Abstract #4963

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

Eva S Peper^1,2, Adèle LC Mackowiak^1,2,3, Berk C Açıkgöz^1,2, Nils Plähn^1,2, Yasaman Safarkhanlo⁴, Li Feng⁵, and Jessica AM Bastiaansen^1,2

¹Department of Diagnostic, Interventional and Pediatric Radiology (DIPR), Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland, ²Translation Imaging Center (TIC), Swiss Institute for Translational and Entrepreneurial Medicine, Bern, Switzerland, ³Department of Diagnostic and Interventional Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁴Department of Cardiology, University Hospital Bern, Bern, Switzerland, ⁵BioMedical Engineering and Imaging Institute (BMEII), Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States

Synopsis

Keywords: Image Reconstruction, Sparse & Low-Rank Models, bSSFP, phase-cycled bSSFP, subspace reconstructionFat 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.

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