MREIT conductivity imaging of the postmortem canine abdomen using CoReHA

Abstract

Magnetic resonance electrical impedance tomography (MREIT) is a new bio-imaging modality providing cross-sectional conductivity images from measurements of internal magnetic flux densities produced by externally injected currents. Recent experimental results of postmortem and in vivo imaging of the canine brain demonstrated its feasibility by showing conductivity images with meaningful contrast among different brain tissues. MREIT image reconstructions involve a series of data processing steps such as k-space data handling, phase unwrapping, image segmentation, meshing, modelling, finite element computation, denoising and so on. To facilitate experimental studies, we need a software tool that automates these data processing steps. In this paper, we summarize such an MREIT software package called CoReHA (conductivity reconstructor using harmonic algorithms). Performing imaging experiments of the postmortem canine abdomen, we demonstrate how CoReHA can be utilized in MREIT. The abdomen with a relatively large field of view and various organs imposes new technical challenges when it is chosen as an imaging domain. Summarizing a few improvements in the experimental MREIT technique, we report our first conductivity images of the postmortem canine abdomen. Illustrating reconstructed conductivity images, we discuss how they discern different organs including the kidney, spleen, stomach and small intestine. We elaborate, as an example, that conductivity images of the kidney show clear contrast among cortex, internal medulla, renal pelvis and urethra. We end this paper with a brief discussion on future work using different animal models.

In vivo magnetic resonance electrical impedance tomography of canine brain: Disease model study of ischemia and abscess

“…experimental technique and now reached the stage of in vivo animal and human experiments. Recent animal MREIT studies showed a good conductivity contrast not only inside the brain but also in other body parts such as the abdomen, pelvis, knee and leg [8][9][10][11]. To support its clinical significance, we should demonstrate that the conductivity image provides meaningful diagnostic information that is not available from other imaging modalities.…”

“…We used CoReHA (conductivity reconstructor using harmonic algorithms), which is an integrated software package for MREIT [10,27,28]. It provides GUI-based functions for all data processing routines needed to produce conductivity images from measured k-space data sets.…”

“…It utilizes an MRI scanner to measure magnetic flux density data inside the object induced by externally injected currents at frequencies less than several kHz [1][2][3][4][5][6][7]. Recent MREIT studies of postmortem and in vivo animals and human subjects demonstrated that significant conductivity contrasts exist among different tissues and organs at such a low frequency [8][9][10][11].…”

J-based Magnetic Resonance Conductivity Tensor Imaging (MRCTI) at 3 T

“…In this study, J-based MRCTI algorithms are used to reconstruct anisotropic conductivity of an experimental phantom. Several in vivo experimental MREIT studies have been performed on animals in recent years [5,6,7]. In vivo MREIT images of a human leg have also been obtained by using Harmonic algorithm, which is a B-based isotropic MREIT reconstruction algorithm [8].…”

CoReHA 2.0: A Software Package forIn VivoMREIT Experiments

“…These specific computations cannot be handled through readily available finite element packages; thus, it is required to develop a user friendly software with graphics user interface (GUI) for those who wish to reconstruct conductivity distributions. Upon these requests, we developed a software package, called CoReHA [3, 4] which stands for conductivity reconstructor using harmonic algorithms, using VC++ MFC 6.0 (Microsoft Foundation Class Library 6.0) and OpenGL under the Microsoft Windows operating system. Based on the harmonic B z algorithm [5], CoReHA supports all procedures from the preprocessing of raw data to conductivity imaging through the intuitively apprehensible graphic user interface, more specifically, data conversion of raw k -space data, data verification, segmentation tools for numerical computation, solvers of forward/inverse problems using finite element methods, and 2D/3D data view as well as histogram.…”