  Diffusion tensor based cardiovascular magnetic resonance (DT-CMR) offers a
non-invasive method to visualize the myocardial microstructure. With the
assumption that the heart is stationary, frames are acquired with multiple
repetitions for different diffusion encoding directions. However, motion from
poor breath-holding and imprecise cardiac triggering complicates DT-CMR
analysis, further challenged by its inherently low SNR, varied contrasts, and
diffusion induced textures. Our solution is a novel framework employing
groupwise registration with an implicit template to isolate respiratory and
cardiac motions, while a tensor-embedded branch preserves diffusion contrast
textures. We have devised a loss refinement tailored for non-linear least
squares fitting and low SNR conditions. Additionally, we introduce new
physics-based and clinical metrics for performance evaluation. Access code and
supplementary materials at: https://github.com/ayanglab/DTCMR-Reg
