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Surface-driven registration method for the structure-informed segmentation of diffusion MR images.

Oscar Esteban1, Dominique Zosso2, Alessandro Daducci3

  • 1Biomedical Image Technologies (BIT), ETSI Telecomunicación, Universidad Politécnica de Madrid, Madrid, Spain; Centro de Investigación Biomédica en Red en Bioingeniería, Biomateriales y Nanomedicina (CIBER-BBN), Spain.

Neuroimage
|May 12, 2016
PubMed
Summary
This summary is machine-generated.

We developed regseg, a novel nonlinear registration method for diffusion MRI data processing. This tool accurately maps anatomical structures in dMRI space, improving brain connectivity analyses and overcoming current limitations.

Keywords:
Active surfacesCortical parcellationDiffusion MRINonlinear registrationSegmentationSusceptibility distortion

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Computational Neuroscience

Background:

  • Mapping human brain connectivity with diffusion MRI (dMRI) requires precise anatomical segmentation.
  • Existing methods struggle with dMRI's low signal-to-noise ratio, resolution, and geometrical distortions.
  • Current approaches involve either native dMRI space segmentation or mapping from T1-weighted (T1w) images, both with limitations.

Purpose of the Study:

  • To introduce regseg, a unified surface-to-volume nonlinear registration method for dMRI data processing.
  • To segment homogeneous regions in multivariate images by mapping nested reference surfaces.
  • To improve the accuracy of structural information mapping in dMRI space for enhanced brain connectivity analyses.

Main Methods:

  • Proposed regseg, a surface-to-volume nonlinear registration technique.
  • Extracted accurate surfaces from subject T1w images.
  • Used fractional anisotropy (FA) and apparent diffusion coefficient (ADC) maps from dMRI as the target image.
  • Verified accuracy using digital phantoms and Human Connectome Project (HCP) dMRI data with realistic deformations.

Main Results:

  • Regseg achieved a misregistration error with a 95% confidence interval of 0.56-0.66mm on HCP datasets, below dMRI resolution (1.25mm).
  • The method demonstrated significantly lower misregistration error compared to a nonlinear b0-to-T2w registration approach.
  • Verified accuracy on digital phantoms with synthetic and random deformations.

Conclusions:

  • Regseg offers accurate mapping of structural information within dMRI space.
  • This improves the reliability of network building in brain connectivity analyses.
  • Enhances the performance of emerging structure-informed dMRI data processing techniques.