Paper Info
Title
Brain Tissue Microstructure Characterization Using dMRI Based Autoencoder Neural-Networks
Abstract
In recent years, multi-compartmental models have been widely used to try to characterize brain tissue microstructure from Diffusion Magnetic Resonance Imaging (dMRI) data. One of the main drawbacks of this approach is that the number of microstructural features needs to be decided a priori and it is embedded in the model definition. However, the number of microstructural features which is possible to obtain from dMRI data given the acquisition scheme is still not clear. In this work, we aim at characterizing brain tissue using autoencoder neural networks in combination with rotation-invariant features. By changing the number of neurons in the autoencoder latent-space, we can effectively control the number of microstructural features that we obtained from the data. By plotting the autoencoder reconstruction error to the number of features we were able to find the optimal trade-off between data fidelity and the number of microstructural features. Our results show how this number is impacted by the number of shells and the b-values used to sample the dMRI signal. We also show how our technique paves the way to a richer characterization of the brain tissue microstructure in-vivo.
Year
DOI
Venue
2021
10.1007/978-3-030-87615-9_5
COMPUTATIONAL DIFFUSION MRI, CDMRI 2021
Keywords
DocType
Volume
Spherical harmonics, Rotation invariant features, Neural networks, Diffusion MRI, Autoencoder
Conference
13006
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
3
Name
Order
Citations
PageRank
Mauro Zucchelli1265.91
Samuel Deslauriers-Gauthier2305.59
Rachid Deriche34903633.65