Low-rank based motion correction followed by automatic frame selection in DT-CMR

TL;DR

This paper introduces a semi-automatic post-processing pipeline for DT-CMR that employs low-rank frame averaging for robust registration and automatic frame rejection, improving data quality in challenging imaging conditions.

Contribution

It presents a novel low-rank based registration and frame selection method that enhances DT-CMR image quality and reduces manual intervention.

Findings

Outperforms previous noise-robust registration methods.

Reduces negative eigenvalues in healthy volunteer data.

Improves overall data quality in DT-CMR images.

Abstract

Motivation: Post-processing of in-vivo diffusion tensor CMR (DT-CMR) is challenging due to the low SNR and variation in contrast between frames which makes image registration difficult, and the need to manually reject frames corrupted by motion. Goals: To develop a semi-automatic post-processing pipeline for robust DT-CMR registration and automatic frame selection. Approach: We used low intrinsic rank averaged frames as the reference to register other low-ranked frames. A myocardium-guided frame selection rejected the frames with signal loss, through-plane motion and poor registration. Results: The proposed method outperformed our previous noise-robust rigid registration on helix angle data quality and reduced negative eigenvalues in healthy volunteers.