ArticleJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology2023
Deep-learning-based methods of attenuation correction for SPECT and PET.
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 23 papers.
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Who cites it
23 citing papers in PubMed.
- Beyond CT: Attenuation correction for stand-alone brain PET.Zeitschrift fur medizinische Physik · 2026Review
- Evolving SPECT-CT technology.The British journal of radiology · 2026Review
- Attenuation correction of cardiacEJNMMI physics · 2026Article
- Impact of Deep-Learning-Based Respiratory Motion Correction on [Biomedicines · 2026Article
- Towards practical radiopharmaceutical treatment planning: a review of dosimetry simplification techniques.Theranostics · 2026Review
- Instrumentation Digital Twins in PET and SPECT Imaging: Current Status, Challenges, and Future Directions.Computational and structural biotechnology journal · 2026Review
- 2.5D Multi-View Averaging Diffusion Model for 3D Medical Image Translation: Application to Low-Count PET Reconstruction With CT-Less Attenuation Correction.IEEE transactions on medical imaging · 2025Article
- Investigation of Attenuation Correction Methods for Dual-Gated Single Photon Emission Computed Tomography (DG-SPECT).Bioengineering (Basel, Switzerland) · 2025Article
- Enhanced direct joint attenuation and scatter correction of whole-body PET images via context-aware deep networks.Zeitschrift fur medizinische Physik · 2025Article
- CT-free attenuation and Monte-Carlo based scatter correction-guided quantitativeEuropean journal of nuclear medicine and molecular imaging · 2025Article
- AI in SPECT Imaging: Opportunities and Challenges.Seminars in nuclear medicine · 2025Review
- 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
- A Cross-talk between Nanomedicines and Cardiac Complications: Comprehensive View.Current pharmaceutical design · 2025Review
- 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
- 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
- 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
- Supplemental Transmission Aided Attenuation Correction for Quantitative Cardiac PET.IEEE transactions on medical imaging · 2024Article
- Attenuation Correction of Long Axial Field-of-View Positron Emission Tomography Using Synthetic Computed Tomography Derived from the Emission Data: Application to Low-Count Studies and Multiple Tracers.Diagnostics (Basel, Switzerland) · 2023Article
- DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.Medical image analysis · 2023Article
Corrections and comments
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2 authors.
Funding
Abstract
Attenuation correction (AC) is essential for quantitative analysis and clinical diagnosis of single-photon emission computed tomography (SPECT) and positron emission tomography (PET). In clinical practice, computed tomography (CT) is utilized to generate attenuation maps (μ-maps) for AC of hybrid SPECT/CT and PET/CT scanners. However, CT-based AC methods frequently produce artifacts due to CT artifacts and misregistration of SPECT-CT and PET-CT scans. Segmentation-based AC methods using magnetic resonance imaging (MRI) for PET/MRI scanners are inaccurate and complicated since MRI does not contain direct information of photon attenuation. Computational AC methods for SPECT and PET estimate attenuation coefficients directly from raw emission data, but suffer from low accuracy, cross-talk artifacts, high computational complexity, and high noise level. The recently evolving deep-learning-based methods have shown promising results in AC of SPECT and PET, which can be generally divided into two categories: indirect and direct strategies. Indirect AC strategies apply neural networks to transform emission, transmission, or MR images into synthetic μ-maps or CT images which are then incorporated into AC reconstruction. Direct AC strategies skip the intermediate steps of generating μ-maps or CT images and predict AC SPECT or PET images from non-attenuation-correction (NAC) SPECT or PET images directly. These deep-learning-based AC methods show comparable and even superior performance to non-deep-learning methods. In this article, we first discussed the principles and limitations of non-deep-learning AC methods, and then reviewed the status and prospects of deep-learning-based methods for AC of SPECT and PET.
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Registered trials
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