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首页> 外文期刊>Biomedical Engineering, IEEE Transactions on >Efficient Simultaneous Reconstruction of Time-Varying Images and Electrode Contact Impedances in Electrical Impedance Tomography
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Efficient Simultaneous Reconstruction of Time-Varying Images and Electrode Contact Impedances in Electrical Impedance Tomography

机译：电阻层析成像中时变图像和电极接触阻抗的高效同时重建

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Objective: In electrical impedance tomography (EIT), we apply patterns of currents on a set of electrodes at the external boundary of an object, measure the resulting potentials at the electrodes, and, given the aggregate dataset, reconstruct the complex conductivity and permittivity within the object. It is possible to maximize sensitivity to internal conductivity changes by simultaneously applying currents and measuring potentials on all electrodes but this approach also maximizes sensitivity to changes in impedance at the interface. Methods: We have, therefore, developed algorithms to assess contact impedance changes at the interface as well as to efficiently and simultaneously reconstruct internal conductivity/permittivity changes within the body. We use simple linear algebraic manipulations, the generalized singular value decomposition, and a dual-mesh finite-element-based framework to reconstruct images in real time. We are also able to efficiently compute the linearized reconstruction for a wide range of regularization parameters and to compute both the generalized cross-validation parameter as well as the L-curve, objective approaches to determining the optimal regularization parameter, in a similarly efficient manner. Results: Results are shown using data from a normal subject and from a clinical intensive care unit patient, both acquired with the GE GENESIS prototype EIT system, demonstrating significantly reduced boundary artifacts due to electrode drift and motion artifact.
机译：目的：在电阻抗断层扫描（EIT）中，我们将电流模式应用于对象外部边界处的一组电极上，测量电极上产生的电势，并在给定聚合数据集的情况下，重构其中的复数电导率和介电常数物体。通过在所有电极上同时施加电流和测量电势，可以最大程度地提高对内部电导率变化的敏感性，但是这种方法还可以最大程度地提高对界面阻抗变化的敏感性。方法：因此，我们开发了算法来评估界面处的接触阻抗变化，以及有效并同时重建体内的内部电导率/介电常数变化。我们使用简单的线性代数运算，广义奇异值分解以及基于双重网格有限元的框架来实时重建图像。我们还能够有效地计算各种正则化参数的线性化重构，并以类似有效的方式计算出通用的交叉验证参数以及L曲线，这是确定最佳正则化参数的客观方法。结果：结果显示使用来自正常受试者和临床重症监护病房患者的数据，这两个数据均通过GE GENESIS原型EIT系统获得，证明由于电极漂移和运动伪影而导致的边界伪影显着减少。

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