ArticleMedical physics2021
Deep learning with noise-to-noise training for denoising in SPECT myocardial perfusion imaging.
Article in Medical physics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- EANM practice guideline for quantitative SPECT-CT.European journal of nuclear medicine and molecular imaging · 2023Guideline
- Artificial Intelligence Across the Cancer Theranostics Workflow: Critical Appraisal of Current Evidence and Future Clinical Translation.Molecular imaging and biology · 2026Review
- Shorter SPECT scans using self-supervised coordinate learning to synthesize skipped projection views.EJNMMI physics · 2025Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Deep learning-based approach for acquisition time reduction in ventilation SPECT in patients after lung transplantation.Radiological physics and technology · 2025Article
- Review
- SPECT-MPI iterative denoising during the reconstruction process using a two-phase learned convolutional neural network.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
- Impact of the Reference Multiple-Time-Point Dosimetry Protocol on the Validity of Single-Time-Point Dosimetry for [Journal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- A Deep-Learning-Based Partial-Volume Correction Method for QuantitativeJournal of nuclear medicine : official publication, Society of Nuclear Medicine · 2024Article
- Application of Nuclear Medicine Techniques in Musculoskeletal Infection: Current Trends and Future Prospects.Journal of clinical medicine · 2024Review
- Observer studies of image quality of denoising reduced-count cardiac single photon emission computed tomography myocardial perfusion imaging by three-dimensional Gaussian post-reconstruction filtering and deep learning.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Need for objective task-based evaluation of deep learning-based denoising methods: A study in the context of myocardial perfusion SPECT.Medical physics · 2023Article
- [Opportunities for artificial intelligence in radiation protection : Improving safety of diagnostic imaging].Radiologie (Heidelberg, Germany) · 2023Review
- Deep learning-based denoising in projection-domain and reconstruction-domain for low-dose myocardial perfusion SPECT.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Mesh modeling of system geometry and anatomy phantoms for realistic GATE simulations and their inclusion in SPECT reconstruction.Physics in medicine and biology · 2023Article
- Comparison of deep learning-based denoising methods in cardiac SPECT.EJNMMI physics · 2023Article
- Deep learning-enhanced nuclear medicine SPECT imaging applied to cardiac studies.EJNMMI physics · 2023Review
- Fast myocardial perfusion SPECT denoising using an attention-guided generative adversarial network.Frontiers in medicine · 2023Article
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Authors and funding
5 authors.
Funding
Abstract
purposePost-reconstruction filtering is often applied for noise suppression due to limited data counts in myocardial perfusion imaging (MPI) with single-photon emission computed tomography (SPECT). We study a deep learning (DL) approach for denoising in conventional SPECT-MPI acquisitions, and investigate whether it can be more effective for improving the detectability of perfusion defects compared to traditional postfiltering.
methodsOwing to the lack of ground truth in clinical studies, we adopt a noise-to-noise (N2N) training approach for denoising in SPECT-MPI images. We consider a coupled U-Net (CU-Net) structure which is designed to improve learning efficiency through feature map reuse. For network training we employ a bootstrap procedure to generate multiple noise realizations from list-mode clinical acquisitions. In the experiments we demonstrated the proposed approach on a set of 895 clinical studies, where the iterative OSEM algorithm with three-dimensional (3D) Gaussian postfiltering was used to reconstruct the images. We investigated the detection performance of perfusion defects in the reconstructed images using the non-prewhitening matched filter (NPWMF), evaluated the uniformity of left ventricular (LV) wall in terms of image intensity, and quantified the effect of smoothing on the spatial resolution of the reconstructed LV wall by using its full-width at half-maximum (FWHM).
resultsCompared to OSEM with Gaussian postfiltering, the DL denoised images with CU-Net significantly improved the detection performance of perfusion defects at all contrast levels (65%, 50%, 35%, and 20%). The signal-to-noise ratio (SNR
conclusionsThe proposed DL with N2N training approach can yield additional noise suppression in SPECT-MPI images over conventional postfiltering. For perfusion defect detection, DL with CU-Net could outperform conventional 3D Gaussian filtering with optimal setting as well as NLM and CAE.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.