Missing Slice Imputation in Population CMR Imaging via Conditional Generative Adversarial Nets

Abstract

Accurate ventricular volume measurements depend on complete heart coverage in cardiac magnetic resonance (CMR) from where most immediate indicators of normal/abnormal cardiac function are available non-invasively. However, incomplete coverage, especially missing basal or apical slices in CMR sequences is insufficiently addressed in population imaging and current clinical research studies yet has important impact on volume calculation accuracy. In this work, we propose a new deep architecture, coined Missing Slice Imputation Generative Adversarial Network (MSIGAN), to learn key features of cardiac short-axis (SAX) slices across different positions, and use them as conditional variables to effectively infer missing slices in the query volumes. In MSIGAN, the slices are first mapped to latent vectors with position features through a regression net. The latent vector corresponding to the desired position is then projected onto the slice manifold conditional on slice intensity through a generator net. The latent vector along with the slice features (i.e., intensity) and desired position control the generation vs. regression. Two adversarial networks are imposed on the regressor and generator, encouraging more realistic slices. Experimental results show that our method outperforms the previous state-of-the-art in missing slice imputation for cardiac MRI.