A highly parallelized framework for computationally intensive MR data analysis

作者：Boubela Roland N; Huf Wolfgang; Kalcher Klaudius; Sladky Ronald; Filzmoser Peter; Pezawas Lukas; Kasper Siegfried; Windischberger Christian^*; Moser Ewald

来源：Magnetic Resonance Materials in Physics Biology and Medicine, 2012, 25(4): 313-320.

DOI：10.1007/s10334-011-0290-7

• Magnetic resonance imaging
• fMRI
• High performance computing
• Statistical computing

The goal of this study was to develop a comprehensive magnetic resonance (MR) data analysis framework for handling very large datasets with user-friendly tools for parallelization and to provide an example implementation. %26lt;br%26gt;Commonly used software packages (AFNI, FSL, SPM) were connected via a framework based on the free software environment R, with the possibility of using Nvidia CUDA GPU processing integrated for high-speed linear algebra operations in R. Three hundred single-subject datasets from the 1,000 Functional Connectomes project were used to demonstrate the capabilities of the framework. %26lt;br%26gt;A framework for easy implementation of processing pipelines was developed and an R package for the example implementation of Fully Exploratory Network ICA was compiled. Test runs on data from 300 subjects demonstrated the computational advantages of a processing pipeline developed using the framework compared to non-parallelized processing, reducing computation time by a factor of 15. %26lt;br%26gt;The feasibility of computationally intensive exploratory analyses allows broader access to the tools for discovery science.

• 出版日期2012-8