UCSF

PURPOSE: Combining MR phase images from multiple receive coils is a challenging problem, complicated by ambiguities introduced by phase wrapping, noise, and the unknown phase-offset between the coils. Various techniques have been proposed to mitigate the effect of these ambiguities but most of the existing methods require additional reference scans and/or use ad hoc post-processing techniques that do not guarantee any optimality. THEORY AND METHODS: Here, the phase estimation problem is formulated rigorously using a maximum-likelihood (ML) approach. The proposed framework jointly designs the acquisition-processing chain: the optimized pulse sequence is a single multiecho gradient echo scan and the corresponding postprocessing algorithm is a voxel-per-voxel ML estimator of the underlying tissue phase. RESULTS: Our proposed framework (Maximum AmbiGuity distance for Phase Imaging, MAGPI) achieves substantial improvements in the phase estimate, resulting in phase signal-to-noise ratio (SNR) gains by up to an order of magnitude compared to existing methods. CONCLUSION: The advantages of MAGPI are: (1) ML-optimal combination of phase data from multiple receive coils, without a reference scan; (2) voxel-per-voxel ML-optimal estimation of the underlying tissue phase, without the need for phase unwrapping or image smoothing; and (3) robust dynamic estimation of channel-dependent phase-offsets.