我們開發了一種基於生成對抗網絡產生的虛擬位移場對面迴訊影像進行無參考的失真校正方法。網絡模型的輸入是擴散張量影像數據集的三維原始b0影像，輸出目標是從反向梯度校正方法導出的相應位移場。獲得的模型使用原始b0影像預測虛擬位移場。根據虛擬位移場校正來自整個擴散張量影像數據集的面迴訊影像。該方法的性能在大型數據庫上進行了跨機構評估。結果表明，所提出的方法有效地減少了面迴訊影像數據集的幾何失真，提高了擴散指數的準確性，並顯著改善了面迴訊影像和高分辨率T1加權影像之間的對位（p < 0.01）。此方法是全自動的，其可獨立執行的應用程式已放至公眾共享。它是一種實用工具，可以減少回顧性研究中的面迴訊影像失真，這些研究使用在沒有參考場圖或反向梯度掃描的情況下獲取的面迴訊影像數據集。

We developed a referenceless echo-planar imaging (EPI) distortion correction method based on a virtual displacement map derived from a generative adversarial network. The input of the network model was a three-dimensional raw b0 volume of a diffusion-tensor dataset, and the output target was the corresponding displacement map derived from a reversed-gradient correction method. The obtained model allowed the prediction of a virtual displacement map with only raw b0 images. The EPI images from an entire diffusion dataset were then corrected based on the displacement map. The method's performance was cross-institutionally evaluated on large-scale databases. The results suggested that the proposed method effectively reduced the geometric distortion of the EPI datasets, increased the accuracy of diffusion indices, and significantly improved co-registration between EPI and high-resolution T1-weighted images (p < 0.01). The developed method is fully automatic, and the obtained standalone application has been shared with the public. It could be a practical tool to reduce EPI distortion in retrospective studies with EPI datasets acquired without reference field maps or reversed-gradient scans.

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