Neural networks for parameter estimation in microstructural MRI: Application to a diffusion-relaxation model of white matter.

ABSTRACT: Specific features of white matter microstructure can be investigated by using biophysical models to interpret relaxation-diffusion MRI brain data. Although more intricate models have the potential to reveal more details of the tissue, they also incur time-consuming parameter estimation that may converge to inaccurate solutions due to a prevalence of local minima in a degenerate fitting landscape. Machine-learning fitting algorithms have been proposed to accelerate the parameter estimation and increase the robustness of the attained estimates. So far, learning-based fitting approaches have been restricted to microstructural models with a reduced number of independent model parameters where dense sets of training data are easy to generate. Moreover, the degree to which machine learning can a

SUBMITTER: de Almeida Martins JP 

PROVIDER: S-EPMC9651573 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature