Polynomial regularization for robust MRI-based estimation of blood flow velocities and pressure gradients

In cardiovascular diagnostics, phase-contrast MRI is a valuable technique for measuring blood flow velocities and computing blood pressure values. Unfortunately, both velocity and pressure data typically suffer from the strong image noise of velocity-encoded MRI. In the past, separate approaches of regularization with physical a-priori knowledge and data representation with continuous functions have been proposed to overcome these drawbacks. In this article, we investigate polynomial regularization as an exemplary specification of combining these two techniques. We perform time-resolved three-dimensional velocity measurements and pressure gradient computations on MRI acquisitions of steady flow in a physical phantom. Results based on the higher quality temporal mean data are used as a reference. Thereby, we investigate the performance of our approach of polynomial regularization, which reduces the root mean squared errors to the reference data by 45% for velocities and 60% for pressure gradients.