A flow quantification method using fluid dynamics regularization and MR tagging

An Improved Element Design for 64-Channel Planar Imaging

Investigation of highly accelerated MRI has developed into a lively corner in the hardware and methodology arena in recent years. At the extreme of (one-dimensional) acceleration, our group introduced Single Echo Acquisition (SEA) imaging, in which the need to phase encode a 64×N(readout) image is eliminated and replaced with the well-localized spatial information obtained from an array of 64 very narrow, long, parallel coils. The narrow coil width (2mm) that facilitates this is accompanied by a concomitant constraint on the useful imaging depth. This note describes a 64-element planar array, constructed within the same 8×13cm total footprint as the original SEA array, still enabling full acceleration in one dimension, but with an element design modified to increase the imaging depth. This was accomplished by lowering the outer conducting legs of the planar pair with respect to the center conductor and adding a geometric decoupling configuration away from the imaging field of view. The element has been called a dual-plane pair in that the current carrying rungs in the imaging FOV function exactly as the planar pair, but are simply placed in two separate planes (sides of PCB in this case).

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.