ArticleIEEE transactions on medical imaging2019
Robust Non-Rigid Motion Compensation of Free-Breathing Myocardial Perfusion MRI Data.
Article in IEEE transactions on medical imaging, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 1 of them a synthesis that pooled it.
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Who cites it
21 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Society for cardiovascular magnetic resonance expert consensus statement on quantitative myocardial perfusion cardiovascular magnetic resonance imaging.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic ResonanceGuideline
- Coronary Microvascular Dysfunction Is Associated With Myocardial Ischemia and Abnormal Coronary Perfusion During Exercise.Circulation · 2019Trial
- Predictive value of multivariate models of quantitative myocardial movement parameters for acute myocardial infarction in elderly patients with coronary artery disease.BMC cardiovascular disorders · 2026Article
- Free-running time-resolved first-pass myocardial perfusion using a multi-scale dynamics decomposition: CMR-MOTUS.Magma (New York, N.Y.) · 2026Article
- Artificial Intelligence-Based Arterial Input Function for the Quantitative Assessment of Myocardial Blood Flow and Perfusion Reserve in Cardiac Magnetic Resonance: A Validation Study.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning-based rapid image reconstruction and motion correction for high-resolution cartesian first-pass myocardial perfusion imaging at 3T.Magnetic resonance in medicine · 2024Article
- Current Research Status of Respiratory Motion for Thorax and Abdominal Treatment: A Systematic Review.Biomimetics (Basel, Switzerland) · 2024Review
- Automatic calculation of myocardial perfusion reserve using deep learning with uncertainty quantification.Quantitative imaging in medicine and surgery · 2023Article
- AI-AIF: artificial intelligence-based arterial input function for quantitative stress perfusion cardiac magnetic resonance.European heart journal. Digital health · 2023Article
- Free-breathing motion-informed locally low-rank quantitative 3D myocardial perfusion imaging.Magnetic resonance in medicine · 2022Article
- Physics-informed neural networks for myocardial perfusion MRI quantification.Medical image analysis · 2022Article
- High-Resolution Free-Breathing Quantitative First-Pass Perfusion Cardiac MR Using Dual-Echo Dixon With Spatio-Temporal Acceleration.Frontiers in cardiovascular medicine · 2022Article
- Clinical Application of Dynamic Contrast Enhanced Perfusion Imaging by Cardiovascular Magnetic Resonance.Frontiers in cardiovascular medicine · 2021Review
- Automated Quantitative Stress Perfusion Cardiac Magnetic Resonance in Pediatric Patients.Frontiers in pediatrics · 2021Article
- Automated detection of left ventricle in arterial input function images for inline perfusion mapping using deep learning: A study of 15,000 patients.Magnetic resonance in medicine · 2020Article
- Feasibility of free-breathing quantitative myocardial perfusion using multi-echo Dixon magnetic resonance imaging.Scientific reports · 2020Article
- Deep-Learning-Based Preprocessing for Quantitative Myocardial Perfusion MRI.Journal of magnetic resonance imaging : JMRI · 2020Article
- Hierarchical Bayesian myocardial perfusion quantification.Medical image analysis · 2020Article
- Automatic in-line quantitative myocardial perfusion mapping: Processing algorithm and implementation.Magnetic resonance in medicine · 2020Article
- Motion correction of chemical exchange saturation transfer MRI series using robust principal component analysis (RPCA) and PCA.Quantitative imaging in medicine and surgery · 2019Article
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5 authors.
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Abstract
Kinetic parameter values, such as myocardial perfusion, can be quantified from dynamic contrast-enhanced magnetic resonance imaging data using tracer-kinetic modeling. However, respiratory motion affects the accuracy of this process. Motion compensation of the image series is difficult due to the rapid local signal enhancement caused by the passing of the gadolinium-based contrast agent. This contrast enhancement invalidates the assumptions of the (global) cost functions traditionally used in intensity-based registrations. The algorithms are unable to distinguish whether the differences in signal intensity between frames are caused by the spatial motion artifacts or the local contrast enhancement. In order to address this problem, a fully automated motion compensation scheme is proposed, which consists of two stages. The first of which uses robust principal component analysis (PCA) to separate the local signal enhancement from the baseline signal, before a refinement stage which uses the traditional PCA to construct a synthetic reference series that is free from motion but preserves the signal enhancement. Validation is performed on 18 subjects acquired in free-breathing and 5 clinical subjects acquired with a breath-hold. The validation assesses the visual quality, the temporal smoothness of tissue curves, and the clinically relevant quantitative perfusion values. The expert observers score the visual quality increased by a mean of 1.58/5 after motion compensation and improvement over the previously published methods. The proposed motion compensation scheme also leads to the improved quantitative performance of motion compensated free-breathing image series [30% reduction in the coefficient of variation across quantitative perfusion maps and 53% reduction in temporal variations (p < 0.001)].
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