Oh Tong In, Kim Hyung Joong, Jeong Woo Chul, Wi Hun, Kwon Oh In, Woo Eung Je
Department of Mathematics, Konkuk University, 143-701 Seoul, Korea.
Biomed Eng Online. 2014 Jun 26;13:87. doi: 10.1186/1475-925X-13-87.
In magnetic resonance electrical impedance tomography (MREIT), we reconstruct conductivity images using magnetic flux density data induced by externally injected currents. Since we extract magnetic flux density data from acquired MR phase images, the amount of measurement noise increases in regions of weak MR signals. Especially for local regions of MR signal void, there may occur excessive amounts of noise to deteriorate the quality of reconstructed conductivity images. In this paper, we propose a new conductivity image enhancement method as a postprocessing technique to improve the image quality.
Within a magnetic flux density image, the amount of noise varies depending on the position-dependent MR signal intensity. Using the MR magnitude image which is always available in MREIT, we estimate noise levels of measured magnetic flux density data in local regions. Based on the noise estimates, we adjust the window size and weights of a spatial averaging filter, which is applied to reconstructed conductivity images. Without relying on a partial differential equation, the new method is fast and can be easily implemented.
Applying the novel conductivity image enhancement method to experimental data, we could improve the image quality to better distinguish local regions with different conductivity contrasts. From phantom experiments, the estimated conductivity values had 80% less variations inside regions of homogeneous objects. Reconstructed conductivity images from upper and lower abdominal regions of animals showed much less artifacts in local regions of weak MR signals.
We developed the fast and simple method to enhance the conductivity image quality by adaptively adjusting the weights and window size of the spatial averaging filter using MR magnitude images. Since the new method is implemented as a postprocessing step, we suggest adopting it without or with other preprocessing methods for application studies where conductivity contrast is of primary concern.
在磁共振电阻抗断层成像（MREIT）中，我们利用外部注入电流感应产生的磁通密度数据来重建电导率图像。由于我们从采集的磁共振相位图像中提取磁通密度数据，在磁共振信号较弱的区域测量噪声量会增加。特别是对于磁共振信号缺失的局部区域，可能会出现过多噪声，从而降低重建电导率图像的质量。在本文中，我们提出一种新的电导率图像增强方法作为后处理技术来提高图像质量。
在磁通密度图像中，噪声量根据与位置相关的磁共振信号强度而变化。利用MREIT中始终可用的磁共振幅度图像，我们估计局部区域中测量的磁通密度数据的噪声水平。基于噪声估计，我们调整应用于重建电导率图像的空间平均滤波器的窗口大小和权重。该新方法不依赖偏微分方程，速度快且易于实现。
将这种新颖的电导率图像增强方法应用于实验数据，我们能够提高图像质量，以便更好地区分具有不同电导率对比度的局部区域。在体模实验中，均匀物体区域内估计的电导率值变化减少了80%。从动物上腹部和下腹部区域重建的电导率图像在磁共振信号较弱的局部区域显示出更少的伪影。
我们开发了一种快速简单的方法，通过使用磁共振幅度图像自适应调整空间平均滤波器的权重和窗口大小来提高电导率图像质量。由于新方法是作为后处理步骤实现的，我们建议在主要关注电导率对比度的应用研究中，无论有无其他预处理方法都采用该方法。
