Venc Design and Velocity Estimation for Phase Contrast MRI

TL;DR

This paper introduces PRoM, a fast estimator for velocity in phase-contrast MRI that improves unwrapping accuracy, extends velocity range, and optimizes acquisition without needing coil sensitivity maps, validated through simulations and in vivo tests.

Contribution

The paper presents PRoM, a novel maximum likelihood estimator that accurately recovers velocities beyond traditional limits using multi-coil data without prior coil sensitivity knowledge.

Findings

48.5% reduction in root mean squared error at 1.5T

Extended velocity range beyond twice the venc

Validated improvements in simulation and in vivo studies

Abstract

In phase-contrast magnetic resonance imaging (PC-MRI), spin velocity contributes to the phase measured at each voxel. Therefore, estimating velocity from potentially wrapped phase measurements is the task of solving a system of noisy congruence equations. We propose Phase Recovery from Multiple Wrapped Measurements (PRoM) as a fast, approximate maximum likelihood estimator of velocity from multi-coil data with possible amplitude attenuation due to dephasing. The estimator can recover the fullest possible extent of unambiguous velocities, which can greatly exceed twice the highest venc. The estimator uses all pairwise phase differences and the inherent correlations among them to minimize the estimation error. Correlations are directly estimated from multi-coil data without requiring knowledge of coil sensitivity maps, dephasing factors, or the actual per-voxel signal-to-noise ratio. Derivation of the estimator yields explicit probabilities of unwrapping errors and the probability distribution for the velocity estimate; this, in turn, allows for optimized design of the phase-encoded acquisition. These probabilities are also incorporated into spatial post-processing to further mitigate wrapping errors. Simulation, phantom, and in vivo results for three-point PC-MRI acquisitions validate the benefits of reduced estimation error, increased recovered velocity range, optimized acquisition, and fast computation. A phantom study at 1.5T demonstrates 48.5% decrease in root mean squared error using PRoM with post-processing versus a conventional "dual-venc" technique. Simulation and 3T in vivo results likewise demonstrate the proposed benefits.