磁共振成像 (MRI) 到计算机断层扫描 (CT) 的合成对于纯 MRI 放射治疗工作流程至关重要，尤其是通过以其准确性而闻名的深度学习技术。然而，当前的监督方法仅限于特定中心的学习并且依赖于配准精度。本研究的目的是评估用于放射治疗剂量计算的前列腺 MRI 到 CT 生成背景下无监督和监督方法的准确性。使用了来自三个不同中心的 99 名前列腺癌患者的 CT/MRI 图像对。对监督和无监督条件生成对抗网络（cGAN）进行了比较。无监督训练结合了风格迁移方法。增强感知合成（CREP）损失的内容和风格表示。对于剂量评估，在体积调制弧疗法 (VMAT) 中提供的光子处方剂量为 60 Gy。 sCT 评估的成像终点是平均绝对误差 (MAE)。剂量测定终点包括 CT 和 sCT 剂量计算之间的绝对剂量差异和伽玛分析。无监督配对网络对身体的 MAE 表现出最高准确度，为 33.6 HU，通过无监督无配对学习获得的最高 MAE 为 45.5 HU。所有架构都提供了临床上可接受的剂量计算结果，伽马通过率高于 94%（1% 1mm 10%）。这项研究表明，多中心数据可以通过无监督学习产生准确的 sCT，从而消除 CT-MRI 配准。 sCT 不仅与 HU 值相匹配，而且还能够进行精确的剂量计算，这表明它们在纯 MRI 放射治疗工作流程中具有更广泛使用的潜力。© 2024 作者。

Magnetic resonance imaging (MRI)-to-computed tomography (CT) synthesis is essential in MRI-only radiotherapy workflows, particularly through deep learning techniques known for their accuracy. However, current supervised methods are limited to specific center's learnings and depend on registration precision. The aim of this study was to evaluate the accuracy of unsupervised and supervised approaches in the context of prostate MRI-to-CT generation for radiotherapy dose calculation.CT/MRI image pairs from 99 prostate cancer patients across three different centers were used. A comparison between supervised and unsupervised conditional Generative Adversarial Networks (cGAN) was conducted. Unsupervised training incorporates a style transfer method with. Content and Style Representation for Enhanced Perceptual synthesis (CREPs) loss. For dose evaluation, the photon prescription dose was 60 Gy delivered in volumetric modulated arc therapy (VMAT). Imaging endpoint for sCT evaluation was Mean Absolute Error (MAE). Dosimetric endpoints included absolute dose differences and gamma analysis between CT and sCT dose calculations.The unsupervised paired network exhibited the highest accuracy for the body with a MAE at 33.6 HU, the highest MAE was 45.5 HU obtained with unsupervised unpaired learning. All architectures provided clinically acceptable results for dose calculation with gamma pass rates above 94 % (1 % 1 mm 10 %).This study shows that multicenter data can produce accurate sCTs via unsupervised learning, eliminating CT-MRI registration. The sCTs not only matched HU values but also enabled precise dose calculations, suggesting their potential for wider use in MRI-only radiotherapy workflows.© 2024 The Author(s).