  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.
