Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning
Tobias Wech, Oliver Schad, Simon Sauer, Jonas Kleineisel, Nils Petri,, Peter Nordbeck, Thorsten A. Bley, Bettina Bae{\ss}ler, Bernhard Petritsch,, Julius F. Heidenreich

TL;DR
This study presents a novel real-time cardiac MRI method using disentangled representation learning, enabling high-quality imaging during free-breathing without ECG gating, offering advantages over traditional segmented approaches.
Contribution
The paper introduces a new joint image reconstruction and segmentation technique based on disentangled representation learning for real-time, free-breathing cardiac MRI.
Findings
Comparable image quality to standard methods in healthy subjects
Favorable image quality in arrhythmic patients
Shorter scan times and increased robustness to arrhythmia
Abstract
A joint image reconstruction and segmentation approach based on disentangled representation learning was trained to enable cardiac cine MR imaging in real-time and under free-breathing. An exploratory feasibility study tested the proposed method in undersampled real-time acquisitions based on an in-house developed spiral bSSFP pulse sequence in eight healthy participants and five patients with intermittent atrial fibrillation. Images and predicted LV segmentations were compared to the reference standard of ECG-gated segmented Cartesian cine in repeated breath-holds and corresponding manual segmentation. On a 5-point Likert scale, image quality of the real-time breath-hold approach and Cartesian cine was comparable in healthy participants (RT-BH: 1.99 .98, Cartesian: 1.94 .86, p=.052), but slightly inferior in free-breathing (RT-FB: 2.40 .98, p<.001). In patients with…
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Taxonomy
TopicsAdvanced MRI Techniques and Applications · Nuclear Physics and Applications · Atomic and Subatomic Physics Research
