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TORTOISEV4: Reimagining the NIH diffusion MRI processing pipeline.

M Okan Irfanoglu1, Amritha Nayak1, Paul Taylor2

  • 1Quantitative Medical Imaging Laboratory, NIBIB, National Institutes of Health, Bethesda, MD, United States.

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Summary
This summary is machine-generated.

Diffusion MRI data require robust pre-processing to address artifacts like motion and distortions. The TORTOISE software has been enhanced for faster, more adaptable processing in large, multi-site studies.

Keywords:
artifactsdiffusion MRIdistortionspipelinepreprocessing

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Area of Science:

  • Neuroimaging
  • Medical Physics
  • Biomedical Engineering

Background:

  • Diffusion MRI (dMRI) data are prone to various artifacts, including low signal-to-noise ratio (SNR), motion, and distortions.
  • Accurate quantitative analysis of dMRI data necessitates appropriate pre-processing before model fitting.
  • Evolving dMRI acquisition techniques and large-scale multi-site studies demand advanced processing tools.

Purpose of the Study:

  • To present the redesigned and enhanced TORTOISE software for dMRI data processing.
  • To address the need for fast, robust, and adaptable dMRI processing pipelines.
  • To provide summary reporting capabilities for identifying problematic dMRI data.

Main Methods:

  • Redesign and enrichment of the TORTOISE software ensemble.
  • Adaptation of the software to handle diverse artifacts and distortions.
  • Implementation of summary reporting features for data quality assessment.

Main Results:

  • TORTOISE has been significantly improved to meet the demands of modern dMRI studies.
  • The enhanced software is adaptable and capable of handling a variety of artifacts.
  • The tool facilitates processing for large, multi-site studies, including challenging populations.

Conclusions:

  • The enhanced TORTOISE software provides a robust solution for dMRI pre-processing.
  • It addresses the challenges posed by complex artifacts and large-scale studies.
  • TORTOISE supports accurate quantitative analysis in diverse neuroimaging research settings.