ArticleScientific reports2022
Validation and quantification of left ventricular function during exercise and free breathing from real-time cardiac magnetic resonance images.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Exercise MRI stress testing of the human heart at 3 Tesla: measurement precision of biventricular function and aortic blood flow during steady-state bicycling exercise.Magma (New York, N.Y.) · 2026Article
- Dynamic right ventricular and atrial volume responses to exercise in endurance-trained and untrained healthy individuals.PloS one · 2026Article
- Real-time CMR combined with cardiopulmonary exercise testing enables integrated assessment of cardiac function during exercise.Frontiers in cardiovascular medicine · 2026Article
- Article
- Retrospectively synchronized time-resolved ventricular cine images from 2D real-time exercise cardiac magnetic resonance imaging.Clinical physiology and functional imaging · 2025Article
- Accelerated real-time cine and flow under in-magnet staged exercise.Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance · 2025Article
- Exercise cardiovascular magnetic resonance shows improved diastolic filling by atrioventricular area difference in athletes and controls.Journal of applied physiology (Bethesda, Md. : 1985) · 2024Article
- Noninvasive Pressure-Volume Loops Predict Major Adverse Cardiac Events in Heart Failure With Reduced Ejection Fraction.JACC. Advances · 2024Article
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10 authors.
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Abstract
Exercise cardiovascular magnetic resonance (CMR) can unmask cardiac pathology not evident at rest. Real-time CMR in free breathing can be used, but respiratory motion may compromise quantification of left ventricular (LV) function. We aimed to develop and validate a post-processing algorithm that semi-automatically sorts real-time CMR images according to breathing to facilitate quantification of LV function in free breathing exercise. A semi-automatic algorithm utilizing manifold learning (Laplacian Eigenmaps) was developed for respiratory sorting. Feasibility was tested in eight healthy volunteers and eight patients who underwent ECG-gated and real-time CMR at rest. Additionally, volunteers performed exercise CMR at 60% of maximum heart rate. The algorithm was validated for exercise by comparing LV mass during exercise to rest. Respiratory sorting to end expiration and end inspiration (processing time 20 to 40 min) succeeded in all research participants. Bias ± SD for LV mass was 0 ± 5 g when comparing real-time CMR at rest, and 0 ± 7 g when comparing real-time CMR during exercise to ECG-gated at rest. This study presents a semi-automatic algorithm to retrospectively perform respiratory sorting in free breathing real-time CMR. This can facilitate implementation of exercise CMR with non-ECG-gated free breathing real-time imaging, without any additional physiological input.
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