttGAN is superior to conventional CNN-based networks for MP-SPECT.
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