用复数Swin Transformer加速帕金森病MRI重建，缩短扫描时间。
Complex Swin Transformer for Accelerating Enhanced SMWI Reconstruction
- 基于复数Swin Transformer实现多回波MRI超分辨率重建
- 256×256采样下结构相似性达0.9116，均方误差为0.076
- 保留关键诊断特征，适合神经影像快速筛查场景
磁敏感加权成像（SMWI）是一种用于检测帕金森病黑质高信号的先进MRI技术。然而，全分辨率SMWI采集受限于扫描时间长。因此需要高效重建方法，从缩减的k空间数据中生成高质量图像并保持诊断价值。本文提出一种基于复数Swin Transformer的网络，用于多回波MRI数据的超分辨率重建。该方法可从低分辨率k空间输入重建出高质量SMWI图像。实验表明，在256×256 k空间数据下，重构图像的结构相似性指数达到0.9116，均方误差为0.076，同时保持了关键诊断特征。该方法实现了在减少k空间采样条件下高质量SMWI重建，显著缩短扫描时间而不损失诊断细节，有望提升SMWI在帕金森病中的临床应用，并支持更快捷高效的神经影像工作流程。
原文摘要 · Abstract (English)
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.
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