Investigating the challenges and generalizability of deep learning brain conductivity mapping

Abstract

Purpose: To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets including pathologies for brain conductivity reconstructions. Methods: 3D patch-based convolutional neural networks were trained to predict conductivity maps from B1 transceive phase data. To compare the performance of DL-EPT 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 were 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 were used for training. Results: High quality conductivity reconstructions from networks trained on simulations with and without noise confirms the potential of deep learning for EPT. However, when this network is used for in-vivo reconstructions, measurement related artifacts affect the quality of conductivity maps. Training DL-EPT networks using conductivity labels from conventional EPT improves the quality of the results. Conclusions: Networks trained on realistic simulations yield reconstruction artifacts when applied to in-vivo data. Training with realistic phase data and conductivity labels from conventional EPT allows for reducing these artifacts.