Diffusion Weighted Imaging Super-Resolution Algorithm for Highly Sparse Raw Data Sequences

Abstract

The utilization of quick compression-sensed magnetic resonance imaging results in an enhancement of diffusion imaging. Wasserstein Generative Adversarial Networks (WGANs) leverage image-based information. The article presents a novel G-guided generative multilevel network, which leverages diffusion weighted imaging (DWI) input data with constrained sampling. The present study aims to investigate two primary concerns pertaining to MRI image reconstruction, namely, image resolution and reconstruction duration. The implementation of simultaneous k-q space sampling has been found to enhance the performance of Rotating Single-Shot Acquisition (RoSA) without necessitating any hardware modifications. Diffusion weighted imaging (DWI) is capable of decreasing the duration of testing by minimizing the amount of input data required. The synchronization of diffusion directions within PROPELLER blades is achieved through the utilization of compressed k-space synchronization. The grids utilized in DW-MRI are represented by minimal-spanning trees. The utilization of conjugate symmetry in sensing and the Partial Fourier approach has been observed to enhance the efficacy of data acquisition as compared to unaltered k-space sampling systems. The image’s sharpness, edge readings, and contrast have been enhanced. These achievements have been certified by numerous metrics including PSNR and TRE. It is desirable to enhance image quality without necessitating any modifications to the hardware.