AxonNet: A Self-supervised Deep Neural Network for Intravoxel Structure Estimation from DW-MRIDownload PDF

Published: 16 May 2023, Last Modified: 16 May 2023Submitted to MIDL 2021Readers: Everyone
Keywords: Self-supervised neural network, Axonal structure estimation, DW-MRI, Multitensor
TL;DR: Computationally efficient and reliable deep neural networks for estimating intra-voxel structure from DW-MRI .
Abstract: We present a method for estimating of intravoxel parameters from a DW-MRI based on deep learning techniques. We show that deep neural networks (DNNs) have the potential to extract information from diffusion-weighted signals to reconstruct cerebral tracts. We present two DNN models: one that estimates the axonal structure in the form of a voxel and the other to calculate the structure of the central voxel using the voxel neighborhood. Our methods are based on a proposed parameter representation suitable for the problem. Since it is practically impossible to have real tagged data for any acquisition protocol, we used a self-supervised strategy. Experiments with synthetic data and real data show that our approach is competitive, and the computational times show that our approach is faster than the state of the art methods, even if training times are considered. This computational advantage increases if we consider the prediction of multiple images with the same acquisition protocol.
Registration: I acknowledge that publication of this at MIDL and in the proceedings requires at least one of the authors to register and present the work during the conference.
Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
Paper Type: methodological development
Primary Subject Area: Image Acquisition and Reconstruction
Secondary Subject Area: Learning with Noisy Labels and Limited Data
6 Replies

Loading