Cardiac MR segmentation from undersampled k-space using deep latent representation learning

Abstract

Reconstructing magnetic resonance imaging (MRI) from undersampled k-space enables the accelerated acquisition of MRI but is a challenging problem. However, in many diagnostic scenarios, perfect reconstructions are not necessary as long as the images allow clinical practitioners to extract clinically relevant parameters. In this work, we present a novel deep learning framework for reconstructing such clinical parameters directly from undersampled data, expanding on the idea of application-driven MRI. We propose two deep architectures, an end-to-end synthesis network and a latent feature interpolation network, to predict cardiac segmentation maps from extremely undersampled dynamic MRI data, bypassing the usual image reconstruction stage altogether. We perform a large-scale simulation study using UK Biobank data containing nearly 1000 test subjects and show that with the proposed approaches, an accurate estimate of clinical parameters such as ejection fraction can be obtained from fewer than 10 k-space lines per time-frame.