A Bayesian approach for 4D flow imaging of aortic valve in a single breath-hold

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.

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.

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.

Using the proposed technique, it is feasible to highly accelerate 4D flow acquisition and thus enable aortic valve imaging within a single breath-hold.