Bibliographic Details
| Title: |
A generalized deep learning network for fractional anisotropy reconstruction: Application to epilepsy and multiple sclerosis |
| Authors: |
Marta Gaviraghi, Antonio Ricciardi, Fulvia Palesi, Wallace Brownlee, Paolo Vitali, Ferran Prados, Baris Kanber, Claudia A. M. Gandini Wheeler-Kingshott |
| Source: |
Frontiers in Neuroinformatics, Vol 16 (2022) |
| Publisher Information: |
Frontiers Media S.A., 2022. |
| Publication Year: |
2022 |
| Collection: |
LCC:Neurosciences. Biological psychiatry. Neuropsychiatry |
| Subject Terms: |
deep learning, fractional anisotropy, diffusion weighted MRI, reduced acquisition time, temporal lobe epilepsy, multiple sclerosis, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571 |
| More Details: |
Fractional anisotropy (FA) is a quantitative map sensitive to microstructural properties of tissues in vivo and it is extensively used to study the healthy and pathological brain. This map is classically calculated by model fitting (standard method) and requires many diffusion weighted (DW) images for data quality and unbiased readings, hence needing the acquisition time of several minutes. Here, we adapted the U-net architecture to be generalized and to obtain good quality FA from DW volumes acquired in 1 minute. Our network requires 10 input DW volumes (hence fast acquisition), is robust to the direction of application of the diffusion gradients (hence generalized), and preserves/improves map quality (hence good quality maps). We trained the network on the human connectome project (HCP) data using the standard model-fitting method on the entire set of DW directions to extract FA (ground truth). We addressed the generalization problem, i.e., we trained the network to be applicable, without retraining, to clinical datasets acquired on different scanners with different DW imaging protocols. The network was applied to two different clinical datasets to assess FA quality and sensitivity to pathology in temporal lobe epilepsy and multiple sclerosis, respectively. For HCP data, when compared to the ground truth FA, the FA obtained from 10 DW volumes using the network was significantly better (p |
| Document Type: |
article |
| File Description: |
electronic resource |
| Language: |
English |
| ISSN: |
1662-5196 |
| Relation: |
https://www.frontiersin.org/articles/10.3389/fninf.2022.891234/full; https://doaj.org/toc/1662-5196 |
| DOI: |
10.3389/fninf.2022.891234 |
| Access URL: |
https://doaj.org/article/81f5fac28ec5410a869bbdb8561ad72c |
| Accession Number: |
edsdoj.81f5fac28ec5410a869bbdb8561ad72c |
| Database: |
Directory of Open Access Journals |
