MaPPeRTrac: A Massively Parallel, Portable, and Reproducible Tractography Pipeline.

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-04-01 Epub Date: 2024-03-06 DOI:10.1007/s12021-024-09650-0
Lanya T Cai, Joseph Moon, Paul B Camacho, Aaron T Anderson, Won Jong Chwa, Bradley P Sutton, Amy J Markowitz, Eva M Palacios, Alexis Rodriguez, Geoffrey T Manley, Shivsundaram Shankar, Peer-Timo Bremer, Pratik Mukherjee, Ravi K Madduri
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Abstract

Large-scale diffusion MRI tractography remains a significant challenge. Users must orchestrate a complex sequence of instructions that requires many software packages with complex dependencies and high computational costs. We developed MaPPeRTrac, an edge-centric tractography pipeline that simplifies and accelerates this process in a wide range of high-performance computing (HPC) environments. It fully automates either probabilistic or deterministic tractography, starting from a subject's magnetic resonance imaging (MRI) data, including structural and diffusion MRI images, to the edge density image (EDI) of their structural connectomes. Dependencies are containerized with Singularity (now called Apptainer) and decoupled from code to enable rapid prototyping and modification. Data derivatives are organized with the Brain Imaging Data Structure (BIDS) to ensure that they are findable, accessible, interoperable, and reusable following FAIR principles. The pipeline takes full advantage of HPC resources using the Parsl parallel programming framework, resulting in the creation of connectome datasets of unprecedented size. MaPPeRTrac is publicly available and tested on commercial and scientific hardware, so it can accelerate brain connectome research for a broader user community. MaPPeRTrac is available at: https://github.com/LLNL/mappertrac .

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MaPPeRTrac:大规模并行、便携、可重现的痕量成像管道。
大规模弥散核磁共振成像束成像仍然是一项重大挑战。用户必须协调一连串复杂的指令,这就需要许多软件包,而这些软件包具有复杂的依赖性和高昂的计算成本。我们开发了MaPPeRTrac,这是一个以边缘为中心的牵引成像流水线,可在各种高性能计算(HPC)环境中简化并加速这一过程。它能从受试者的磁共振成像(MRI)数据(包括结构和弥散 MRI 图像)到其结构连接体的边缘密度图像(EDI),实现概率或确定性牵引成像的完全自动化。依赖项通过 Singularity(现称为 Apptainer)进行容器化,并与代码解耦,以实现快速原型设计和修改。数据衍生物采用脑成像数据结构(BIDS)进行组织,以确保它们可查找、可访问、可互操作,并遵循 FAIR 原则可重复使用。该管道使用 Parsl 并行编程框架,充分利用了高性能计算资源,从而创建了规模空前的连接组数据集。MaPPeRTrac 公开可用,并在商业和科学硬件上进行了测试,因此可以为更广泛的用户群加速大脑连接组研究。MaPPeRTrac可在以下网址获取:https://github.com/LLNL/mappertrac 。
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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
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