Abstract #4613

Cluster Based Sparse Variational Minimization for Multi-Compartment Dictionary Fitting to bSSFP Signal Profiles

Berk Can Acikgoz^1,2, Adèle L.C. Mackowiak^1,2,3, Nils M.J. Plähn^1,2, Yasaman Safarkhanloo⁴, Eva S. Peper^1,2, Tom Hilbert^3,5,6, Giulia M.C. Rossi³, and Jessica A.M. 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 Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, ⁴Advanced Clinical Imaging Technology,Department of Cardiology, Inselspital, Bern University Hospital, Bern, Switzerland, ⁵Siemens Healthineers International AG, Lausanne, Switzerland, ⁶LTS5, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland

Synopsis

Keywords: Data Processing, Quantitative Imaging, phase-cycled bSSFP, fat fractionA novel cluster-based sparse total-variation minimization (CSTV) dictionary matching algorithm for multi-compartment parameter mapping applied to a phase-cycled balanced steady state free precession (PC-bSSFP) signal profile is presented. A dictionary encoding off-resonance frequency and relaxation time ratio is matched to the measured profiles with the proposed method to recover the underlying fat fraction, proton density, and water-fat-separated images simultaneously. The performance of the proposed method is tested and compared with a Laplacian regularized non-negative least squares dictionary matching method in phantom and in vivo experiments.

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