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Related Experiment Video

Updated: Jun 26, 2026

Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

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Published on: July 28, 2013

Biophysical Diffusion MRI Models Better Identify White Matter Tracts in Edema.

Isaac E Prentiss1, Sasha Hakhu1, Jennapher Lingo VanGilder1

  • 1School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ 85287, USA.

Tomography (Ann Arbor, Mich.)
|June 25, 2026
PubMed
Summary
This summary is machine-generated.

Biophysical diffusion models like NODDI and SM can identify white matter tracts in edematous regions, outperforming standard DTI. These advanced models offer improved presurgical planning for brain tumor resection.

Keywords:
DTINODDISMdiffusion MRIedematractographywhite matter

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Diffusion Tensor Magnetic Resonance Imaging in Chronic Spinal Cord Compression
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Published on: May 7, 2019

Area of Science:

  • Neuroimaging
  • Biophysical modeling
  • Diffusion MRI

Background:

  • White matter (WM) tract detection is crucial for presurgical planning.
  • Standard MRI techniques (T1/T2, DTI) struggle to identify WM tracts in edematous regions due to increased extracellular water and isotropic diffusion.
  • Advanced biophysical models (NODDI, SM) compartmentalize diffusion signals to overcome these limitations.

Purpose of the Study:

  • To evaluate if biophysical multi-compartment models can robustly identify WM tracts within edematous regions.
  • To assess the recovery of tractography streamlines in edematous areas using these advanced models.

Main Methods:

  • Utilized multi-shell diffusion-weighted MRI data from meningioma patients.
  • Compared fractional anisotropy (FA) from standard and free-water-corrected DTI, orientation dispersion index (ODI) from NODDI, and P2 from SM fODF.
  • Evaluated tractography performance across models in edematous and contralateral WM regions.

Main Results:

  • Biophysical model metrics (1 - ODI and P2) in edema closely matched contralateral measurements.
  • Standard DTI metrics (FA, FW-FA) showed substantial reductions in edematous regions.
  • (1 - ODI) slightly increased in edema (~8%), while P2 remained unchanged.

Conclusions:

  • Biophysical diffusion models demonstrate potential for robust WM tract identification in edematous brain regions.
  • These models offer improved preoperative mapping capabilities compared to standard DTI.
  • Advanced diffusion modeling aids in overcoming imaging challenges posed by edema during surgical planning.