用无参自正则化最优传输修复脑成像的几何失真，速度快精度高。
SuCor: Susceptibility Distortion Correction via Parameter-Free and Self-Regularized Optimal Transport
- 通过最优传输建模相位编码方向的失真，自动选择正则化强度。
- 在HCP数据上与结构图像互信息达0.341，优于FSL TOPUP的0.317。
- 单核CPU仅需12秒，无需人工调参，适合大规模脑影像处理。
我们提出SuCor，一种利用最优传输（OT）修正回波平面成像（EPI）中由磁敏感性引起的几何失真方法，沿相位编码方向建模失真场。给定一对反向相位编码的EPI体积，将每个列的失真场建模为相反极性强度分布间的Wasserstein-2重心位移。在频域通过弯曲能惩罚进行正则化，其强度由Morozov不一致原理自动选择，无需人工调参。在包含左右对称的b0 EPI对和配准的T1结构参考图像的人类连接组项目（HCP）数据集上，SuCor与T1图像的平均体素互信息达到0.341，优于FSL TOPUP的0.317，且仅需约12秒单核CPU运行时间。
原文摘要 · Abstract (English)
We present SuCor, a method for correcting susceptibility induced geometric distortions in echo planar imaging (EPI) using optimal transport (OT) along the phase encoding direction. Given a pair of reversed phase encoding EPI volumes, we model each column of the distortion field as a Wasserstein-2 barycentric displacement between the opposing-polarity intensity profiles. Regularization is performed in the spectral domain using a bending-energy penalty whose strength is selected automatically via the Morozov discrepancy principle, requiring no manual tuning. On a human connectome project (HCP) dataset with left-right/right-left b0 EPI pairs and a co-registered T1 structural reference, SuCor achieves a mean volumetric mutual information of 0.341 with the T1 image, compared to 0.317 for FSL TOPUP, while running in approximately 12 seconds on a single CPU core.
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