Edinburgh mfEIT Dataset

This dataset is for publication "MMV-Net: A Multiple Measurement Vector Network for Multifrequency Electrical Impedance Tomography". ABSTRACT: Multifrequency electrical impedance tomography (mfEIT) is an emerging biomedical imaging modality to reveal frequency-dependent conductivity distributions in biomedical applications. Conventional model-based image reconstruction methods suffer from low spatial resolution, unconstrained frequency correlation, and high computational cost. Deep learning has been extensively applied in solving the EIT inverse problem in biomedical and industrial process imaging. However, most existing learning-based approaches deal with the single-frequency setup, which is inefficient and ineffective when extended to the multifrequency setup. This article presents a multiple measurement vector (MMV) model-based learning algorithm named MMV-Net to solve the mfEIT image reconstruction problem. MMV-Net considers the correlations between mfEIT images and unfolds the update steps of the Alternating Direction Method of Multipliers for the MMV problem (MMV-ADMM). The nonlinear shrinkage operator associated with the weighted l2,1 regularization term of MMV-ADMM is generalized in MMV-Net with a cascade of a Spatial Self-Attention module and a Convolutional Long Short-Term Memory (ConvLSTM) module to better capture intrafrequency and interfrequency dependencies. The proposed MMV-Net was validated on our Edinburgh mfEIT Dataset and a series of comprehensive experiments. The results show superior image quality, convergence performance, noise robustness, and computational efficiency against the conventional MMV-ADMM and the state-of-the-art deep learning methods.

本数据集配套发表论文《MMV-Net：面向多频电阻抗层析成像的多测量向量网络》。摘要：多频电阻抗层析成像（multifrequency electrical impedance tomography, mfEIT）是一种新兴的生物医学成像模态，可用于揭示生物医学应用中与频率相关的电导率分布。传统基于模型的图像重建方法存在空间分辨率低、频率相关性无约束、计算成本高昂等问题。深度学习已被广泛应用于解决生物医学与工业过程成像中的电阻抗层析成像逆问题，但现有基于学习的方法大多仅针对单频场景，当扩展至多频场景时效率与效果均欠佳。本文提出一种基于多测量向量（multiple measurement vector, MMV）模型的学习算法MMV-Net，用于求解mfEIT图像重建问题。MMV-Net充分考虑mfEIT图像间的相关性，并展开求解MMV问题的交替方向乘子法（alternating direction method of multipliers for the MMV problem, MMV-ADMM）的迭代更新步骤。针对MMV-ADMM中带加权l₂,₁正则项的非线性收缩算子，本文通过空间自注意力模块（Spatial Self-Attention module）与卷积长短期记忆（convolutional long short-term memory, ConvLSTM）模块的级联结构对其进行泛化，以更好地捕捉频内与频间依赖关系。本文所提出的MMV-Net在爱丁堡mfEIT数据集与一系列综合对照实验中得到验证，结果表明，相较于传统MMV-ADMM方法与当前主流深度学习方法，所提方法在图像质量、收敛性能、噪声鲁棒性与计算效率上均表现更优。

搜集汇总

背景与挑战

背景概述

该数据集是一个用于多频电阻抗断层成像（mfEIT）研究的公开数据集，发布于2022年，由爱丁堡大学的研究团队创建。它主要用于支持深度学习算法MMV-Net的验证，在图像质量、收敛性能和噪声鲁棒性方面提供评估基准，并包含完整的文件和相关许可证信息。