A Bayesian model for highly accelerated phase-contrast MRI


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Abstract

Purpose:Phase-contrast magnetic resonance imaging is a noninvasive tool to assess cardiovascular disease by quantifying blood flow; however, low data acquisition efficiency limits the spatial and temporal resolutions, real-time application, and extensions to four-dimensional flow imaging in clinical settings. We propose a new data processing approach called Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) that accelerates the acquisition by exploiting data structure unique to phase-contrast magnetic resonance imaging.Theory and Methods:The proposed approach models physical correlations across space, time, and velocity encodings. The proposed Bayesian approach exploits the relationships in both magnitude and phase among velocity encodings. A fast iterative recovery algorithm is introduced based on message passing. For validation, prospectively undersampled data are processed from a pulsatile flow phantom and five healthy volunteers.Results:The proposed approach is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates Symbol. For SV, Pearson Symbol for phantom imaging (n = 24) and Symbol for prospectively accelerated in vivo imaging (n = 10) for Symbol.Conclusion:The proposed approach enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to four-dimensional flow imaging, where higher acceleration may be possible due to additional redundancy. Magn Reson Med 76:689–701, 2016. © 2015 Wiley Periodicals, Inc.

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