Lori Nicolás F, Ibañez Augustin, Lavrador Rui, Fonseca Lucia, Santos Carlos, Travasso Rui, Pereira Artur, Rossetti Rosaldo, Sousa Nuno, Alves Victor
Algoritmi Centre, University of Minho, Braga, Portugal.
Laboratory of Neuroimaging and Neuroscience (LANEN), Institute of Translational and Cognitive Neuroscience (INCyT), INECO Foundation Rosario, Favaloro University, Rosario, Argentina.
J Med Syst. 2016 Nov;40(11):243. doi: 10.1007/s10916-016-0594-2. Epub 2016 Sep 29.
High Angular Resolution Diffusion Imaging (HARDI) is a type of brain imaging that collects a very large amount of data, and if many subjects are considered then it amounts to a big data framework (e.g., the human connectome project has 20 Terabytes of data). HARDI is also becoming increasingly relevant for clinical settings (e.g., detecting early cerebral ischemic changes in acute stroke, and in pre-clinical assessment of white matter-WM anatomy using tractography). Thus, this method is becoming a routine assessment in clinical settings. In such settings, the computation time is critical, and finding forms of reducing the processing time in high computation processes such as Diffusion Spectrum Imaging (DSI), a form of HARDI data, is very relevant to increase data-processing speed. Here we analyze a method for reducing the computation time of the dMRI-based axonal orientation distribution function h by using Monte Carlo sampling-based methods for voxel selection. Results evidenced a robust reduction in required data sampling of about 50 % without losing signal's quality. Moreover, we show that the convergence to the correct value in this type of Monte Carlo HARDI/DSI data-processing has a linear improvement in data-processing speed of the ODF determination. Although further improvements are needed, our results represent a promissory step for future processing time reduction in big data.
高角分辨率扩散成像（HARDI）是一种脑成像技术，它会收集大量数据，如果考虑众多受试者，那么这就构成了一个大数据框架（例如，人类连接组计划有20太字节的数据）。HARDI在临床环境中也变得越来越重要（例如，在急性卒中中检测早期脑缺血变化，以及在使用纤维束成像对白质 - WM解剖结构进行临床前评估）。因此，这种方法正成为临床环境中的常规评估手段。在这种情况下，计算时间至关重要，在诸如扩散谱成像（DSI，一种HARDI数据形式）这样的高计算过程中找到减少处理时间的方法，对于提高数据处理速度非常重要。在这里，我们分析了一种通过基于蒙特卡罗采样的体素选择方法来减少基于扩散磁共振成像（dMRI）的轴突方向分布函数h的计算时间的方法。结果表明所需数据采样稳健减少了约50%，同时不损失信号质量。此外，我们表明在这种蒙特卡罗HARDI/DSI数据处理中向正确值的收敛在ODF确定的数据处理速度上有线性提升。尽管还需要进一步改进，但我们的结果代表了未来在大数据处理时间减少方面的一个有希望的进展。
