Low Rank Subspace-Constrained Compressed Sensing Reconstruction of Highly Accelerated Phase-Cycled bSSFP MRI for Fat Fraction Quantification

Fat fraction (FF) maps can be estimated from phase-cycled (PC) balanced steady-state free precession (bSSFP) imaging using a dictionary fitting approach (SPARCQ). Three-dimensional PC bSSFP imaging, however, is time consuming, since around 16 PCs must be sampled to fully describe a voxels’ PC profile. Compressed sensing (CS) has been proven to accelerate MRI scans. This study has two aims 1) to evaluate the performance of two CS algorithms (total variation (TV)- vs. subspace-constrained) taking regularisation along the PC dimension into account and 2) to investigate the upper limits of acceleration using these techniques on FF quantification using SPARCQ.