MREIT conductivity imaging of the postmortem canine abdomen using CoReHA

Volumetric measurements of weak current-induced magnetic fields in the human brain at high resolution

PURPOSE

Clinical use of transcranial electrical stimulation (TES) requires accurate knowledge of the injected current distribution in the brain. MR current density imaging (MRCDI) uses measurements of the TES-induced magnetic fields to provide this information. However, sufficient sensitivity and image quality in humans in vivo has only been documented for single-slice imaging.

METHODS

A recently developed, optimally spoiled, acquisition-weighted, gradient echo-based 2D-MRCDI method has now been advanced for volume coverage with densely or sparsely distributed slices: The 3D rectilinear sampling (3D-DENSE) and simultaneous multislice acquisition (SMS-SPARSE) were optimized and verified by cable-loop experiments and tested with 1-mA TES experiments for two common electrode montages.

RESULTS

Comparisons between the volumetric methods against the 2D-MRCDI showed that relatively long acquisition times of 3D-DENSE using a single slab with six slices hindered the expected sensitivity improvement in the current-induced field measurements but improved sensitivity by 61% in the Laplacian of the field, on which some MRCDI reconstruction methods rely. Also, SMS-SPARSE acquisition of three slices, with a factor 2 CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) acceleration, performed best against the 2D-MRCDI with sensitivity improvements for the∆ B z , c $$ \Delta {B}_{z,c} $$ and Laplacian noise floors of 56% and 78% (baseline without current flow) as well as 43% and 55% (current injection into head). SMS-SPARSE reached a sensitivity of 67 pT for three distant slices at 2 × 2 × 3 mm3 resolution in 10 min of total scan time, and consistently improved image quality.

CONCLUSION

Volumetric MRCDI measurements with high sensitivity and image quality are well suited to characterize the TES field distribution in the human brain.

Low-frequency conductivity tensor imaging with a single current injection using DT-MREIT

Diffusion tensor-magnetic resonance electrical impedance tomography (DT-MREIT) is an imaging modality to obtain low-frequency anisotropic conductivity distribution employing diffusion tensor imaging and MREIT techniques. DT-MREIT is based on the linear relationship between the conductivity and water self-diffusion tensors in a porous medium, like the brain white matter. Several DT-MREIT studies in the literature provide cross-sectional anisotropic conductivity images of tissue phantoms, canine brain, and the human brain. In these studies, the conductivity tensor images are reconstructed using the diffusion tensor and current density data acquired by injecting two linearly independent current patterns. In this study, a novel reconstruction algorithm is devised for DT-MREIT to reconstruct the conductivity tensor images using a single current injection. Therefore, the clinical applicability of DT-MREIT can be improved by reducing the total acquisition time, the number of current injection cables, and contact electrodes to half by decreasing the number of current injection patterns to one. The proposed method is evaluated utilizing simulated measurements and physical experiments. The results obtained show the successful reconstruction of the anisotropic conductivity distribution using the proposed single current DT-MREIT.

Low-frequency dominant electrical conductivity imaging of in vivo human brain using high-frequency conductivity at Larmor-frequency and spherical mean diffusivity without external injection current

Diffusion weighted imaging based on random Brownian motion of water molecules within a voxel provides information on the micro-structure of biological tissues through water molecule diffusivity. As the electrical conductivity is primarily determined by the concentration and mobility of ionic charge carriers, the macroscopic electrical conductivity of biological tissues is also related to the diffusion of electrical ions. This paper aims to investigate the low-frequency electrical conductivity by relying on a pre-defined biological model that separates the brain into the intracellular (restricted) and extracellular (hindered) compartments. The proposed method uses B1 mapping technique, which provides a high-frequency conductivity distribution at Larmor frequency, and the spherical mean technique, which directly estimates the microscopic tissue structure based on the water molecule diffusivity and neurite orientation distribution. The total extracellular ion concentration, which is separated from the high-frequency conductivity, is recovered using the estimated diffusivity parameters and volume fraction in each compartment. We propose a method to reconstruct the low-frequency dominant conductivity tensor by taking into consideration the extracted extracellular diffusion tensor map and the reconstructed electrical parameters. To demonstrate the reliability of the proposed method, we conducted two phantom experiments. The first one used a cylindrical acrylic cage filled with an agar in the background region and four anomalies for the effect of ion concentration on the electrical conductivity. The other experiment, in which the effect of ion mobility on the conductivity was verified, used cell-like materials with thin insulating membranes suspended in an electrolyte. Animal and human brain experiments were conducted to visualize the low-frequency dominant conductivity tensor images. The proposed method using a conventional MRI scanner can predict the internal current density map in the brain without directly injected external currents.

Evaluation of magnetohydrodynamic effects in magnetic resonance electrical impedance tomography at ultra-high magnetic fields

PURPOSE

Artifacts observed in experimental magnetic resonance electrical impedance tomography images were hypothesized to be because of magnetohydrodynamic (MHD) effects.

THEORY AND METHODS

Simulations of MREIT acquisition in the presence of MHD and electrical current flow were performed to confirm findings. Laminar flow and (electrostatic) electrical conduction equations were bidirectionally coupled via Lorentz force equations, and finite element simulations were performed to predict flow velocity as a function of time. Gradient sequences used in spin-echo and gradient echo acquisitions were used to calculate overall effects on MR phase images for different electrical current application or phase-encoding directions.

RESULTS

Calculated and experimental phase images agreed relatively well, both qualitatively and quantitatively, with some exceptions. Refocusing pulses in spin echo sequences did not appear to affect experimental phase images.

CONCLUSION

MHD effects were confirmed as the cause of observed experimental phase changes in MREIT images obtained at high fields. These findings may have implications for quantitative measurement of viscosity using MRI techniques. Methods developed here may be also important in studies of safety and in vivo artifacts observed in high field MRI systems.

Software Toolbox for Low-Frequency Conductivity and Current Density Imaging Using MRI

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

OBJECTIVE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

Human in-vivo brain magnetic resonance current density imaging (MRCDI)

Magnetic resonance current density imaging (MRCDI) and MR electrical impedance tomography (MREIT) are two emerging modalities, which combine weak time-varying currents injected via surface electrodes with magnetic resonance imaging (MRI) to acquire information about the current flow and ohmic conductivity distribution at high spatial resolution. The injected current flow creates a magnetic field in the head, and the component of the induced magnetic field ΔB_z,c parallel to the main scanner field causes small shifts in the precession frequency of the magnetization. The measured MRI signal is modulated by these shifts, allowing to determine ΔB_z,c for the reconstruction of the current flow and ohmic conductivity. Here, we demonstrate reliable ΔB_z,c measurements in-vivo in the human brain based on multi-echo spin echo (MESE) and steady-state free precession free induction decay (SSFP-FID) sequences. In a series of experiments, we optimize their robustness for in-vivo measurements while maintaining a good sensitivity to the current-induced fields. We validate both methods by assessing the linearity of the measured ΔB_z,c with respect to the current strength. For the more efficient SSFP-FID measurements, we demonstrate a strong influence of magnetic stray fields on the ΔB_z,c images, caused by non-ideal paths of the electrode cables, and validate a correction method. Finally, we perform measurements with two different current injection profiles in five subjects. We demonstrate reliable recordings of ΔB_z,c fields as weak as 1 nT, caused by currents of 1 mA strength. Comparison of the ΔB_z,c measurements with simulated ΔB_z,c images based on FEM calculations and individualized head models reveals significant linear correlations in all subjects, but only for the stray field-corrected data. As final step, we reconstruct current density distributions from the measured and simulated ΔB_z,c data. Reconstructions from non-corrected ΔB_z,c measurements systematically overestimate the current densities. Comparing the current densities reconstructed from corrected ΔB_z,c measurements and from simulated ΔB_z,c images reveals an average coefficient of determination R² of 71%. In addition, it shows that the simulations underestimated the current strength on average by 24%. Our results open up the possibility of using MRI to systematically validate and optimize numerical field simulations that play an important role in several neuroscience applications, such as transcranial brain stimulation, and electro- and magnetoencephalography.

Non-invasive evaluation of the kinematic activity of the intact left ventricle of the heart

Vector analysis of the movement of the epicardium has been used to calculate the energy efficiency of different parts of the left cardiac ventricle. The protocol based on the results of these calculations would allow the calculation of the potential power of myocardial contraction.

Current Density Imaging During Transcranial Direct Current Stimulation Using DT-MRI and MREIT: Algorithm Development and Numerical Simulations

OBJECTIVE

Transcranial direct current stimulation (tDCS) is a neuromodulatory technique for neuropsychiatric diseases and neurological disorders. In the tDCS treatment, dc current is injected into the head through a pair of electrodes attached on the scalp over a target region. A current density imaging method is needed to quantitatively visualize the internal current density distribution during the tDCS treatment.

METHODS

We developed a novel current density image reconstruction algorithm using 1) a subject specific segmented 3-D head model, 2) diffusion tensor data, and 3) magnetic flux density data induced by the tDCS current. We acquired T1 weighted and diffusion tensor images of the head using the MRI scanner before the treatment. During the treatment, we can measure the induced magnetic flux density data using a magnetic resonance electrical impedance tomography (MREIT) pulse sequence. In this paper, the magnetic flux density data were numerically generated.

RESULTS

Numerical simulation results show that the proposed method successfully recovers the current density distribution including the effects of the anisotropic, as well as isotropic conductivity values of different tissues in the head.

CONCLUSION

The proposed current density imaging method using DT-MRI and MREIT can reliably recover cross-sectional images of the current density distribution during the tDCS treatment.

SIGNIFICANCE

Success of the tDCS treatment depends on a precise determination of the induced current density distribution within different anatomical structures of the brain. Quantitative visualization of the current density distribution in the brain will play an important role in understanding the effects of the electrical stimulation.

Conductivity image enhancement in MREIT using adaptively weighted spatial averaging filter

Background

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.

Methods

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.

Results

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.

Conclusion

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.

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

In this study, current density (J) - based Magnetic Resonance Conductivity Tensor Imaging (MRCTI) reconstruction algorithms namely, the Anisotropic Equipotential Projection (AEPP), the Anisotropic J-Substitution (AJS) and the Anisotropic Hybrid J-Substitution (AHJS) algorithms are implemented to reconstruct conductivity tensor images of a physical phantom using a 3T magnetic resonance imaging system. 10mA current pulses are injected in synchrony with a conventional spin-echo pulse sequence. Furthermore, a new J-based hybrid algorithm namely, the Anisotropic Hybrid Equipotential Projection (AHEPP) is proposed. In addition, reconstruction performances of the four algorithms are evaluated.

Simulations and phantom evaluations of magnetic resonance electrical impedance tomography (MREIT) for breast cancer detection

MREIT is a new imaging modality that can be used to reconstruct high-resolution conductivity images of the human body. Since conductivity values of cancerous tissues in the breast are significantly higher than those of surrounding normal tissues, breast imaging using MREIT may provide a new noninvasive way of detecting early stage of cancer. In this paper, we present results of experimental and numerical simulation studies of breast MREIT. We built a realistic three-dimensional model of the human breast connected to a simplified model of the chest including the heart and evaluated the ability of MREIT to detect cancerous anomalies in a background material with similar electrical properties to breast tissue. We performed numerical simulations of various scenarios in breast MREIT including assessment of the effects of fat inclusions and effects related to noise levels, such as changing the amplitude of injected currents, effect of added noise and number of averages. Phantom results showed straightforward detection of cancerous anomalies in a background was possible with low currents and few averages. The simulation results showed it should be possible to detect a cancerous anomaly in the breast, while restricting the maximal current density in the heart below published levels for nerve excitation.

Numerical simulations of MREIT conductivity imaging for brain tumor detection

Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recent in vivo animal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performing in vivo brain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull's low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged.

CoReHA 2.0: a software package for in vivo MREIT experiments

Magnetic resonance electrical impedance tomography (MREIT) is a new medical imaging modality visualizing static conductivity images of electrically conducting subjects. Recently, MREIT has rapidly progressed in its theory, algorithm, and experiment technique and now reached to the stage of in vivo animal experiments. In this paper, we present a software, named CoReHA 2.0 standing for the second version of conductivity reconstructor using harmonic algorithms, to facilitate in vivo MREIT reconstruction of conductivity image. This software offers various computational tools including preprocessing of MREIT data, identification of 2D geometry of the imaging domain and electrode positions, and reconstruction of cross-sectional scaled conductivity images from MREIT data. In particular, in the new version, we added several tools including ramp-preserving denoising, harmonic inpainting, and local harmonic B z algorithm to deal with data from in vivo experiments. The presented software will be useful to researchers in the field of MREIT for simulation, validation, and further technical development.

Simultaneous imaging of dual-frequency electrical conductivity using a combination of MREIT and MREPT

PURPOSE

To propose a single magnetic resonance scan conductivity imaging technique providing dual-frequency characteristics of tissue conductivity.

METHODS

Using a modified spin-echo pulse sequence, the magnetic flux density induced by externally injected currents and the B1+ phase map with injected current effects removed were acquired simultaneously. The low-frequency conductivity was reconstructed from the measured magnetic flux density by the projected current density method, while the high-frequency conductivity was reconstructed using the B1+ maps. Three different conductivity phantoms were used to demonstrate low- and high-frequency conductivity characteristics.

RESULTS

A conductivity spectrum at two frequencies was successfully acquired with the proposed scheme. Magnetic resonance electrical impedance tomography is advantageous for seeing an anomaly itself wrapped with a thin insulating membrane. In addition, if the membrane is porous, the membrane property can be quantitatively visualized with magnetic resonance electrical impedance tomography. Magnetic resonance electrical properties tomography does not detect such membranes, which enable it to probe things inside an insulating membrane.

CONCLUSION

Considering these pros and cons and also the fact that the conductivity of biological tissue changes with frequency, a dual-frequency conductivity imaging incorporating both magnetic resonance electrical impedance tomography and magnetic resonance electrical properties tomography in future animal and human experiments is suggested.

MREIT experiments with 200 µA injected currents: a feasibility study using two reconstruction algorithms, SMM and harmonic B(Z)

Magnetic resonance electrical impedance tomography (MREIT) is a technique that produces images of conductivity in tissues and phantoms. In this technique, electrical currents are applied to an object and the resulting magnetic flux density is measured using magnetic resonance imaging (MRI) and the conductivity distribution is reconstructed using these MRI data. Currently, the technique is used in research environments, primarily studying phantoms and animals. In order to translate MREIT to clinical applications, strict safety standards need to be established, especially for safe current limits. However, there are currently no standards for safe current limits specific to MREIT. Until such standards are established, human MREIT applications need to conform to existing electrical safety standards in medical instrumentation, such as IEC601. This protocol limits patient auxiliary currents to 100 µA for low frequencies. However, published MREIT studies have utilized currents 10-400 times larger than this limit, bringing into question whether the clinical applications of MREIT are attainable under current standards. In this study, we investigated the feasibility of MREIT to accurately reconstruct the relative conductivity of a simple agarose phantom using 200 µA total injected current and tested the performance of two MREIT reconstruction algorithms. These reconstruction algorithms used are the iterative sensitivity matrix method (SMM) by Ider and Birgul (1998 Elektrik 6 215-25) with Tikhonov regularization and the harmonic B(Z) proposed by Oh et al (2003 Magn. Reason. Med. 50 875-8). The reconstruction techniques were tested at both 200 µA and 5 mA injected currents to investigate their noise sensitivity at low and high current conditions. It should be noted that 200 µA total injected current into a cylindrical phantom generates only 14.7 µA current in imaging slice. Similarly, 5 mA total injected current results in 367 µA in imaging slice. Total acquisition time for 200 µA and 5 mA experiments was about 1 h and 8.5 min, respectively. The results demonstrate that conductivity imaging is possible at low currents using the suggested imaging parameters and reconstructing the images using iterative SMM with Tikhonov regularization, which appears to be more tolerant to noisy data than harmonic B(Z).

Experimental performance evaluation of multi-echo ICNE pulse sequence in magnetic resonance electrical impedance tomography

Latest experimental results in magnetic resonance electrical impedance tomography (MREIT) demonstrated high-resolution in vivo conductivity imaging of animal and human subjects using imaging currents of 5 to 9 mA. Externally injected imaging currents induce magnetic flux density distributions, which are affected by a conductivity distribution. Since we extract the induced magnetic flux density images from MR phase images, it is essential to reduce noise in the phase images. In vivo human and disease model animal experiments require reduction of imaging current amplitudes and scan times. In this article, we investigate a multi-echo based MREIT pulse sequence where we utilize a remaining time after the first echo within one TR to obtain more echo signals. It also allows us to prolong the total current injection time. From phantom and animal imaging experiments, we found that this method significantly reduces the noise level in measured magnetic flux density images. We describe experimental validation of the multi-echo sequence by comparing its performance with a single-echo method using 3 mA imaging currents. The proposed method will be advantageous for an imaging region with long T2 values such as the brain and knee. Depending on T2 values, we suggest using two or three echoes in future experimental studies.

Ion mobility imaging and contrast mechanism of apparent conductivity in MREIT

Magnetic resonance electrical impedance tomography (MREIT) aims to produce high-resolution cross-sectional images of conductivity distribution inside the human body. Injected current into an imaging object induces a distribution of internal magnetic flux density, which is measured by using an MRI scanner. We can reconstruct a conductivity image based on its relation with the measured magnetic flux density. In this paper, we explain the contrast mechanism in MREIT by performing and analyzing a series of numerical simulations and imaging experiments. We built a stable conductivity phantom including a hollow insulating cylinder with holes. Filling both inside and outside the hollow cylinder with the same saline, we controlled ion mobilities to create a conductivity contrast without being affected by the ion diffusion process. From numerical simulations and imaging experiments, we found that slopes of induced magnetic flux densities change with hole diameters and therefore conductivity contrasts. Associating the hole diameter with apparent conductivity of the region inside the hollow cylinder with holes, we could experimentally validate the contrast mechanism in MREIT. Interpreting reconstructed apparent conductivity images of the phantom as ion mobility images, we discuss the meaning of the apparent conductivity seen by a certain probing method. In designing MREIT imaging experiments, the ion mobility imaging method using the proposed stable conductivity phantom will enable us to estimate a distinguishable conductivity contrast for a given set of imaging parameters.

High-resolution MREIT using low imaging currents

Magnetic Resonance Electrical Impedance Tomography (MREIT) produces cross-sectional images of a conductivity distribution inside the human body. We use an MRI scanner as a tool to measure induced internal magnetic flux density distributions subject to externally injected currents. Recent experimental MREIT studies demonstrated conductivity image reconstructions of in vivo animal and human subjects with a few millimeter pixel size using 3 mA current injections. To enhance the clinical applicability of MREIT especially in neuroimaging applications, it is necessary to develop high-resolution MREIT techniques using low imaging currents. In this study, we demonstrate the capability of MREIT to perform conductivity imaging with less than 1 mA injection currents. The experimental results using a 3 T MRI scanner with a multi-echo ICNE pulse sequence and high-performance RF coils demonstrate that we can distinguish two different anomalies in reconstructed conductivity images with less than 1 mm pixel sizes. We plan to apply the developed experimental method to in vivo head imaging of small animals to investigate the feasibility of functional MREIT as a new neuro-imaging method.

Experimental verification of contrast mechanism in Magnetic Resonance Electrical Impedance Tomography (MREIT)

Magnetic Resonance Electrical Impedance Tomography (MREIT) aims to produce cross-sectional images of a conductivity distribution inside the human body with a spatial resolution of a few millimeters. Injecting currents into an imaging object at different directions, we measure induced internal magnetic flux densities using an MRI scanner. Conductivity images are reconstructed based on the relation between the induced magnetic flux density and conductivity. Though there have been theoretical and experimental MREIT studies to explain and validate its imaging method, understanding the contrast mechanism in MREIT could be difficult due to the complexity in associated mathematical expressions. In this paper, we explain the contrast mechanism by performing and analyzing a series of imaging experiments of stable conductivity phantoms. Placing a thin and hollow cylinder with holes around its side inside a saline tank, we could construct a conductivity phantom with a stable conductivity contrast between two regions inside and outside the cylinder. Images of induced magnetic flux densities show ramp structures of which slopes are determined by conductivity contrasts. From the experimental results, we summarize the contrast mechanism in MREIT for better designs of MREIT pulse sequences and data processing methods.

Integration of the denoising, inpainting and local harmonic B(z) algorithm for MREIT imaging of intact animals

Conductivity imaging based on the current-injection MRI technique has been developed in magnetic resonance electrical impedance tomography. Current injected through a pair of surface electrodes induces a magnetic flux density distribution inside an imaging object, which results in additional magnetic field inhomogeneity. We can extract phase changes related to the current injection and obtain an image of the induced magnetic flux density. Without rotating the object inside the bore, we can measure only one component B(z) of the magnetic flux density B = (B(x), B(y), B(z)). Based on a relation between the internal conductivity distribution and B(z) data subject to multiple current injections, one may reconstruct cross-sectional conductivity images. As the image reconstruction algorithm, we have been using the harmonic B(z) algorithm in numerous experimental studies. Performing conductivity imaging of intact animal and human subjects, we found technical difficulties that originated from the MR signal void phenomena in the local regions of bones, lungs and gas-filled tubular organs. Measured B(z) data inside such a problematic region contain an excessive amount of noise that deteriorates the conductivity image quality. In order to alleviate this technical problem, we applied hybrid methods incorporating ramp-preserving denoising, harmonic inpainting with isotropic diffusion and ROI imaging using the local harmonic B(z) algorithm. These methods allow us to produce conductivity images of intact animals with best achievable quality. We suggest guidelines to choose a hybrid method depending on the overall noise level and existence of distinct problematic regions of MR signal void.