📝 SummarXiv

Abstract

2D cine phase contrast (CPC) MRI provides quantitative information on blood velocity and flow within the human vasculature. However, data acquisition is time-consuming, motivating the reconstruction of the velocity field from undersampled measurements to reduce scan times. In this work, we propose using neural fields to parametrize the complex-valued images, leveraging their inductive bias for the reconstruction of the velocity data. Additionally, to mitigate the inherent over-smoothing of neural fields, we introduce a simple voxel-based postprocessing step. We validate our method numerically in Cartesian and radial k-space with both high and low temporal resolution data. Our approach achieves accurate reconstructions at high acceleration factors, with low errors even at 16x and 32x undersampling, and consistently outperforms classical locally low-rank regularized voxel-based methods in both flow estimates and anatomical depiction.