ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2023
Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
- SPECT and PET imaging of Alzheimer's disease revisited: from biomarkers to artificial intelligence-based prediction.Annals of nuclear medicine · 2026Review
- Clinical Applications of Artificial Intelligence in Cardiovascular Imaging: Where Do We Stand?Life (Basel, Switzerland) · 2026Review
- SMGDiff: step mapping generalized diffusion model for efficient noise reduction in cardiac-gated myocardial perfusion SPECT images.EJNMMI physics · 2026Article
- Investigation of Attenuation Correction Methods for Dual-Gated Single Photon Emission Computed Tomography (DG-SPECT).Bioengineering (Basel, Switzerland) · 2025Article
- 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
- Deep learning-based low count whole-body positron emission tomography denoising incorporating computed tomography priors.Quantitative imaging in medicine and surgery · 2024Article
- SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.EJNMMI physics · 2024Article
- Deep learning-based multi-frequency denoising for myocardial perfusion SPECT.EJNMMI physics · 2024Article
- Deep generative denoising networks enhance quality and accuracy of gated cardiac PET data.Annals of nuclear medicine · 2024Article
- DuDoCFNet: Dual-Domain Coarse-to-Fine Progressive Network for Simultaneous Denoising, Limited-View Reconstruction, and Attenuation Correction of Cardiac SPECT.IEEE transactions on medical imaging · 2024Article
- A Review on Low-Dose Emission Tomography Post-Reconstruction Denoising with Neural Network Approaches.IEEE transactions on radiation and plasma medical sciences · 2024Article
- DEMIST: A Deep-Learning-Based Detection-Task-Specific Denoising Approach for Myocardial Perfusion SPECT.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
- Comparison of deep learning-based denoising methods in cardiac SPECT.EJNMMI physics · 2023Article
- Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.Frontiers in medicine · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundLow-dose (LD) myocardial perfusion (MP) SPECT suffers from high noise level, leading to compromised diagnostic accuracy. Here we investigated the denoising performance for MP-SPECT using a conditional generative adversarial network (cGAN) in projection-domain (cGAN-prj) and reconstruction-domain (cGAN-recon).
methodsSixty-four noisy SPECT projections were simulated for a population of 100 XCAT phantoms with different anatomical variations and
resultscGAN denoising improves image quality as compared to LD and conventional post-reconstruction filtering. cGAN-prj can further reduce the dose level as compared to cGAN-recon without compromising the image quality.
conclusionsDenoising based on cGAN-prj is superior to cGAN-recon for MP-SPECT.
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Registered trials
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