ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
Improving detection accuracy of perfusion defect in standard dose SPECT-myocardial perfusion imaging by deep-learning denoising.
Article in Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Efficiency of Artificial Intelligence in Three-Dimensional Reconstruction of Medical Imaging.Cureus · 2025Review
- Artificial intelligence applied in identifying left ventricular walls in myocardial perfusion scintigraphy images: Pilot study.PloS one · 2025Article
- Review of cardiovascular imaging in the Journal of Nuclear Cardiology 2022: single photon emission computed tomography.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Review
- 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
6 authors.
Funding
Abstract
backgroundWe previously developed a deep-learning (DL) network for image denoising in SPECT-myocardial perfusion imaging (MPI). Here we investigate whether this DL network can be utilized for improving detection of perfusion defects in standard-dose clinical acquisitions.
methodsTo quantify perfusion-defect detection accuracy, we conducted a receiver-operating characteristic (ROC) analysis on reconstructed images with and without processing by the DL network using a set of clinical SPECT-MPI data from 190 subjects. For perfusion-defect detection hybrid studies were used as ground truth, which were created from clinically normal studies with simulated realistic lesions inserted. We considered ordered-subset expectation-maximization (OSEM) reconstruction with corrections for attenuation, resolution, and scatter and with 3D Gaussian post-filtering. Total perfusion deficit (TPD) scores, computed by Quantitative Perfusion SPECT (QPS) software, were used to evaluate the reconstructed images.
resultsCompared to reconstruction with optimal Gaussian post-filtering (sigma = 1.2 voxels), further DL denoising increased the area under the ROC curve (AUC) from 0.80 to 0.88 (P-value < 10
conclusionsDL denoising can effectively improve the detection of abnormal defects in standard-dose SPECT-MPI images over conventional reconstruction.
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