000265525 001__ 265525 000265525 005__ 20190812204800.0 000265525 037__ $$aCONF 000265525 245__ $$aRethinking Sampling in Parallel MRI: A Data-Driven Approach 000265525 260__ $$c2019 000265525 269__ $$a2019 000265525 300__ $$a5 000265525 336__ $$aConference Papers 000265525 520__ $$aIn the last decade, Compressive Sensing (CS) has emerged as the most promising, model-driven approach to accelerate MRI scans. CS relies on the key sparsity assumption and proposes random sampling for data acquisition. The practical CS approaches in MRI employ variable-density (VD) sampling, where samples are drawn at random based on a parametric probability model which focuses on the center of the Fourier domain. In stark contrast to this model-driven sampling approaches, we propose a data-driven framework for optimizing sampling in parallel (multi-coil) MRI. Our approach does not assume any structure in the data, and instead optimizes a performance metric (e.g. PSNR) for any given reconstruction algorithm, based on our earlier learning-based sampling framework previously applied to 2D MRI which we also extend to 3D MRI setting in this work by employing lazy evaluations in the greedy algorithm. We show boosted performance for the parallel MRI based on this sampling approach and highlight the inefficiency of variable density approaches. This suggests that data-driven sampling methods could be the key to unlocking the full power of CS applied to MRI. 000265525 6531_ $$aParallel MRI 000265525 6531_ $$acompressive sensing 000265525 6531_ $$alearning-based subsampling 000265525 6531_ $$agreedy algorithm 000265525 700__ $$0247491$$aGözcü, Baran$$g200700 000265525 700__ $$0254175$$aSanchez, Thomas$$g225447 000265525 700__ $$0243957$$aCevher, Volkan$$g199128 000265525 8564_ $$uhttps://infoscience.epfl.ch/record/265525/files/Rethinking_Sampling_in_Parallel_MRI_A_Data_Driven_Approach.pdf$$s1335461 000265525 8560_ $$falessandra.bianchi@epfl.ch 000265525 909C0 $$pLIONS$$mvolkan.cevher@epfl.ch$$0252306$$zMarselli, Béatrice$$xU12179 000265525 909CO $$pconf$$pSTI$$ooai:infoscience.epfl.ch:265525 000265525 960__ $$abaran.goezcue@epfl.ch 000265525 961__ $$aalessandra.bianchi@epfl.ch 000265525 973__ $$aEPFL$$rREVIEWED 000265525 980__ $$aCONF 000265525 981__ $$aoverwrite