A protocol for the analysis of DTI data collected from young children

Maksym Tokariev1,2, Virve Vuontela1,2, Jaana Perkola3

  • 1Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland.

Methodsx
|May 9, 2020
PubMed

Insights

This study presents a specialized diffusion tensor imaging (DTI) protocol for analyzing pediatric brain microstructure. The optimized DTI methods ensure robust analysis in children, crucial for understanding neurodevelopment.

Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Biomedical Engineering

Background:

  • Diffusion Tensor Imaging (DTI) provides insights into brain white matter microstructure.
  • Pediatric DTI requires tailored protocols due to developmental differences and motion artifacts.
  • Standard DTI analysis methods can be biased by inadequate normalization and tensor reconstruction in children.

Purpose of the Study:

  • To present a novel DTI acquisition sequence and analysis pipeline specifically designed for pediatric populations.
  • To address challenges in DTI data analysis for extremely preterm-born children.
  • To improve the robustness and accuracy of DTI-based brain microstructure analysis in pediatric cohorts.

Main Methods:

  • Development of a pediatric-specific DTI acquisition protocol balancing scan time, diffusion parameters, and analysis needs.
  • Utilized multiple software packages for artifact correction and robust tensor estimation.
  • Created a population-specific template for nonlinear registration to enhance brain image alignment in children.

Main Results:

  • The proposed protocol and analysis steps were applied to DTI data from extremely preterm-born school-aged children and controls.
  • The methods effectively addressed artifacts and ensured reliable tensor estimation.
  • Improved alignment of pediatric brain images was achieved through population-specific template registration.

Conclusions:

  • The presented DTI acquisition and analysis protocol is suitable for pediatric populations, including vulnerable groups like preterm infants.
  • This approach enhances the reliability of DTI studies investigating brain microstructure in children.
  • The findings support the use of optimized DTI methods for understanding neurodevelopmental trajectories.

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