用于电阻抗断层成像图像重建的条件扩散模型
A Conditional Diffusion Model for Electrical Impedance Tomography Image Reconstruction
AI总结：
提出基于条件扩散模型的EIT图像重建方法CDEIT，通过前向加噪和反向去噪过程在边界电压条件下生成电导率图像，并提供归一化流程实现模拟到真实数据的迁移，实验证明其优于现有方法。
摘要：
电阻抗断层成像（EIT）是一种非侵入性成像技术，能够重建组织和材料的电导率图像。由于其低成本、实时能力和非电离特性，它在从医学成像到工业过程监测和触觉传感等各种应用领域中广受欢迎。EIT通过在给定电流注入的情况下测量边界电压，来可视化物体内部的电导率分布。然而，由于欠采样电压数据与高分辨率电导率图像之间的不匹配，EIT图像重建是一个不适定问题。目前已经提出了多种方法，包括传统方法和基于深度学习的方法，这些方法利用了空间正则化器和图像回归范式。在本研究中，提出了一种基于条件扩散模型的EIT重建新方法，称为CDEIT。具体而言，CDEIT包含前向扩散过程和反向去噪过程：前向扩散过程首先逐渐向干净电导率图像添加高斯噪声，反向去噪过程则学习在边界电压的条件下从含噪版本预测原始电导率图像。模型训练完成后，CDEIT对测试电压数据应用条件反向过程来生成所需的电导率。此外，我们提供了归一化过程的细节，展示了在模拟数据集上训练的EIT图像重建模型如何应用于具有不同尺寸、激励电流和背景电导率的真实数据集。在合成数据集和两个真实数据集上进行的实验表明，所提出的模型优于现有最先进的方法。CDEIT软件作为开源提供（https://github.com/shuaikaishi/CDEIT）以确保可重复性。
英文摘要：
Electrical impedance tomography (EIT) is a non-invasive imaging technique, capable of reconstructing images of the electrical conductivity of tissues and materials. It is popular in diverse application areas, from medical imaging to industrial process monitoring and tactile sensing, due to its low cost, real-time capabilities and non-ionizing nature. EIT visualizes the conductivity distribution within a body by measuring the boundary voltages, given a current injection. However, EIT image reconstruction is ill-posed due to the mismatch between the under-sampled voltage data and the high-resolution conductivity image. A variety of approaches, both conventional and deep learning-based, have been proposed, capitalizing on the use of spatial regularizers, and the paradigm of image regression. In this research, a novel method based on the conditional diffusion model for EIT reconstruction is proposed, termed CDEIT. Specifically, CDEIT consists of the forward diffusion process, which first gradually adds Gaussian noise to the clean conductivity images, and a reverse denoising process, which learns to predict the original conductivity image from its noisy version, conditioned on the boundary voltages. Following model training, CDEIT applies the conditional reverse process on test voltage data to generate the desired conductivities. Moreover, we provide the details of a normalization procedure, which demonstrates how EIT image reconstruction models trained on simulated datasets can be applied on real datasets with varying sizes, excitation currents and background conductivities. Experiments conducted on a synthetic dataset and two real datasets demonstrate that the proposed model outperforms state-of-the-art methods. The CDEIT software is available as open-source (https://github.com/shuaikaishi/CDEIT) for reproducibility purposes.
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