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

High-angular resolution diffusion imaging (HARDI) is an advanced method for characterizing brain microstructure and function. However, HARDI is time-consuming limiting real-world applications, especially in children. We aimed to address the challenge by creating non-acquired HARDI data through deep learning and testing utility in neurodevelopment. Brain diffusion magnetic resonance imaging from 95 children aged 2-10 years (49 females) were examined. Each subject included two source b-value datasets: 750 s/mm² and 2000 s/mm², 30 directions each with five b0 volumes. Deep learning was voxel-wise, approximately 1,620,000 voxels from 12-subject each matched on age and sex for training and validation, respectively, to predict b2000 s/mm² from b750 s/mm² dataset. Different training samples, window sizes, and tissue inputs were also assessed. The remaining 71 individuals were used for tract-based analysis of neurodevelopment, which involved eight brain white matter tracts. Results showed that model performance improved with an increasing training sample, large window size, and additional brain segmentation input. HARDI outcomes including predicted data were similar in pattern to source-only data across tracts and metrics, with all appeared to increase with age. Additionally, tract volumes showed consistent sex differences between prediction-integrated and source-only datasets in all but pyramidal tract. Collectively, deep learning-enabled HARDI is feasible for pediatric imaging, which may help reduce scan time by half and characterize sex-specific neurodevelopment.