Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study.

ABSTRACT: Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or

SUBMITTER: Kebiri H 

PROVIDER: S-EPMC10327173 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature