UCSF

We have previously proposed a novel magnetic resonance (MR) phase imaging framework (MAGPI) based on a three-echo sequence that demonstrated substantial gains in phase signal-to-noise ratio (SNR). We improve upon the performance of MAGPI by extending the formulation to handle (i) an alternating gradient polarity (bipolar) readout scheme and (ii) an arbitrary number of echoes. We formulate the phase-imaging problem using maximum-likelihood (ML) estimation. The acquisition uses an optimized multi-echo gradient echo (MEGE) sequence. The tissue-phase estimation algorithm is a voxel-per-voxel approach, which requires no reference scans, no phase unwrapping and no spatial denoising. Unlike other methods, our bipolar readout model is general and does not make simplifying assumptions about the even-odd echo phase errors. The results show that (a) our proposed bipolar MAGPI approach improves on the phase SNR gains achieved with monopolar MAGPI and (b) the phase SNR converges with the number of echoes more rapidly with bipolar MAGPI. Importantly, bipolar MAGPI enables phase imaging in severely SNR-constrained scenarios, where monopolar MAGPI is unable to find solutions. The substantial phase SNR gains achieved with our framework are used here to (a) accelerate acquisitions (full brain 0.89 mm in-plane resolution in 2 min 30 sec) and (b) enable high-contrast high-resolution phase imaging (310 µm in-plane resolution) at clinical field strengths. Copyright © 2016 John Wiley & Sons, Ltd.