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A Bayesian approach for 4D flow imaging of aortic valve in a single breath‐hold
Author(s) -
Rich Adam,
Potter Lee C.,
Jin Ning,
Liu Yingmin,
Simonetti Orlando P.,
Ahmad Rizwan
Publication year - 2019
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.27386
Subject(s) - computer science , voxel , imaging phantom , bayesian probability , flow (mathematics) , inverse problem , wavelet , artificial intelligence , bayesian inference , pattern recognition (psychology) , contrast (vision) , bayes' theorem , algorithm , mathematics , physics , mathematical analysis , geometry , optics
Purpose To develop and validate a data processing technique that allows phase‐contrast MRI‐based 4D flow imaging of the aortic valve in a single breath‐hold. Theory and Methods To regularize the ill‐posed inverse problem, we extend a recently proposed 2D phase‐contrast MRI method to 4D flow imaging. Adopting an empirical Bayes approach, spatial and temporal redundancies are exploited via sparsity in the wavelet domain, and the voxel‐wise magnitude and phase structure across encodings is captured in a conditional mixture prior that applies regularizing constraints based on the presence of flow. We validate the proposed technique using data from a mechanical flow phantom and five healthy volunteers. Results The flow parameters derived from the proposed technique are in good agreement with those derived from reference datasets for both in vivo and mechanical flow experiments at accelerations rates as high as R = 27. Additionally, the proposed technique outperforms kt SPARSE‐SENSE and a method that exploits spatio‐temporal sparsity but does not utilize signal structure across encodings. Conclusions Using the proposed technique, it is feasible to highly accelerate 4D flow acquisition and thus enable aortic valve imaging within a single breath‐hold.