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Abstract #0769

Rapid Whole-Heart CMR with Single Volume Super-Resolution

Jennifer Steeden1, Michael Quail2, Alexander Gotschy2,3, Andreas Hauptmann1,4, Rodney Jones1, and Vivek Muthurangu1
1University College London, London, United Kingdom, 2Great Ormond Street Hospital, London, United Kingdom, 3University and ETH Zurich, Institute for Biomedical Engineering, Zurich, Switzerland, 4University of Oulu, Oulu, Finland

Three-dimensional (3D), whole heart, balanced steady state free precession (WH-bSSFP) sequences provides excellent delineation of both intra-cardiac and vascular anatomy. However, they are usually cardiac triggered and respiratory navigated, resulting in long acquisition times (10-15minutes). Here, we propose a machine-learning single-volume super-resolution reconstruction (SRR), to recover high-resolution features from rapidly acquired low-resolution WH-bSSFP data. We show that it is possible to train a network using synthetically down-sampled WH-bSSFP data. We tested the network on synthetic test data and 40 prospective data sets, showing ~3x speed-up in acquisition time, with excellent agreement with reference standard high resolution WH-bSSFP images.

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