ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2022
CT-free attenuation correction for dedicated cardiac SPECT using a 3D dual squeeze-and-excitation residual dense network.
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 31 papers.
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31 citing papers in PubMed.
- General Purpose Deep Learning Attenuation Correction Improves Diagnostic Accuracy of SPECT MPI: A Multicenter Study.JACC. Cardiovascular imaging · 2025Trial
- Deep learning-based attenuation and scatter correction in myocardial SPECT without using X-ray CT images.Radiological physics and technology · 2026Article
- Approximate CT-free patient specific attenuation correction method for cardiac-dedicated pinhole CZT system: a feasibility phantom study.EJNMMI physics · 2026Article
- A correlative quantitative phase contrast and fluorescence super-resolution microscope for imaging molecules in their cellular context.bioRxiv : the preprint server for biology · 2025Article
- Development and in silico imaging trial evaluation of a deep-learning-based transmission-less attenuation compensation method for DaT SPECT.Medical physics · 2025Article
- POUR-Net: A Population-Prior-Aided Over-Under-Representation Network for Low-Count PET Attenuation Map Generation.IEEE transactions on medical imaging · 2025Article
- 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
- ISIT-GEN: An in silico imaging trial to assess the inter-scanner generalizability of CTLESS for myocardial perfusion SPECT on defect-detection task.Proceedings of SPIE--the International Society for Optical Engineering · 2025Article
- 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
- Fusing global context with multiscale context for enhanced breast cancer classification.Scientific reports · 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
- Deep learning approach using SPECT-to-PET translation for attenuation correction in CT-less myocardial perfusion SPECT imaging.Annals of nuclear medicine · 2024Article
- Deep learning to automate SPECT MPI myocardial reorientation.Journal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- Deep learning approximation of attenuation maps for myocardial perfusion SPECT with an IQEJNMMI physics · 2023Article
- DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.Medical image analysis · 2023Article
- Generation of Whole-Body FDG ParametricIEEE transactions on radiation and plasma medical sciences · 2023Article
- 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
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Funding
Abstract
backgroundAttenuation correction (AC) using CT transmission scanning enables the accurate quantitative analysis of dedicated cardiac SPECT. However, AC is challenging for SPECT-only scanners. We developed a deep learning-based approach to generate synthetic AC images from SPECT images without AC.
methodsCT-free AC was implemented using our customized Dual Squeeze-and-Excitation Residual Dense Network (DuRDN). 172 anonymized clinical hybrid SPECT/CT stress/rest myocardial perfusion studies were used in training, validation, and testing. Additional body mass index (BMI), gender, and scatter-window information were encoded as channel-wise input to further improve the network performance.
resultsQuantitative and qualitative analysis based on image voxels and 17-segment polar map showed the potential of our approach to generate consistent SPECT AC images. Our customized DuRDN showed superior performance to conventional network design such as U-Net. The averaged voxel-wise normalized mean square error (NMSE) between the predicted AC images by DuRDN and the ground-truth AC images was 2.01 ± 1.01%, as compared to 2.23 ± 1.20% by U-Net.
conclusionsOur customized DuRDN facilitates dedicated cardiac SPECT AC without CT scanning. DuRDN can efficiently incorporate additional patient information and may achieve better performance compared to conventional U-Net.
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