Enhancing HARDI Reconstruction from Undersampled Data Via Multi-Context and Feature Inter-Dependency GAN

Abstract

In addition to the more traditional diffusion tensor imaging (DTI), over time, reconstruction techniques like HARDI have been proposed, which have a comparatively higher scanning time due to increased measurements, but are significantly better in the estimation of fiber structures. In order to make HARDI-based analysis faster, we propose an approach to reconstruct more HARDI volumes in q-space. The proposed GAN-based architecture leverages several modules, including a multi-context module, feature inter-dependencies module along-with numerous losses such as L1, adversarial, and total variation loss, to learn the transformation. The method is backed by some encouraging quantitative and visual results.