马普特拉克:一个大规模的并行,便携式和可重复的曲谱管道
Lanya T Cai1, Joseph Moon2, Paul B Camacho3
1Department of Radiology and Biomedical Imaging, University of California, San Francisco, 185 Berry St., San Francisco, CA, 94107, USA.
Neuroinformatics
|March 6, 2024
概括
在高性能计算 (HPC) 环境中,MaPPeRTrac简化和加速了大规模的扩散磁力共振成像 (MRI) 曲谱. 这条自动化管道有效地产生结构连接体,使先进的大脑成像研究更容易获得.
科学领域:
- 神经成像是一种神经成像.
- 计算神经科学是一种神经科学.
- 高性能计算 高性能计算
背景情况:
- 扩散核磁共振扫描是计算密集的,需要复杂的软件编排.
- 目前的方法对大规模连接组分析提出了重大挑战.
研究的目的:
- 开发一个自动化,高效和用户友好的曲谱管道.
- 为了简化和加速从MRI数据生成结构连接体.
- 为了提高大规模的大脑连接组研究的可访问性.
主要方法:
- 开发了MaPPeRTrac,这是一个以边缘为中心的曲谱管道,用于自动化概率或确定性曲谱.
- 使用奇点 (Apptainer) 的容器化依赖关系和使用脑成像数据结构 (BIDS) 组织的数据.
- 利用Parsl平行编程框架,有效利用高性能计算 (HPC) 的资源.
主要成果:
- MaPPeRTrac完全自动化了从MRI数据到结构连接体的边缘密度图像 (EDI) 的过程.
- 该管道展示了在多种HPC环境上显著加速和简化曲谱.
- 启用了前所未有的规模的连接组数据集的创建.
结论:
- MaPPeRTrac简化和加速扩散核磁共振扫描,克服了以前的计算和依赖性挑战.
- 管道的设计促进了FAIR数据原则,并扩大了对先进的连接组研究的访问.
- 公共可用性和跨平台兼容性促进了神经成像社区更广泛的采用.
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