Introduction of Sample Based Prior into the D-Bar Method Through a Schur Complement Property.
Introduction of Sample Based Prior into the D-Bar Method Through a Schur Complement Property.
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DOI：
10.1109/tmi.2020.3012428
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发表时间：
2020-12
影响因子：
10.6
通讯作者：
Lima RG
中科院分区：
文献类型：
--
作者：
Santos TBR;Nakanishi RM;Kaipio JP;Mueller JL;Lima RG
电阻抗断层扫描（EIT）是一种非侵入性医学成像技术，通过测量由低频、低振幅施加电流产生的电极上的电压来计算身体感兴趣区域的电导率图像。在数学上，电导率反问题是非线性和病态的，并且重构具有低空间分辨率的特点。提高EIT图像空间分辨率的一种方法是在重构算法中加入基于解剖学和生理学的先验信息。统计反转理论提供了一种包括代表性样本人口的先验信息的方法。本文提出了一种基于Schur补性质在D-bar方法中引入统计先验信息的方法。该方法通过最大化与先验信息和模型相一致的图像的条件概率密度函数，改进了D-bar方法获得的图像，给出了从电压测量中计算的D-bar图像。实验结果表明，采用该方法重建的d条图像具有较高的空间分辨率。
Electrical impedance tomography (EIT) is a non-invasive medical imaging technique in which images of the conductivity in a region of interest in the body are computed from measurements of voltages on electrodes arising from low-frequency, low-amplitude applied currents. Mathematically, the inverse conductivity problem is nonlinear and ill-posed, and the reconstructions have characteristically low spatial resolution. One approach to improve the spatial resolution of EIT images is to include anatomically and physiologically-based prior information in the reconstruction algorithm. Statistical inversion theory provides a means of including prior information from a representative sample population. In this paper, a method is proposed to introduce statistical prior information into the D-bar method based on Schur complement properties. The method presents an improvement of the image obtained by the D-bar method by maximizing the conditional probability density function of an image that is consistent with a prior information and the model, given a D-bar image computed from the voltage measurements. Experimental phantoms show an improved spatial resolution by the use of the proposed method for the D-bar image reconstructions.