Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays an important role in diagnosis and grading of brain tumors. Although manual DCE biomarker extraction algorithms boost the diagnostic yield of DCE-MRI by providing quantitative information on tumor prognosis and prediction, they are time-consuming and prone to human errors. In this paper, we propose a fully-automated, end-to-end system for DCE-MRI analysis of brain tumors. Our deep learning-powered technique does not require any user interaction, it yields reproducible results, and it is rigorously validated against benchmark and clinical data. Also, we introduce a cubic model of the vascular input function used for pharmacokinetic modeling which significantly decreases the fitting error when compared with the state of the art, alongside a real-time algorithm for determination of the vascular input region. An extensive experimental study, backed up with statistical tests, showed that our system delivers state-of-the-art results while requiring less than 3 min to process an entire input DCE-MRI study using a single GPU.

动态对比增强磁共振成像（DCE-MRI）在脑肿瘤的诊断和分级中起着重要作用。尽管手动DCE生物标志物提取算法通过提供有关肿瘤预后和预测的定量信息来提高DCE-MRI的诊断率，但它们既耗时又容易出现人为错误。在本文中，我们提出了一种用于脑肿瘤DCE-MRI分析的全自动，端到端系统。我们的深度学习技术不需要任何用户交互，它可产生可重复的结果，并且已针对基准和临床数据进行了严格验证。此外，我们介绍了用于药代动力学建模的血管输入函数的三次模型，与现有技术相比，该模型显着降低了拟合误差，以及用于确定血管输入区域的实时算法。一项广泛的实验研究，并得到统计测试的支持，表明我们的系统提供了最先进的结果，而使用单个GPU只需不到3分钟的时间即可处理整个输入DCE-MRI研究。