Super-resolution generative adversarial networks with static T2*WI-based subject-specific learning to improve spatial difference sensitivity in fMRI activation

ABSTRACT: The spatial resolution of fMRI is relatively poor and improvements are needed to indicate more specific locations for functional activities. Here, we propose a novel scheme, called Static T2*WI-based Subject-Specific Super Resolution fMRI (STSS-SRfMRI), to enhance the functional resolution, or ability to discriminate spatially adjacent but functionally different responses, of fMRI. The scheme is based on super-resolution generative adversarial networks (SRGAN) that utilize a T2*-weighted image (T2*WI) dataset as a training reference. The efficacy of the scheme was evaluated through comparison with the activation maps obtained from the raw unpreprocessed functional data (raw fMRI). MRI images were acquired from 30 healthy volunteers using a 3 Tesla scanner. The modified SRGAN reconstructs a

SUBMITTER: Ota J 

PROVIDER: S-EPMC9209532 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature