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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