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一种用于电阻抗断层成像/超声断层成像双模态图像重建的拉格朗日-牛顿方法。

A Lagrange-Newton Method for EIT/UT Dual-Modality Image Reconstruction.

作者信息

Liang Guanghui, Ren Shangjie, Zhao Shu, Dong Feng

机构信息

Tianjin Key Laboratory of Process Measurement and Control, School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China.

Institute of Biomedical Engineering, CAMS & PUMC (Chinese Academy of Medical Sciences and Peking Union Medical College), Tianjin 300192, China.

出版信息

Sensors (Basel). 2019 Apr 26;19(9):1966. doi: 10.3390/s19091966.

DOI:10.3390/s19091966
PMID:31035459
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6540236/
Abstract

An image reconstruction method is proposed based on Lagrange-Newton method for electrical impedance tomography (EIT) and ultrasound tomography (UT) dual-modality imaging. Since the change in conductivity distribution is usually accompanied with the change in acoustic impedance distribution, the reconstruction targets of EIT and UT are unified to the conductivity difference using the same mesh model. Some background medium distribution information obtained from ultrasound transmission and reflection measurements can be used to construct a hard constraint about the conductivity difference distribution. Then, the EIT/UT dual-modality inverse problem is constructed by an equality constraint equation, and the Lagrange multiplier method combining Newton-Raphson iteration is used to solve the EIT/UT dual-modality inverse problem. The numerical and experimental results show that the proposed dual-modality image reconstruction method has a better performance than the single-modality EIT method and is more robust to the measurement noise.

摘要

提出了一种基于拉格朗日-牛顿法的电阻抗断层成像（EIT）和超声断层成像（UT）双模态成像的图像重建方法。由于电导率分布的变化通常伴随着声阻抗分布的变化，利用相同的网格模型将EIT和UT的重建目标统一为电导率差。从超声透射和反射测量中获得的一些背景介质分布信息可用于构建关于电导率差分布的硬约束。然后，通过等式约束方程构建EIT/UT双模态反问题，并采用结合牛顿-拉夫森迭代的拉格朗日乘数法求解EIT/UT双模态反问题。数值和实验结果表明，所提出的双模态图像重建方法比单模态EIT方法具有更好的性能，并且对测量噪声更具鲁棒性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/f6ce64e476c9/sensors-19-01966-g013.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/fdd23fad9dfd/sensors-19-01966-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/df649af48dd0/sensors-19-01966-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/da6e8d3a5661/sensors-19-01966-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/dd3ae342134a/sensors-19-01966-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/62b304c42c3a/sensors-19-01966-g011.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9439/6540236/f6ce64e476c9/sensors-19-01966-g013.jpg

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