仅用单向相位编码即可纠正扩散MRI的畸变，提升数据可用性。
Susceptibility Distortion Correction of Diffusion MRI with a single Phase-Encoding Direction
- 用深度学习从单次扫描中重建畸变图像，无需成对采集。
- 性能接近传统topup方法，几何畸变校正效果显著。
- 适合无法获取双方向数据的临床或回顾性研究场景。
扩散MRI（dMRI）通过分析组织中水分子的扩散行为，可有效映射脑微结构与连接性。但快速采集多组3D脑影像常导致图像质量妥协，其中最棘手的问题是磁敏感性引起的畸变，造成显著的几何与强度失真。传统校正方法如topup依赖于成对的blip-up和blip-down图像，限制了其在仅采集单相位编码方向的回顾性数据中的应用。本文提出一种基于深度学习的方法，仅需单次扫描（任一方向）即可实现畸变校正，无需配对采集。实验表明，该方法性能可媲美topup，具备高效、实用的潜力，为单向采集的dMRI数据提供有效的畸变校正方案。
原文摘要 · Abstract (English)
Diffusion MRI (dMRI) is a valuable tool to map brain microstructure and connectivity by analyzing water molecule diffusion in tissue. However, acquiring dMRI data requires to capture multiple 3D brain volumes in a short time, often leading to trade-offs in image quality. One challenging artifact is susceptibility-induced distortion, which introduces significant geometric and intensity deformations. Traditional correction methods, such as topup, rely on having access to blip-up and blip-down image pairs, limiting their applicability to retrospective data acquired with a single phase encoding direction. In this work, we propose a deep learning-based approach to correct susceptibility distortions using only a single acquisition (either blip-up or blip-down), eliminating the need for paired acquisitions. Experimental results show that our method achieves performance comparable to topup, demonstrating its potential as an efficient and practical alternative for susceptibility distortion correction in dMRI.
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