ArticleNMR in biomedicine2024
ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function.
Article in NMR in biomedicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 3 citations in OpenAlex.
- Generative MR Multitasking With Complex-Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real-Time Imaging.Magnetic resonance in medicine · 2026Article
- Free-Breathing Dynamic, Regularized, Adaptive Cluster Optimization (DRACO) Cine Cardiac MRI in Atrial Fibrillation.Journal of magnetic resonance imaging : JMRI · 2026Article
- Real-time 4D MRI reconstruction using DVR-NeMF, a framework for dynamic volumetric reconstruction.Cell reports methods · 2025Article
- Dynamic Regularized Adaptive Cluster Optimization (DRACO) for Quantitative Cardiac Cine MRI in Complex Arrhythmias.Journal of magnetic resonance imaging : JMRI · 2025Article
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
backgroundDespite the widespread use of cine MRI for evaluation of cardiac function, existing real-time methods do not easily enable quantification of ventricular function. Moreover, segmented cine MRI assumes periodicity of cardiac motion. We aim to develop a self-gated, cine MRI acquisition scheme with data-driven cluster-based binning of cardiac motion.
methodsA Cartesian golden-step balanced steady-state free precession sequence with sorted k-space ordering was designed. Image data were acquired with breath-holding. Principal component analysis and k-means clustering were used for binning of cardiac phases. Cluster compactness in the time dimension was assessed using temporal variability, and dispersion in the spatial dimension was assessed using the Caliński-Harabasz index. The proposed and the reference electrocardiogram (ECG)-gated cine methods were compared using a four-point image quality score, SNR and CNR values, and Bland-Altman analyses of ventricular function.
resultsA total of 10 subjects with sinus rhythm and 8 subjects with arrhythmias underwent cardiac MRI at 3.0 T. The temporal variability was 45.6 ms (cluster) versus 24.6 ms (ECG-based) (p < 0.001), and the Caliński-Harabasz index was 59.1 ± 9.1 (cluster) versus 22.0 ± 7.1 (ECG based) (p < 0.001). In subjects with sinus rhythm, 100% of the end-systolic and end-diastolic images from both the cluster and reference approach received the highest image quality score of 4. Relative to the reference cine images, the cluster-based multiphase (cine) image quality consistently received a one-point lower score (p < 0.05), whereas the SNR and CNR values were not significantly different (p = 0.20). In cases with arrhythmias, 97.9% of the end-systolic and end-diastolic images from the cluster approach received an image quality score of 3 or more. The mean bias values for biventricular ejection fraction and volumes derived from the cluster approach versus reference cine were negligible.
conclusionECG-free cine cardiac MRI with data-driven clustering for binning of cardiac motion is feasible and enables quantification of cardiac function.
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