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.
Abstract (translated)
Susceptibility Map Weighted Imaging (SMWI) 是一种先进的磁共振成像技术，用于检测帕金森病中的黑质高信号。然而，全分辨率的 SMWI 获取受到长时间扫描的影响。因此，需要高效的重建方法来利用减少的空间(k空间)数据生成高质量的 SMWI 图像并保持诊断相关性。 在这项工作中，我们提出了一种基于复数Swin Transformer网络的方法，用于多回波MRI数据的超分辨率重建。该方法可以从低分辨率k空间输入中重构出高质量的SMWI图像。实验结果表明，在从256×256 k空间数据重构 SMWI 时，所提方法在结构相似性指数 (SSIM) 和均方误差 (MSE) 分别达到0.9116和0.076的同时，保持了关键的诊断特征。这种方法使SMWI可以从减少的空间采样中进行高质量重建，从而缩短扫描时间而不损害诊断细节。 该方法有望提高 SMWI 在帕金森病临床应用中的适用性，并支持更快、更高效的神经影像工作流程。
URL
https://arxiv.org/abs/2512.22202
