CS-Dixon: Compressed Sensing for Water-Fat Dixon Reconstruction

Introduction:Water-fat separation is of interest in several MRI applications including fat suppression and fat quantification. Chemical shift imagingallows robust water-fat separation [1, 2], however the acquisition ofmultiple images results in prolonged scan time. Accelerated water-fat separationusing compressed sensing (CS) was partly addressed in [3] by considering theseparation as a spectroscopic problem and exploiting the spectralsparsity in the reconstruction. However,this approach requires data acquisition atmultiple echo times (significantly more than 3), prolonging the scantime and limiting the effective acceleration factor. In this workwe consider the commonly used three point Dixon approach forwater-fat separation.Dixon reconstruction already assumes signal sparsity in the spectral dimension by modeling the signal by a two point spectrum at fixed frequencies.An integrated CS-Dixon algorithm is proposed,which applies a sparsity constraint onthe water and fat images and jointly estimates water, fat andfield map images. The method allows scantime reduction of above 3 in 3D MRI, fullycompensating for the additional time necessary to acquire thechemical shift encoded data.