Smith David S, Li Xia, Arlinghaus Lori R, Yankeelov Thomas E, Welch E Brian
Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA.
Institute of Imaging Science, Vanderbilt University , Nashville, TN , USA ; Department of Radiology and Radiological Sciences, Vanderbilt University , USA ; Department of Physics and Astronomy, Vanderbilt University , USA ; Department of Cancer Biology, Vanderbilt University , USA.
PeerJ. 2015 Apr 23;3:e909. doi: 10.7717/peerj.909. eCollection 2015.
We present a fast, validated, open-source toolkit for processing dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) data. We validate it against the Quantitative Imaging Biomarkers Alliance (QIBA) Standard and Extended Tofts-Kety phantoms and find near perfect recovery in the absence of noise, with an estimated 10-20× speedup in run time compared to existing tools. To explain the observed trends in the fitting errors, we present an argument about the conditioning of the Jacobian in the limit of small and large parameter values. We also demonstrate its use on an in vivo data set to measure performance on a realistic application. For a 192 × 192 breast image, we achieved run times of <1 s. Finally, we analyze run times scaling with problem size and find that the run time per voxel scales as O(N (1.9)), where N is the number of time points in the tissue concentration curve. DCEMRI.jl was much faster than any other analysis package tested and produced comparable accuracy, even in the presence of noise.
我们展示了一个用于处理动态对比增强磁共振成像（DCE-MRI）数据的快速、经过验证的开源工具包。我们根据定量成像生物标志物联盟（QIBA）标准和扩展的Tofts-Kety体模对其进行了验证，发现在无噪声情况下能实现近乎完美的恢复，与现有工具相比，运行时间估计加快了10 - 20倍。为了解释拟合误差中观察到的趋势，我们针对小参数值和大参数值极限情况下雅可比矩阵的条件提出了一种观点。我们还展示了它在体内数据集上的应用，以衡量其在实际应用中的性能。对于一幅192×192的乳腺图像，我们实现了运行时间小于1秒。最后，我们分析了运行时间随问题规模的缩放情况，发现每体素的运行时间按O(N (1.9))缩放，其中N是组织浓度曲线中的时间点数。即使在存在噪声的情况下，DCEMRI.jl也比测试的任何其他分析软件包都快得多，并且产生了相当的准确性。
