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High angular resolution diffusion imaging of neurodevelopment in children through data creation with deep learning.

Olayinka Oladosu1,2, Fanny Lo3, Bryce Geeraert4

  • 1Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, T2N 4N1, Canada. olayinka.oladosu1@ucalgary.ca.

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|June 28, 2026
PubMed
Summary
This summary is machine-generated.

Deep learning can generate high-angular resolution diffusion imaging (HARDI) data, reducing pediatric brain scan times by half. This method aids in understanding neurodevelopment and sex-specific brain differences.

Keywords:
Data creationDeep learningHigh angular resolution diffusion imagingNeurodevelopmentPediatric imagingTract-based analysis

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Developmental Neuroscience

Background:

  • High-angular resolution diffusion imaging (HARDI) offers detailed brain microstructure insights but is time-intensive.
  • Current HARDI acquisition protocols pose challenges for pediatric populations due to prolonged scanning times.

Purpose of the Study:

  • To develop a deep learning model for generating non-acquired HARDI data from existing lower-resolution datasets.
  • To evaluate the utility of deep learning-generated HARDI data in assessing neurodevelopment in children.

Main Methods:

  • A voxel-wise deep learning approach was employed to predict high b-value (2000 s/mm²) HARDI data from lower b-value (750 s/mm²) data in pediatric subjects.
  • Model performance was optimized by varying training sample size, window dimensions, and incorporating brain segmentation inputs.
  • Tract-based analysis was conducted on 71 children (aged 2-10 years) using both source-only and predicted HARDI data.

Main Results:

  • Deep learning model performance improved with larger training datasets, increased window sizes, and the inclusion of brain segmentation data.
  • Tract-specific HARDI metrics derived from predicted data closely mirrored those from source-only data, showing age-related increases.
  • Consistent sex differences in tract volumes were observed between predicted and source-only datasets, with the exception of the pyramidal tract.

Conclusions:

  • Deep learning-based HARDI data generation is a feasible approach for pediatric neuroimaging, potentially halving scan duration.
  • This technique facilitates the characterization of neurodevelopmental trajectories and sex-specific variations in brain structure in children.