Unsupervised Deep Learning for FOD-Based Susceptibility Distortion Correction in Diffusion MRI.

ABSTRACT: Susceptibility induced distortion is a major artifact that affects the diffusion MRI (dMRI) data analysis. In the Human Connectome Project (HCP), the state-of-the-art method adopted to correct this kind of distortion is to exploit the displacement field from the B0 image in the reversed phase encoding images. However, both the traditional and learning-based approaches have limitations in achieving high correction accuracy in certain brain regions, such as brainstem. By utilizing the fiber orientation distribution (FOD) computed from the dMRI, we propose a novel deep learning framework named DistoRtion Correction Net (DrC-Net), which consists of the U-Net to capture the latent information from the 4D FOD images and the spatial transformer network to propagate the displacement field and back

SUBMITTER: Qiao Y 

PROVIDER: S-EPMC9177803 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature