Division of Clinical Pharmacology and Therapeutics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.
Magn Reson Imaging. 2013 May;31(4):618-23. doi: 10.1016/j.mri.2012.09.007. Epub 2012 Nov 30.
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is often used to examine vascular function in malignant tumors and noninvasively monitor drug efficacy of antivascular therapies in clinical studies. However, complex numerical methods used to derive tumor physiological properties from DCE-MRI images can be time-consuming and computationally challenging. Recent advancement of computing technology in graphics processing unit (GPU) makes it possible to build an energy-efficient and high-power parallel computing platform for solving complex numerical problems. This study develops the first reported fast GPU-based method for nonparametric kinetic analysis of DCE-MRI data using clinical scans of glioblastoma patients treated with bevacizumab (Avastin®). In the method, contrast agent concentration-time profiles in arterial blood and tumor tissue are smoothed using a robust kernel-based regression algorithm in order to remove artifacts due to patient motion and then deconvolved to produce the impulse response function (IRF). The area under the curve (AUC) and mean residence time (MRT) of the IRF are calculated using statistical moment analysis, and two tumor physiological properties that relate to vascular permeability, volume transfer constant between blood plasma and extravascular extracellular space (K(trans)) and fractional interstitial volume (ve) are estimated using the approximations AUC/MRT and AUC. The most significant feature in this method is the use of GPU-computing to analyze data from more than 60,000 voxels in each DCE-MRI image in parallel fashion. All analysis steps have been automated in a single program script that requires only blood and tumor data as the sole input. The GPU-accelerated method produces K(trans) and ve estimates that are comparable to results from previous studies but reduces computational time by more than 80-fold compared to a previously reported central processing unit-based nonparametric method. Furthermore, it is at least several orders of magnitudes faster than standard parametric methods that perform compartmental modeling. This finding indicates that the GPU-based method can significantly shorten the computational times required to assess tumor physiology from DCE-MRI data in preclinical and clinical development of antivascular therapies.
动态对比增强磁共振成像（DCE-MRI）常用于检查恶性肿瘤的血管功能，并在临床研究中无创监测抗血管治疗药物的疗效。然而，从 DCE-MRI 图像中推导出肿瘤生理特性的复杂数值方法可能既耗时又具有计算挑战性。图形处理单元（GPU）计算技术的最新进展使得构建一个节能且高性能的并行计算平台成为可能，以解决复杂的数值问题。本研究开发了一种基于 GPU 的快速非参数动力学分析方法，用于分析接受贝伐单抗（Avastin®）治疗的胶质母细胞瘤患者的 DCE-MRI 数据。在该方法中，使用基于稳健核的回归算法对动脉血和肿瘤组织中的对比剂浓度-时间曲线进行平滑处理，以消除由于患者运动引起的伪影，然后对其进行反卷积以产生脉冲响应函数（IRF）。使用统计矩分析计算 IRF 的曲线下面积（AUC）和平均停留时间（MRT），并使用 AUC/MRT 和 AUC 两个近似值来估计与血管通透性相关的两个肿瘤生理特性，即血液和血管外细胞外空间之间的体积转移常数（K(trans)）和间质体积分数（ve）。该方法的最大特点是使用 GPU 计算并行分析每个 DCE-MRI 图像中超过 60000 个体素的数据。所有分析步骤都已在单个程序脚本中自动化，该脚本仅需要血液和肿瘤数据作为唯一输入。GPU 加速方法生成的 K(trans)和 ve 估计值与之前的研究结果相当，但与之前报道的基于中央处理器的非参数方法相比，计算时间减少了 80 多倍。此外，它比执行房室建模的标准参数方法快几个数量级。这一发现表明，基于 GPU 的方法可以大大缩短评估抗血管治疗药物在临床前和临床开发中从 DCE-MRI 数据中获得的肿瘤生理学所需的计算时间。
