背景 动态磁敏感性增强（DSC）磁共振成像获得的脑血容量（CBV）图对临床场景有用，但不常见。 目的 通过使用桥头磁共振成像作为参考标准，测试图像到图像转换技术在脑肿瘤的标准磁共振成像序列中生成CBV图的效果。 材料和方法 本回顾性研究纳入了756例磁共振成像检查，包括从桥头磁共振成像制作的定量CBV图。两种算法，即特征一致性生成对抗网络（GAN）和只有均方误差损失的三维编码器-解码器网络，被训练用于合成CBV图。 这两种算法的性能分别通过结构相似度指数（SSIM）进行定量评估，由两位神经放射科医师用四点Likert量表进行定性评估。采用多中心数据集（4个外部和1个内部）评估了合成的CBV图与脑肿瘤的标准磁共振成像的临床价值（肿瘤分级、预后预测、鉴别诊断）。通过z检验比较了诊断和预测准确性的差异。 结果 在将T1加权成像、增强T1加权成像和表观弥散系数图作为输入的三维编码器-解码器网络中，合成性能最佳（SSIM，86.29% ± 4.30）。神经放射科医师对合成的CBV图的平均定性评分为2.63。将合成的CBV与标准MRI相结合，提高了鉴别诊断脑胶质瘤 （标准MRI扫描下的ROC曲线下面积[AUC]为0.707；标准MRI扫描与CBV图的AUC为0.857；z = 15.17; P < .001），预测脑胶质瘤预后（标准MRI扫描AUC，0.654；标准MRI扫描与CBV图的AUC，0.793；z = 9.62；P < .001），以及鉴别脑胶质瘤复发和治疗反应 （标准MRI扫描AUC，0.778；标准MRI扫描与CBV图的AUC，0.853；z = 4.86；P < .001）和脑转移（标准MRI扫描AUC，0.749；标准MRI扫描与CBV图的AUC，0.857；z = 6.13；P < .001）。 结论 GAN图像到图像转换技术能够从标准MRI扫描中生成准确的合成CBV图，有助于改善脑肿瘤的临床评估。 在CC BY 4.0协议下发表。本文附有补充资料。此外详见该期刊中Branstetter的社论。

Background Cerebral blood volume (CBV) maps derived from dynamic susceptibility contrast-enhanced (DSC) MRI are useful but not commonly available in clinical scenarios. Purpose To test image-to-image translation techniques for generating CBV maps from standard MRI sequences of brain tumors using the bookend technique DSC MRI as ground-truth references. Materials and Methods A total of 756 MRI examinations, including quantitative CBV maps produced from bookend DSC MRI, were included in this retrospective study. Two algorithms, the feature-consistency generative adversarial network (GAN) and three-dimensional encoder-decoder network with only mean absolute error loss, were trained to synthesize CBV maps. The performance of the two algorithms was evaluated quantitatively using the structural similarity index (SSIM) and qualitatively by two neuroradiologists using a four-point Likert scale. The clinical value of combining synthetic CBV maps and standard MRI scans of brain tumors was assessed in several clinical scenarios (tumor grading, prognosis prediction, differential diagnosis) using multicenter data sets (four external and one internal). Differences in diagnostic and predictive accuracy were tested using the z test. Results The three-dimensional encoder-decoder network with T1-weighted images, contrast-enhanced T1-weighted images, and apparent diffusion coefficient maps as the input achieved the highest synthetic performance (SSIM, 86.29% ± 4.30). The mean qualitative score of the synthesized CBV maps by neuroradiologists was 2.63. Combining synthetic CBV with standard MRI improved the accuracy of grading gliomas (standard MRI scans area under the receiver operating characteristic curve [AUC], 0.707; standard MRI scans with CBV maps AUC, 0.857; z = 15.17; P < .001), prediction of prognosis in gliomas (standard MRI scans AUC, 0.654; standard MRI scans with CBV maps AUC, 0.793; z = 9.62; P < .001), and differential diagnosis between tumor recurrence and treatment response in gliomas (standard MRI scans AUC, 0.778; standard MRI scans with CBV maps AUC, 0.853; z = 4.86; P < .001) and brain metastases (standard MRI scans AUC, 0.749; standard MRI scans with CBV maps AUC, 0.857; z = 6.13; P < .001). Conclusion GAN image-to-image translation techniques produced accurate synthetic CBV maps from standard MRI scans, which could be used for improving the clinical evaluation of brain tumors. Published under a CC BY 4.0 license. Supplemental material is available for this article. See also the editorial by Branstetter in this issue.