A Deep Learning-based Multi-Contrast MRI Registration Model with a Realistic Flow Field and Reduced Over-Smoothing Effect

To optimize DL-based medical registration models to produce a more realistic flow field and reduce the over-smoothing effect of deformable registration while keeping the generalizability of the multi-contrast registration model to work on all anatomical structures and contrast of MRI, we proposed an algorithm that adopts the image synthetic framework from SynthMorph and optimized with cycle-consistent loss. By experimenting with the jacobian loss, bidirectional loss, and cycle-consistent loss, we managed to further optimize the results of registered images. The evaluation of two MRI image datasets, the BraTS dataset, and the LSpine dataset, demonstrated the increased SSIM, PSNR, and LocalNCC.