Finding the limits of deep learning clinical sensitivity with fractional anisotropy (FA) microstructure maps.

ABSTRACT:

Background

Quantitative maps obtained with diffusion weighted (DW) imaging, such as fractional anisotropy (FA) -calculated by fitting the diffusion tensor (DT) model to the data,-are very useful to study neurological diseases. To fit this map accurately, acquisition times of the order of several minutes are needed because many noncollinear DW volumes must be acquired to reduce directional biases. Deep learning (DL) can be used to reduce acquisition times by reducing the number of DW volumes. We already developed a DL network named "one-minute FA," which uses 10 DW volumes to obtain FA maps, maintaining the same characteristics and clinical sensitivity of the FA maps calculated with the standard method using more volumes. Recent publications have indicated that it is possible to tra

SUBMITTER: Gaviraghi M 

PROVIDER: S-EPMC11199892 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature