Keywords: AI/ML Image Reconstruction, Pediatric
Motivation: High angular resolution diffusion imaging has great potential but is time-consuming so is limited in pediatric clinical studies.
Goal(s): To assess the utility of novel deep learning techniques for predicting non-acquired brain diffusion MRI for equivalent HARDI analyses.
Approach: A multilayer perceptron (MLP) and convolutional neural network (CNN) were trained to predict b=2000s/mm2 data from b=750s/mm2 data. The neurite orientation dispersion and density index (NODDI) outcomes were computed with quality evaluated with PSNR and SSIM.
Results: Both deep learning methods achieved the goal but the CNN outperformed the MLP.
Impact: By applying a competitive neural network method, high angular resolution diffusion imaging can be made possible for the pediatric population in a typical clinical setting based only on half of the data typically required.
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