ArticleEuropean journal of nuclear medicine and molecular imaging2020
Deep learning-based attenuation map generation for myocardial perfusion SPECT.
Article in European journal of nuclear medicine and molecular imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 59 papers.
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Who cites it
59 citing papers in PubMed, 120 citations in OpenAlex.
- The prognostic value of quantitative myocardial blood flow and flow reserve by dynamic CZT-SPECT for major adverse cardiovascular events.European journal of nuclear medicine and molecular imaging · 2026Article
- A projection-domain deep learning approach for respiratory motion correction of myocardial perfusion imaging using a multi-pinhole solid-state SPECT.Medical physics · 2026Article
- Accurate CT-free correction of attenuation, scatter, and aperture effects using deep learning in dedicated cardiac pinhole SPECT.Annals of nuclear medicine · 2026Article
- Approximate CT-free patient specific attenuation correction method for cardiac-dedicated pinhole CZT system: a feasibility phantom study.EJNMMI physics · 2026Article
- Experimental Evaluation of DE-SPECT: A Hyperspectral SPECT System for Region-Selective 3-D Gamma-Ray Spectroscopy.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Attenuation correction of cardiacEJNMMI physics · 2026Article
- The importance of developing multiparametric prognostic scores to stratify coronary risk by means of artificial intelligence.European journal of nuclear medicine and molecular imaging · 2026Article
- Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT.Medical physics · 2025Article
- CT-free kidney single-photon emission computed tomography for glomerular filtration rate.Scientific reports · 2025Article
- Deep learning in nuclear medicine: from imaging to therapy.Annals of nuclear medicine · 2025Review
- Deep learning-based CT-free attenuation correction for cardiac SPECT: a new approach.BMC medical imaging · 2025Article
- On the Effect of the Patient Table on Attenuation in Myocardial Perfusion Imaging SPECT.EJNMMI physics · 2025Article
- Investigation of a deep learning-based reconstruction approach utilizing dual-view projection for myocardial perfusion SPECT imaging.American journal of nuclear medicine and molecular imaging · 2025Article
- The role of deep learning in myocardial perfusion imaging for diagnosis and prognosis: A systematic review.iScience · 2024Review
- CTLESS: A scatter-window projection and deep learning-based transmission-less attenuation compensation method for myocardial perfusion SPECT.IEEE transactions on medical imaging · 2024Article
- Investigation of scatter energy window width and count levels for deep learning-based attenuation map estimation in cardiac SPECT/CT imaging.Physics in medicine and biology · 2024Article
- Article
- Deep-learning-based attenuation map generation in kidney single photon emission computed tomography.EJNMMI physics · 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
- TAI-GAN: A Temporally and Anatomically Informed Generative Adversarial Network for early-to-late frame conversion in dynamic cardiac PET inter-frame motion correction.Medical image analysis · 2024Article
Corrections and comments
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Authors and funding
5 authors at 1 institution in 2 countries.
Funding
Abstract
purposeAttenuation correction using CT transmission scanning increases the accuracy of single-photon emission computed tomography (SPECT) and enables quantitative analysis. Current existing SPECT-only systems normally do not support transmission scanning and therefore scans on these systems are susceptible to attenuation artifacts. Moreover, the use of CT scans also increases radiation dose to patients and significant artifacts can occur due to the misregistration between the SPECT and CT scans as a result of patient motion. The purpose of this study is to develop an approach to estimate attenuation maps directly from SPECT emission data using deep learning methods.
methodsBoth photopeak window and scatter window SPECT images were used as inputs to better utilize the underlying attenuation information embedded in the emission data. The CT-based attenuation maps were used as labels with which cardiac SPECT/CT images of 65 patients were included for training and testing. We implemented and evaluated deep fully convolutional neural networks using both standard training and training using an adversarial strategy.
resultsThe synthetic attenuation maps were qualitatively and quantitatively consistent with the CT-based attenuation map. The globally normalized mean absolute error (NMAE) between the synthetic and CT-based attenuation maps were 3.60% ± 0.85% among the 25 testing subjects. The SPECT reconstructed images corrected using the CT-based attenuation map and synthetic attenuation map are highly consistent. The NMAE between the reconstructed SPECT images that were corrected using the synthetic and CT-based attenuation maps was 0.26% ± 0.15%, whereas the localized absolute percentage error was 1.33% ± 3.80% in the left ventricle (LV) myocardium and 1.07% ± 2.58% in the LV blood pool.
conclusionWe developed a deep convolutional neural network to estimate attenuation maps for SPECT directly from the emission data. The proposed method is capable of generating highly reliable attenuation maps to facilitate attenuation correction for SPECT-only scanners for myocardial perfusion imaging.
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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.