AI Article Synopsis

  • This study analyzes diffusion metrics along white matter fiber tracts using advanced methods like DTI and NODDI, aiming for better microstructural characterization.
  • We compared data from 15 healthy individuals and 22 brain tumor patients to create a normative database of diffusion metrics.
  • NODDI metrics were found to more accurately reflect microstructural changes in tissue surrounding tumors compared to traditional DTI metrics, suggesting potential for future research on various pathological conditions.

Article Abstract

Along-tract statistics analysis enables the extraction of quantitative diffusion metrics along specific white matter fiber tracts. Besides quantitative metrics derived from classical diffusion tensor imaging (DTI), such as fractional anisotropy and diffusivities, new parameters reflecting the relative contribution of different diffusion compartments in the tissue can be estimated through advanced diffusion MRI methods as neurite orientation dispersion and density imaging (NODDI), leading to a more specific microstructural characterization. In this study, we extracted both DTI- and NODDI-derived quantitative microstructural diffusion metrics along the most eloquent fiber tracts in 15 healthy subjects and in 22 patients with brain tumors. We obtained a robust intraprotocol reference database of normative along-tract microstructural metrics, and their corresponding plots, from healthy fiber tracts. Each diffusion metric of individual patient's fiber tract was then plotted and statistically compared to the normative profile of the corresponding metric from the healthy fiber tracts. NODDI-derived metrics appeared to account for the pathological microstructural changes of the peritumoral tissue more accurately than DTI-derived ones. This approach may be useful for future studies that may compare healthy subjects to patients diagnosed with other pathological conditions.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7927309PMC
http://dx.doi.org/10.1002/hbm.25291DOI Listing

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