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Reproducible Tract Profiles (RTP): from diffusion MRI acquisition to publication
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AbstractReproducible Tract Profiles (RTP) comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography. The tools take MRI data from the scanner and process them through a series of analysis implemented as Docker containers that are integrated into a modern neuroinformatics platform (Flywheel). The platform guarantees that the entire pipeline can be re-executed, using the same data and computational parameters. In this paper, we describe (1) a cloud based neuroinformatics platform, (2) a tool to programmatically access and control the platform from a client, and (3) the DWI analysis tools that are used to identify the positions of 22 tracts and their diffusion profiles. The combination of these three components defines a system that transforms raw data into reproducible tract profiles for publication.Graphical abstractReproducible Tract Profiles (RTP) comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography. The RTP methods comprise two main parts.
Server side software tools for storing data and metadata and managing containerized computations.Client side software tools that enable the researcher to read data and metadata and manage server-side computations.The server-side computational tools are embedded in containers that are linked to a JSON file with a complete specification of the computational parameters. The data and computational infrastructure on the server is fully reproducible.
Title: Reproducible Tract Profiles (RTP): from diffusion MRI acquisition to publication
Description:
AbstractReproducible Tract Profiles (RTP) comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography.
The tools take MRI data from the scanner and process them through a series of analysis implemented as Docker containers that are integrated into a modern neuroinformatics platform (Flywheel).
The platform guarantees that the entire pipeline can be re-executed, using the same data and computational parameters.
In this paper, we describe (1) a cloud based neuroinformatics platform, (2) a tool to programmatically access and control the platform from a client, and (3) the DWI analysis tools that are used to identify the positions of 22 tracts and their diffusion profiles.
The combination of these three components defines a system that transforms raw data into reproducible tract profiles for publication.
Graphical abstractReproducible Tract Profiles (RTP) comprises a set of methods to manage and analyze diffusion weighted imaging (DWI) data for reproducible tractography.
The RTP methods comprise two main parts.
Server side software tools for storing data and metadata and managing containerized computations.
Client side software tools that enable the researcher to read data and metadata and manage server-side computations.
The server-side computational tools are embedded in containers that are linked to a JSON file with a complete specification of the computational parameters.
The data and computational infrastructure on the server is fully reproducible.
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