ArticleIEEE transactions on medical imaging2021
Automatic Inter-Frame Patient Motion Correction for Dynamic Cardiac PET Using Deep Learning.
Article in IEEE transactions on medical imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed.
- The Effect of Motion Correction on Quantitative Perfusion Indices and Threshold-Based Interpretation inDiagnostics (Basel, Switzerland) · 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
- Applications of artificial intelligence in nuclear medicine.Zeitschrift fur medizinische Physik · 2026Review
- The impact of motion correction on quantitatively measured perfusion indices using cardiac-dedicated dynamic CZT-SPECT myocardial perfusion imaging.EJNMMI physics · 2026Article
- Dynamic frame-by-frame motion correction forEuropean journal of nuclear medicine and molecular imaging · 2026Article
- Data-Driven Motion Correction Algorithm: Validation in [Journal of clinical medicine · 2026Article
- A deep learning method for total-body dynamic PET imaging with dual-time-window protocols.European journal of nuclear medicine and molecular imaging · 2025Article
- Fast inter-frame motion correction in contrast-free ultrasound quantitative microvasculature imaging using deep learning.Scientific reports · 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
- Comparing various AI approaches to traditional quantitative assessment of the myocardial perfusion in [Scientific reports · 2024Article
- Empowering PET: harnessing deep learning for improved clinical insight.European radiology experimental · 2024Review
- Feasibility study of a SiPM-fiber detector for non-invasive measurement of arterial input function for preclinical and clinical positron emission tomography.EJNMMI physics · 2024Article
- Detection and correction of patient motion in dynamicJournal of nuclear cardiology : official publication of the American Society of Nuclear Cardiology · 2023Article
- 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
- DuSFE: Dual-Channel Squeeze-Fusion-Excitation co-attention for cross-modality registration of cardiac SPECT and CT.Medical image analysis · 2023Article
- MCP-Net: Introducing Patlak Loss Optimization to Whole-body Dynamic PET Inter-frame Motion Correction.IEEE transactions on medical imaging · 2023Article
- 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
- Unsupervised inter-frame motion correction for whole-body dynamic PET using convolutional long short-term memory in a convolutional neural network.Medical image analysis · 2022Article
- Dynamic cardiac PET motion correction using 3D normalized gradient fields in patients and phantom simulations.Medical physics · 2021Article
- A Feasibility Study of Hybrid Deep-Learning Prediction with Online Adaptation of Breathing Irregularities for Long-Term Internal Organ Motion During Radiotherapy.Technology in cancer research & treatmentArticle
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7 authors.
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Abstract
Patient motion during dynamic PET imaging can induce errors in myocardial blood flow (MBF) estimation. Motion correction for dynamic cardiac PET is challenging because the rapid tracer kinetics of 82Rb leads to substantial tracer distribution change across different dynamic frames over time, which can cause difficulties for image registration-based motion correction, particularly for early dynamic frames. In this paper, we developed an automatic deep learning-based motion correction (DeepMC) method for dynamic cardiac PET. In this study we focused on the detection and correction of inter-frame rigid translational motion caused by voluntary body movement and pattern change of respiratory motion. A bidirectional-3D LSTM network was developed to fully utilize both local and nonlocal temporal information in the 4D dynamic image data for motion detection. The network was trained and evaluated over motion-free patient scans with simulated motion so that the motion ground-truths are available, where one million samples based on 65 patient scans were used in training, and 600 samples based on 20 patient scans were used in evaluation. The proposed method was also evaluated using additional 10 patient datasets with real motion. We demonstrated that the proposed DeepMC obtained superior performance compared to conventional registration-based methods and other convolutional neural networks (CNN), in terms of motion estimation and MBF quantification accuracy. Once trained, DeepMC is much faster than the registration-based methods and can be easily integrated into the clinical workflow. In the future work, additional investigation is needed to evaluate this approach in a clinical context with realistic patient motion.
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