Enhanced DTCMR With Cascaded Alignment and Adaptive Diffusion

Abstract

Diffusion tensor cardiovascular magnetic resonance (DTCMR) is the only non-invasive method for visualizing myocardial microstructure, but it is challenged by inconsistent breath-holds and imperfect cardiac triggering, causing in-plane shifts and through-plane warping with an inadequate tensor fitting. While rigid registration corrects in-plane shifts, deformable registration risks distorting the diffusion distribution, and selecting a reference frame among low SNR frames is challenging. Existing pairwise deep learning and iterative methods are unsuitable for DTCMR due to their inability to handle the drastic in-plane motion and disentangle the diffusion contrast distortion with through-plane motions on low SNR frames, which compromises the accuracy of clinical biomarker tensor estimation. Our study introduces a novel deep learning framework incorporating tensor information for groupwise deformable registration, effectively correcting intra-subject inter-frame motion. This framework features a cascaded registration branch for addressing in-plane and through-plane motions and a parallel branch for generating pseudo-frames with diffusion contrasts and template updates to guide registration with a refined loss function and denoising. We evaluated our method on four DTCMR-specific metrics using data from over 900 cases from 2012 to 2023. Our method outperformed three traditional and two deep learning-based methods, achieving reduced fitting errors, the lowest percentage of negative eigenvalues at 0.446%, the highest R2 of HA line profiles at 0.911, no negative Jacobian Determinant, and the shortest reference time of 0.06 seconds per case. In conclusion, our deep learning framework significantly improves DTCMR imaging by effectively correcting inter-frame motion and surpassing existing methods across multiple metrics, demonstrating substantial clinical potential.