Purpose Phase-contrast magnetic resonance imaging (PC-MRI) 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 4D 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 PC-MRI. Theory and Methods ReVEAL 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 ReVEAL is in good agreement, quantified by peak velocity and stroke volume (SV), with reference data for acceleration rates R ≤ 10. For SV, Pearson r ≥ 0.996 for phantom imaging (n = 24) and r ≥ 0.956 for prospectively accelerated in vivo imaging (n = 10) for R ≤ 10. Conclusion ReVEAL enables accurate quantification of blood flow from highly undersampled data. The technique is extensible to 4D flow imaging, where higher acceleration may be possible due to additional redundancy.

By exploiting transform sparsity and magnitude consistency, accurate quantification of mean stiffness in the liver can be obtained at an acceleration rate of up to R = 6. This potentially enables the collection of three to four liver slices, as per clinical protocol, within a single breath-hold. Magn Reson Med 80:1178-1188, 2018. © 2018 International Society for Magnetic Resonance in Medicine.

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.

BackgroundAortic stenosis (AS) is a common valvular disorder, and disease severity is currently assessed by transthoracic echocardiography (TTE). However, TTE results can be inconsistent in some patients, thus other diagnostic modalities such as cardiovascular magnetic resonance (CMR) are demanded. While traditional unidirectional phase-contrast CMR (1Dir PC-CMR) underestimates velocity if the imaging plane is misaligned to the flow direction, multi-directional acquisitions are expected to improve velocity measurement accuracy. Nonetheless, clinical use of multidirectional techniques has been hindered by long acquisition times. Our goal was to quantify flow parameters in patients using 1Dir PC-CMR and a faster multi-directional technique (3Dir PC-CMR), and compare to TTE.MethodsTwenty-three patients were prospectively assessed with TTE and CMR. Slices above the aortic valve were acquired for both PC-CMR techniques and cine SSFP images were acquired to quantify left ventricular stroke volume. 3Dir PC-CMR implementation included a variable density sampling pattern with acceleration rate of 8 and a reconstruction method called ReVEAL, to significantly accelerate acquisition. 3Dir PC-CMR reconstruction was performed offline and ReVEAL-based image recovery was performed on the three (x, y, z) encoding pairs. 1Dir PC-CMR was acquired with GRAPPA acceleration rate of 2 and reconstructed online. CMR derived flow parameters and aortic valve area estimates were compared to TTE.ResultsReVEAL based 3Dir PC-CMR derived parameters correlated better with TTE than 1Dir PC-CMR. Correlations ranged from 0.61 to 0.81 between TTE and 1Dir PC-CMR and from 0.61 to 0.87 between TTE and 3Dir-PC-CMR. The correlation coefficients between TTE, 1Dir and 3Dir PC-CMR Vpeakwere 0.81 and 0.87, respectively. In comparison to ReVEAL, TTE slightly underestimates peak velocities, which is not surprising as TTE is only sensitive to flow that is parallel to the acoustic beam.ConclusionsBy exploiting structure unique to PC-CMR, ReVEAL enables multi-directional flow imaging in clinically feasible acquisition times. Results support the hypothesis that ReVEAL-based 3Dir PC-CMR provides better estimation of hemodynamic parameters in AS patients in comparison to 1Dir PC-CMR. While TTE can accurately measure velocity parallel to the acoustic beam, it is not sensitive to the other directions of flow. Therefore, multi-directional flow imaging, which encodes all three components of the velocity vector, can potentially outperform TTE in patients with eccentric or multiple jets.Electronic supplementary materialThe online version of this article (doi:10.1186/s12968-017-0339-5) contains supplementary material, which is available to authorized users.

To develop and validate an acquisition and processing technique that enables fully self-gated 4D flow imaging with whole-heart coverage in a fixed 5-minute scan. Theory and Methods: The data are acquired continuously using Cartesian sampling and sorted into respiratory and cardiac bins using the self-gating signal. The reconstruction is performed using a recently proposed Bayesian method called ReVEAL4D. ReVEAL4D is validated using data from 8 healthy volunteers and 2 patients and compared with compressed sensing technique, L1-SENSE. Results: Healthy subjects-Compared with 2D phase-contrast MRI (2D-PC), flow quantification from ReVEAL4D shows no significant bias. In contrast, the peak velocity and peak flow rate for L1-SENSE are significantly underestimated. Compared with traditional parallel MRI-based 4D flow imaging, ReVEAL4D demonstrates small but significant biases in net flow and peak flow rate, with no significant bias in peak velocity. All 3 indices are significantly and more markedly underestimated by L1-SENSE. Patients-Flow quantification from ReVEAL4D agrees well with the 2D-PC reference. In contrast, L1-SENSE markedly underestimated peak velocity. Conclusions: The combination of highly accelerated 5-minute Cartesian acquisition, self-gating, and ReVEAL4D enables whole-heart 4D flow imaging with accurate flow quantification.