Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction

TL;DR

This paper introduces a complex Swin Transformer network that efficiently reconstructs high-quality susceptibility map weighted images from reduced MRI data, enabling faster scans without losing diagnostic details.

Contribution

It presents a novel complex-valued Swin Transformer architecture specifically designed for super-resolution reconstruction of SMWI from low-resolution k-space data.

Findings

Achieves SSIM of 0.9116 and MSE of 0.076 on 256x256 data

Reconstructs high-quality SMWI from reduced k-space sampling

Supports shorter scan times without diagnostic loss

Abstract

Susceptibility Map Weighted Imaging (SMWI) is an advanced magnetic resonance imaging technique used to detect nigral hyperintensity in Parkinsons disease. However, full resolution SMWI acquisition is limited by long scan times. Efficient reconstruction methods are therefore required to generate high quality SMWI from reduced k space data while preserving diagnostic relevance. In this work, we propose a complex valued Swin Transformer based network for super resolution reconstruction of multi echo MRI data. The proposed method reconstructs high quality SMWI images from low resolution k space inputs. Experimental results demonstrate that the method achieves a structural similarity index of 0.9116 and a mean squared error of 0.076 when reconstructing SMWI from 256 by 256 k space data, while maintaining critical diagnostic features. This approach enables high quality SMWI reconstruction from reduced k space sampling, leading to shorter scan times without compromising diagnostic detail. The proposed method has the potential to improve the clinical applicability of SMWI for Parkinsons disease and support faster and more efficient neuroimaging workflows.