Improved Time-Resolved MRA Using k-Space Deep Learning

Abstract

In dynamic contrast enhanced (DCE) MRI, temporal and spatial resolution can be improved by time-resolved angiography with interleaved stochastic trajectories (TWIST) thanks to its highly accelerated acquisitions. However, due to limited k-space samples, the periphery of the k-space data from several adjacent frames should be combined to reconstruct one temporal frame so that the temporal resolution of TWIST is limited. Furthermore, the k-space sampling patterns of TWIST imaging have been especially designed for a generalized autocalibrating partial parallel acquisition (GRAPPA) reconstruction. Therefore, the number of shared frames cannot be reduced to provide a reconstructed image with better temporal resolution. The purpose of this study is to improve the temporal resolution of TWIST using a novel k-space deep learning approach. Direct k-space interpolation is performed simultaneously for multiple coils by exploiting spatial domain redundancy and multi-coil diversity. Furthermore, the proposed method can provide the reconstructed images with various numbers of view sharing. Experimental results using in vivo TWIST data set showed the accuracy and the flexibility of the proposed method.