Engineering Tomography Laboratory (ETL), Department of Electronic and Electrical Engineering, University of Bath, Bath, UK.
Physiol Meas. 2013 Jun;34(6):645-58. doi: 10.1088/0967-3334/34/6/645. Epub 2013 May 29.
Electrical impedance tomography (EIT) is a fast and cost-effective technique to provide a tomographic conductivity image of a subject from boundary current-voltage data. This paper proposes a time and memory efficient method for solving a large scale 3D EIT inverse problem using a parallel conjugate gradient (CG) algorithm. The 3D EIT system with a large number of measurement data can produce a large size of Jacobian matrix; this could cause difficulties in computer storage and the inversion process. One of challenges in 3D EIT is to decrease the reconstruction time and memory usage, at the same time retaining the image quality. Firstly, a sparse matrix reduction technique is proposed using thresholding to set very small values of the Jacobian matrix to zero. By adjusting the Jacobian matrix into a sparse format, the element with zeros would be eliminated, which results in a saving of memory requirement. Secondly, a block-wise CG method for parallel reconstruction has been developed. The proposed method has been tested using simulated data as well as experimental test samples. Sparse Jacobian with a block-wise CG enables the large scale EIT problem to be solved efficiently. Image quality measures are presented to quantify the effect of sparse matrix reduction in reconstruction results.
电阻抗断层成像（EIT）是一种快速且具有成本效益的技术，可通过边界电流-电压数据提供对象的层析导电性图像。本文提出了一种使用并行共轭梯度（CG）算法解决大规模 3D EIT 逆问题的省时、省内存的方法。具有大量测量数据的 3D EIT 系统会产生大型 Jacobian 矩阵；这可能会导致计算机存储和反演过程中的困难。3D EIT 的挑战之一是减少重建时间和内存使用，同时保持图像质量。首先，提出了一种稀疏矩阵降阶技术，使用阈值将 Jacobian 矩阵中的非常小的值设置为零。通过将 Jacobian 矩阵调整为稀疏格式，可以消除零元素，从而节省内存需求。其次，开发了一种用于并行重建的分块 CG 方法。该方法已通过模拟数据和实验测试样本进行了测试。稀疏 Jacobian 与分块 CG 相结合，可有效地解决大规模 EIT 问题。提出了图像质量度量标准来量化稀疏矩阵降阶在重建结果中的影响。
