GPU-Accelerated Compartmental Modeling Analysis of DCE-MRI Data from Glioblastoma Patients Treated with Bevacizumab

The compartment model analysis using medical imaging data is the well-established but extremely time consuming technique for quantifying the changes in microvascular physiology of targeted organs in clinical patients after antivascular therapies. In this paper, we present a first graphics processing unit-accelerated method for compartmental modeling of medical imaging data. Using this approach, we performed the analysis of dynamic contrast-enhanced magnetic resonance imaging data from bevacizumab-treated glioblastoma patients in less than one minute per slice without losing accuracy. This approach reduced the computation time by more than 120-fold comparing to a central processing unit-based method that performed the analogous analysis steps in serial and more than 17-fold comparing to the algorithm that optimized for central processing unit computation. The method developed in this study could be of significant utility in reducing the computational times required to assess tumor physiology from dynamic contrast-enhanced magnetic resonance imaging data in preclinical and clinical development of antivascular therapies and related fields.

采用医学影像数据的隔室模型分析（compartment model analysis）是一项成熟但耗时极久的技术，用于量化临床患者接受抗血管治疗后靶器官的微血管生理学变化。本文首次提出了一种基于图形处理器（graphics processing unit，GPU）加速的医学影像数据隔室建模方法。借助该方法，我们对接受贝伐珠单抗治疗的胶质母细胞瘤患者的动态对比增强磁共振成像（dynamic contrast-enhanced magnetic resonance imaging，DCE-MRI）数据开展分析，单切片处理耗时不足一分钟且未损失分析精度。相较于串行执行同类分析步骤的中央处理器（central processing unit，CPU）基方法，该方法将计算耗时缩短120倍以上；相较于针对CPU计算优化的算法，耗时缩短幅度亦超过17倍。本研究开发的方法，在抗血管治疗及其相关领域的临床前与临床开发阶段中，可有效缩减从动态对比增强磁共振成像数据中评估肿瘤生理学特征所需的计算时长，具备重要应用价值。