To investigate deep learning electrical properties tomography (EPT) for application on different simulated and in-vivo datasets, including pathologies for brain conductivity reconstructions, 3D patch-based convolutional neural networks were trained to predict conductivity maps from B 1 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 patients with brain lesions, 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. High quality conductivity reconstructi...

中文翻译：

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研究深度学习脑电导率测绘的挑战和普遍性

为了研究深度学习电学断层成像（EPT）在不同的模拟和体内数据集上的应用，包括脑电导率重建的病理学，对基于3D补丁的卷积神经网络进行了训练，以根据B 1收发相位数据预测电导率图。为了比较DL-EPT网络在不同数据集上的性能，在整个工作中使用了三个数据集，一个来自模拟，两个来自健康志愿者和脑损伤患者的体内测量。首先，在训练和测试中引入了具有不同水平的均匀高斯噪声的所有数据集上，对经过模拟训练的网络进行了测试。其次，要研究针对模拟和实测相位图之间系统差异的潜在鲁棒性，具有常规EPT的电导率标签的体内数据用于训练。高质量电导率重建