Sajib Saurav Z K, Katoch Nitish, Kim Hyung Joong, Kwon Oh In, Woo Eung Je
Department of Biomedical EngineeringKyung Hee University.
Department of MathematicsKonkuk University.
IEEE Trans Biomed Eng. 2017 Nov;64(11):2505-2514. doi: 10.1109/TBME.2017.2732502.
Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI.
To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment.
The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox.
Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes.
Low-frequency conductivity and current density imaging using MRI includes magnetic resonance electrical impedance tomography (MREIT), diffusion tensor MREIT (DT-MREIT), conductivity tensor imaging (CTI), and magnetic resonance current density imaging (MRCDI). MRCDI and MREIT provide current density and isotropic conductivity images, respectively, using current-injection phase MRI techniques. DT-MREIT produces anisotropic conductivity tensor images by incorporating diffusion weighted MRI into MREIT. These current-injection techniques are finding clinical applications in diagnostic imaging and also in transcranial direct current stimulation (tDCS), deep brain stimulation (DBS), and electroporation where treatment currents can function as imaging currents. To avoid adverse effects of nerve and muscle stimulations due to injected currents, conductivity tensor imaging (CTI) utilizes B1 mapping and multi-b diffusion weighted MRI to produce low-frequency anisotropic conductivity tensor images without injecting current. This paper describes numerical implementations of several key mathematical functions for conductivity and current density image reconstructions in MRCDI, MREIT, DT-MREIT, and CTI.
To facilitate experimental studies of clinical applications, we developed a software toolbox for these low-frequency conductivity and current density imaging methods. This MR-based conductivity imaging (MRCI) toolbox includes 11 toolbox functions which can be used in the MATLAB environment.
The MRCI toolbox is available at http://iirc.khu.ac.kr/software.html . Its functions were tested by using several experimental datasets, which are provided together with the toolbox.
Users of the toolbox can focus on experimental designs and interpretations of reconstructed images instead of developing their own image reconstruction softwares. We expect more toolbox functions to be added from future research outcomes.
利用磁共振成像（MRI）进行的低频电导率和电流密度成像包括磁共振电阻抗断层成像（MREIT）、扩散张量MREIT（DT-MREIT）、电导率张量成像（CTI）以及磁共振电流密度成像（MRCDI）。MRCDI和MREIT分别使用电流注入相位MRI技术提供电流密度图像和各向同性电导率图像。DT-MREIT通过将扩散加权MRI纳入MREIT来生成各向异性电导率张量图像。这些电流注入技术正在诊断成像以及经颅直流电刺激（tDCS）、深部脑刺激（DBS）和电穿孔等领域找到临床应用，在这些应用中治疗电流可作为成像电流。为避免注入电流引起的神经和肌肉刺激的不良影响，电导率张量成像（CTI）利用B1映射和多b扩散加权MRI来生成不注入电流的低频各向异性电导率张量图像。本文描述了MRCDI、MREIT、DT-MREIT和CTI中电导率和电流密度图像重建的几个关键数学函数的数值实现。
为便于临床应用的实验研究，我们为这些低频电导率和电流密度成像方法开发了一个软件工具箱。这个基于MR的电导率成像（MRCI）工具箱包括11个可在MATLAB环境中使用的工具箱函数。
MRCI工具箱可在http://iirc.khu.ac.kr/software.html获取。其功能通过使用与工具箱一起提供的几个实验数据集进行了测试。
该工具箱的用户可以专注于实验设计和对重建图像的解释，而无需开发自己的图像重建软件。我们期望未来的研究成果能增加更多的工具箱功能。
利用磁共振成像（MRI）进行的低频电导率和电流密度成像包括磁共振电阻抗断层成像（MREIT）、扩散张量MREIT（DT-MREIT）、电导率张量成像（CTI）以及磁共振电流密度成像（MRCDI）。MRCDI和MREIT分别使用电流注入相位MRI技术提供电流密度图像和各向同性电导率图像。DT-MREIT通过将扩散加权MRI纳入MREIT来生成各向异性电导率张量图像。这些电流注入技术正在诊断成像以及经颅直流电刺激（tDCS）、深部脑刺激（DBS）和电穿孔等领域找到临床应用，在这些应用中治疗电流可作为成像电流。为避免注入电流引起的神经和肌肉刺激的不良影响，电导率张量成像（CTI）利用B1映射和多b扩散加权MRI来生成不注入电流的低频各向异性电导率张量图像。本文描述了MRCDI、MREIT、DT-MREIT和CTI中电导率和电流密度图像重建的几个关键数学函数的数值实现。
为便于临床应用的实验研究，我们为这些低频电导率和电流密度成像方法开发了一个软件工具箱。这个基于MR的电导率成像（MRCI）工具箱包括11个可在MATLAB环境中使用的工具箱函数。
MRCI工具箱可在http://iirc.khu.ac.kr/software.html获取。其功能通过使用与工具箱一起提供的几个实验数据集进行了测试。
该工具箱的用户可以专注于实验设计和对重建图像的解释，而无需开发自己的图像重建软件。我们期望未来的研究成果能增加更多的工具箱功能。
