A Bayesian model for highly accelerated phase-contrast MRI

et al. 2018

Self Cite

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.

Section: Introductionmentioning

confidence: 99%

A bayesian method for accelerated magnetic resonance elastography of the liver

Ebersole

Ahmad

et al. 2018

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“…VISTA allows variable density but does not permit retrospective adjustment of temporal resolution. We recently extended VISTA for PC‐MRI . Using VISTA and CAVA patterns, the undersampling process was repeated to simulate six different readout lines per frame (LPF), that is, LPF ( k ) = 4, 5, 6, 8, 10, and 15, leading to temporal resolutions (2 × k × TR) of 41.0, 51.3, 61.6, 82.1, 102.6, and 153.9 ms, respectively.…”

Section: Methodsmentioning

confidence: 99%

CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA)

Gregg

Jin

et al. 2019

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Purpose To develop a variable density Cartesian sampling method that allows retrospective adjustment of temporal resolution for dynamic MRI applications and to validate it in real‐time phase contrast MRI (PC‐MRI). Theory and Methods The proposed method, called CArtesian sampling with Variable density and Adjustable temporal resolution (CAVA), begins by producing a sequence of phase encoding indices based on the golden ratio increment. Then, variable density is introduced by nonlinear stretching of the indices. Finally, the elements of the resulting sequence are rounded up to the nearest integer. The performance of CAVA is evaluated using PC‐MRI data from a pulsatile flow phantom and real‐time, free‐breathing data from ten healthy volunteers. Results CAVA enabled image recovery at various temporal resolutions that were selected retrospectively. For the pulsatile flow phantom, image quality and flow quantification accuracy from CAVA were comparable to that from another pseudo‐random sampling pattern with fixed temporal resolution. In addition, flow quantification results based on CAVA were in good agreement with a breath‐held segmented acquisition. Conclusions By allowing retrospective binning of the MRI data, CAVA provides an avenue to retrospectively adjust the temporal resolution of PC‐MRI.

“…Second, the resulting Generalized Approximate Message Passing (GAMP) prior can be further simplified to a two-component mixture that has tractable message derivations and computation. As described in, 11 we choose the following conditional prior on each complex-valued voxel:…”

Section: Bayesian Data Modelmentioning

confidence: 99%

“…The resulting problem is made computationally tractable by a combination of standard and loopy belief propagation, leading to a novel, iterative inversion algorithm. The framework, called ReVEAL4D, is a non‐trivial extension of our recently described 2D PC‐MRI technique referred to as Reconstructing Velocity Encoded MRI with Approximate message passing aLgorithms (ReVEAL) . ReVEAL4D is validated using in vivo and mechanical phantom data, yielding acceleration rates in access of twenty.…”

Section: Introductionmentioning

confidence: 99%

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

Potter

Jin³

et al. 2018

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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.