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Magnetic Resonance Imaging01:24

Magnetic Resonance Imaging

Magnetic resonance imaging (MRI) is a noninvasive medical imaging technique based on a phenomenon of nuclear physics discovered in the 1930s, in which matter exposed to magnetic fields and radio waves was found to emit radio signals. In 1970, a physician and researcher named Raymond Damadian noticed that malignant (cancerous) tissue gave off different signals than normal body tissue. He applied for a patent for the first MRI scanning device in clinical use by the early 1980s. The early MRI...

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The connectome mapper: an open-source processing pipeline to map connectomes with MRI.

Alessandro Daducci1, Stephan Gerhard, Alessandra Griffa

  • 1Signal Processing Laboratory, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland. alessandro.daducci@epfl.ch

Plos One
|December 29, 2012
PubMed
Summary

The Connectome Mapper is an open-source Python pipeline that simplifies processing diffusion magnetic resonance imaging (dMRI) data for global brain connectivity analysis. It streamlines data organization, processing, and analysis for researchers.

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Image Analysis

Background:

  • Diffusion magnetic resonance imaging (dMRI) enables global brain connectivity analysis.
  • Existing software packages are often task-specific and use disparate file formats.
  • This fragmentation complicates data organization and processing.

Purpose of the Study:

  • To present the Connectome Mapper, a unified software pipeline.
  • To facilitate the organization, processing, and analysis of dMRI data for connectivity studies.
  • To provide an open-source solution for researchers.

Main Methods:

  • Development of a Python-based software pipeline.
  • Integration of methods for local intra-voxel structure reconstruction.
  • Inclusion of algorithms for fibre tract trajectory estimation.
  • Ensuring compatibility with various dMRI data processing steps.

Main Results:

  • A comprehensive pipeline for global brain connectivity analysis.
  • Streamlined workflow for dMRI data organization and processing.
  • Open-source availability promoting wider research adoption.

Conclusions:

  • The Connectome Mapper simplifies complex dMRI analysis workflows.
  • It offers a unified and accessible tool for brain connectivity research.
  • The pipeline supports researchers in advancing the field of connectomics.