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

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 arrhythmia, image quality from both real-time approaches was favourable (RT-BH: 2.10 ± 1.28, p<.001, RT-FB: 2.40 ± 1.13, p<.001, 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 EF: 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 is capable of acquiring high-quality cardiac cine data in 1-2 minutes without the need for ECG gating and breath-holds. It thus offers a promising alternative to the current clinical practice of segmented acquisition, with shorter scan times, higher patient comfort and increased robustness to arrhythmia and patient incompliance.