Deep learning brain conductivity mapping using a patch-based 3D U-net

TL;DR

This paper explores deep learning for brain conductivity mapping using a 3D U-net, demonstrating improved in-vivo results when training on conventional EPT labels, but highlighting challenges with simulated data.

Contribution

It introduces a patch-based 3D U-net approach for brain conductivity mapping and compares training on simulated versus real EPT data, showing the importance of realistic training labels.

Findings

Training on simulated data with noise yields artifacts in in-vivo applications.

Using real EPT labels improves reconstruction quality and reduces artifacts.

Deep learning shows promise but faces challenges with in-vivo data artifacts.

Abstract

Purpose: To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets including pathologies for obtaining quantitative brain conductivity maps. Methods: 3D patch-based convolutional neural networks were trained to predict conductivity maps from B1 transceive phase data. To compare the performance of DLEPT networks on different datasets, three datasets were used throughout this work, one from simulations and two from in-vivo measurements from healthy volunteers and cancer patients, respectively. At first, networks trained on simulations are tested on all datasets with different levels of homogeneous Gaussian noise introduced in training and testing. Secondly, to investigate potential robustness towards systematical differences between simulated and measured phase maps, in-vivo data with conductivity labels from conventional EPT is used for training. Results: High quality of reconstructions from networks trained on simulations with and without noise confirms the potential of deep learning for EPT. However, artifact encumbered results in this work uncover challenges in application of DLEPT to in-vivo data. Training DLEPT networks on conductivity labels from conventional EPT improves quality of results. This is argued to be caused by robustness to artifacts from image acquisition. Conclusions: Networks trained on simulations with added homogeneous Gaussian noise yield reconstruction artifacts when applied to in-vivo data. Training with realistic phase data and conductivity labels from conventional EPT allows for severely reducing these artifacts.