Keywords: Diffusion Analysis & Visualization, Motion Correction
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.
Goal(s): 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.
Impact: This improved image registration and frame selection algorithm may enable groupwise deformable registration on DT-CMR, paving the way towards clinical translation.
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