Joint image reconstruction and segmentation of real-time cardiac MRI in free-breathing using a model based on disentangled representation learning.

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To investigate image quality and agreement of derived cardiac function parameters in a novel joint image reconstruction and segmentation approach based on disentangled representation learning, enabling real-time cardiac cine imaging during free-breathing. A multi-tasking neural network architecture, incorporating disentangled representation learning, was trained using simulated examinations based on data from a public repository along with MR scans specifically acquired for model development. An exploratory feasibility study evaluated the method on undersampled real-time acquisitions using 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 with 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 arrhythmia, both real-time approaches demonstrated favourable image quality (RT-BH: 2.10 ± 1.28, p<.001, RT-FB: 2.40 ± 1.13, p<.01, Cartesian: 2.68 ± 1.13). Intra-observer reliability was good (ICC=.77,95%-confidence interval [.75,.79], p<.001). In functional analysis, a positive bias was observed for ejection fractions derived from the proposed model compared to the clinical reference standard (RT-BH mean: 58.5 ± 5.6%, bias: +3.47%, 95%-confidence interval [-.86, 7.79%], RT-FB mean: 57.9 ± 10.6%, bias: +1.45%, [-3.02, 5.91%], Cartesian mean: 54.9 ± 6.7%). The introduced real-time MR imaging technique enables high-quality cardiac cine data acquisitions in 1-2minutes, eliminating the need for ECG gating and breath-holds. This approach offers a promising alternative to the current clinical practice of segmented acquisition, with shorter scan times, improved patient comfort, and increased robustness to arrhythmia and patient non-compliance.