Future Processing, Bojkowska 37A, 44-100 Gliwice, Poland; Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100 Gliwice, Poland.
Artif Intell Med. 2020 Jan;102:101769. doi: 10.1016/j.artmed.2019.101769. Epub 2019 Nov 27.
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发挥着重要作用。虽然手动 DCE 生物标志物提取算法通过提供肿瘤预后和预测的定量信息来提高 DCE-MRI 的诊断效果，但这些算法耗时且容易出错。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，可产生可重复的结果，并经过基准和临床数据的严格验证。此外，我们还引入了一种用于药代动力学建模的血管输入函数的立方模型，与最新技术相比，该模型显著降低了拟合误差，同时还引入了一种用于确定血管输入区域的实时算法。经过广泛的实验研究和统计测试，我们的系统在使用单个 GPU 处理整个输入 DCE-MRI 研究时，只需不到 3 分钟的时间就能提供最先进的结果。
