Aggregated motion estimation for real-time MRI reconstruction

TL;DR

This paper introduces a novel real-time MRI reconstruction method that incorporates frame deformations into data consistency, improving temporal fidelity and artifact removal without extra measurements or rigid assumptions.

Contribution

It presents a new approach that models non-rigid deformations between frames and jointly estimates images and coil sensitivities in a nonlinear framework.

Findings

Enhanced image quality with better temporal fidelity

Effective removal of residual artifacts

Applicable to non-Cartesian sampling schemes

Abstract

Real-time magnetic resonance imaging (MRI) methods generally shorten the measuring time by acquiring less data than needed according to the sampling theorem. In order to obtain a proper image from such undersampled data, the reconstruction is commonly defined as the solution of an inverse problem, which is regularized by a priori assumptions about the object. While practical realizations have hitherto been surprisingly successful, strong assumptions about the continuity of image features may affect the temporal fidelity of the estimated images. Here we propose a novel approach for the reconstruction of serial real-time MRI data which integrates the deformations between nearby frames into the data consistency term. The method is not required to be affine or rigid and does not need additional measurements. Moreover, it handles multi-channel MRI data by simultaneously determining the image and its coil sensitivity profiles in a nonlinear formulation which also adapts to non-Cartesian (e.g., radial) sampling schemes. Experimental results of a motion phantom with controlled speed and in vivo measurements of rapid tongue movements demonstrate image improvements in preserving temporal fidelity and removing residual artifacts.