ArticleMedical image analysis2022
Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network.
Article in Medical image analysis, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Patlak-Guided Self-Supervised Learning for Dynamic PET Denoising.IEEE transactions on radiation and plasma medical sciences · 2026Article
- Kinetic modeling with total body PET-current status and future applications.The British journal of radiology · 2026Review
- Parametric imaging of dynamic long-axial-field-of-view PET scans: Technical challenges, statistical insights and clinical applications.Zeitschrift fur medizinische Physik · 2026Review
- Tangible advantages of multi-stage brain motion compensation for PET imaging demonstrated in multiple studies.medRxiv : the preprint server for health sciences · 2025Article
- A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.European journal of nuclear medicine and molecular imaging · 2025Article
- 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
- The role of dynamic, static, and delayed total-body PET imaging in the detection and differential diagnosis of oncological lesions.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Whole-body PET image denoising for reduced acquisition time.Frontiers in medicine · 2024Article
- TAI-GAN: Temporally and Anatomically Informed GAN for Early-to-Late Frame Conversion in Dynamic Cardiac PET Motion Correction.Simulation and synthesis in medical imaging : ... International Workshop, SASHIMI ..., held in conjunction with MICCAI ..., proceedings. SASHIMI (Workshop) · 2023Article
- MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.IEEE transactions on medical imaging · 2023Article
- Inter-pass motion correction for whole-body dynamic PET and parametric imaging.IEEE transactions on radiation and plasma medical sciences · 2023Article
- Motion correction and its impact on quantification in dynamic total-body 18F-fluorodeoxyglucose PET.EJNMMI physics · 2022Article
- MCP-Net: Inter-frame Motion Correction with Patlak Regularization for Whole-body Dynamic PET.Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Subject motion in whole-body dynamic PET introduces inter-frame mismatch and seriously impacts parametric imaging. Traditional non-rigid registration methods are generally computationally intense and time-consuming. Deep learning approaches are promising in achieving high accuracy with fast speed, but have yet been investigated with consideration for tracer distribution changes or in the whole-body scope. In this work, we developed an unsupervised automatic deep learning-based framework to correct inter-frame body motion. The motion estimation network is a convolutional neural network with a combined convolutional long short-term memory layer, fully utilizing dynamic temporal features and spatial information. Our dataset contains 27 subjects each under a 90-min FDG whole-body dynamic PET scan. Evaluating performance in motion simulation studies and a 9-fold cross-validation on the human subject dataset, compared with both traditional and deep learning baselines, we demonstrated that the proposed network achieved the lowest motion prediction error, obtained superior performance in enhanced qualitative and quantitative spatial alignment between parametric K
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