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Abstract #1001

CINENet: Deep learning-based 3D Cardiac CINE Reconstruction with multi-coil complex 4D Spatio-Temporal Convolutions

Thomas Küstner1, Niccolo Fuin1, Kerstin Hammernik2, Aurelien Bustin1, Radhouene Neji1,3, Daniel Rueckert2, René M Botnar1, and Claudia Prieto1
1Biomedical Engineering Department, School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom, 2Department of Computing, Imperial College London, London, United Kingdom, 3MR Research Collaborations, Siemens Healthcare Limited, Frimley, United Kingdom

CINE MRI is the gold-standard for the assessment of cardiac function. Compressed Sensing (CS) reconstruction has enabled 3D CINE acquisition with left ventricular (LV) coverage in a single breath-hold. However, maximal achievable acceleration is limited by the performance of the selected reconstruction method. Deep learning has shown to provide good-quality reconstructions of highly accelerated 2D CINE imaging. In this work, we propose a novel 4D (3D+time) reconstruction network for prospectively undersampled 3D Cartesian cardiac CINE that utilizes complex-valued spatial-temporal convolutions. The proposed network outperforms CS in visual quality and shows good agreement for LV function to gold-standard 2D CINE.

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