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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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Automated vs. conventional tractography in multiple sclerosis: variability and correlation with disability.

Daniel S Reich1, Arzu Ozturk, Peter A Calabresi

  • 1Department of Radiology, Johns Hopkins University, Baltimore, MD 21287, USA. reichds@ninds.nih.gov

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|December 1, 2009
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Automated tract probability mapping offers more reliable brain white matter tract analysis in multiple sclerosis than conventional methods. This technique shows lower variability and better lesion handling, aiding clinical outcome studies.

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Area of Science:

  • Neuroimaging
  • Medical Image Analysis
  • Neurology

Background:

  • Diffusion-tensor-imaging fiber tractography is crucial for studying brain white matter tracts.
  • Conventional tractography is time-consuming, variable, and susceptible to structural changes in disease.
  • Automated segmentation is needed for clinical utility but faces challenges with altered brain structures.

Purpose of the Study:

  • To investigate an automated tract-probability-mapping scheme for brain white matter tract segmentation.
  • To compare the automated method with conventional tractography in multiple sclerosis.
  • To assess the clinical utility of automated tract analysis for linking imaging to disability.

Main Methods:

  • Applied an automated tract-probability-mapping scheme to multiple sclerosis patients.
  • Compared scan-rescan variability between automated and conventional tractography.
  • Correlated tract-specific MRI indices from both methods with clinical disability scores.

Main Results:

  • The automated method demonstrated significantly lower scan-rescan variability (0.7-1.5% vs. up to 3%).
  • Automated segmentation successfully navigated lesions, avoiding tractography failures seen in conventional methods.
  • Tract-specific MRI indices showed moderate to strong correlations between methods, with similar links to clinical disability, except in the optic tract where the automated method failed.

Conclusions:

  • Automated tract-probability mapping provides a more consistent and robust alternative to conventional tractography for specific white matter tracts.
  • The method's improved reliability and lesion handling make it valuable for research correlating imaging findings with clinical outcomes in neurological diseases.
  • Judicious application of automated tract analysis holds promise for advancing clinical neuroimaging research, despite limitations in certain tracts.