Magnetic Resonance Current Density Imaging Research Articles

MR imaging of the magnetic fields induced by injected currents can guide improvements of individualized head volume conductor models

Abstract Volume conductor models of the human head are routinely used to estimate the induced electric fields in transcranial brain stimulation (TBS) and for source localization in electro- and magnetoencephalography (EEG and MEG). Magnetic resonance current density imaging (MRCDI) has the potential to act as a non-invasive method for dose control and model validation but requires very sensitive MRI acquisition approaches. A double-echo echo-planar imaging (EPI) method is here introduced. It combines fast and sensitive imaging of the magnetic fields generated by the current flow of transcranial electric stimulation with increased robustness to physiological noise. For validation, noise floor measurements without injected currents were obtained in five subjects for an established multi-echo gradient-echo (MGRE) sequence and the new EPI method. In addition, data with current injection were acquired in each subject with a right-left (RL) and anterior-posterior (AP) electrode montage with both sequences to assess the accuracy of subject-specific detailed head models. In line with previous findings, the noise floor measurements showed that the MGRE results suffered from spatial low-frequency noise patterns, which were mostly absent in the EPI data. A recently published approach optimizes the ohmic conductivities of subject-specific head models by minimizing the difference between simulated and measured current-induced magnetic fields. Here, simulations demonstrated that the MGRE noise patterns have a larger negative impact on the optimization results than the EPI noise. For the current injection measurements, a larger discrepancy was found for the RL electrode montage compared with the AP electrode montage consistently for all subjects. This discrepancy that remained in part also after optimization of the ohmic conductivities, was similar for the data of the two sequences and larger than the measurement noise, and thus demonstrates systematic biases in the volume conductor models. We have shown that EPI-based MRCDI is superior to established techniques by mitigating the effects of previously reported spatial low-frequency noise in MRCDI if limited spatial resolution is acceptable. Additionally, the consistent inter-subject results indicate that MRCDI is capable of picking up inaccuracies in computational head models and will be useful to guide systematic improvements.

A Fast Magnetic Flux Density Measurement Method With Skip-Echo Acquired Turbo Spin Echo (SATE).

The measurements of magnetic flux density ( Bz) needed in magnetic resonance electrical impedance tomography (MREIT) and magnetic resonance current density imaging (MRCDI) techniques often utilize spin echo (SE)-based sequences for data acquisition. The low imaging speed of SE-based methods significantly hampers the clinical applications of MREIT and MRCDI. Here, we propose a new sequence for substantially accelerating the acquisition of Bz measurements. A skip-echo acquired turbo spin echo (SATE) imaging sequence based on the conventional turbo spin echo (TSE) technique was proposed by adding a skip-echo module in front of the TSE acquisition module. The skip-echo module consisted of a series of refocusing pulses without acquisition. In SATE, amplitude-modulated crusher gradients were used to remove the stimulated echo pathways, and the radiofrequency (RF) pulse shape was specially selected to preserve more signals. In efficiency evaluation experiments performed on a spherical gel phantom, we demonstrated that SATE had improved measurement efficiency compared to the conventional TSE sequence via skipping one echo before acquiring signals. The accuracy of the Bz measurements by SATE was validated against those by the multi-echo injection current nonlinear encoding (ME-ICNE) method, while SATE was able to accelerate the data acquisition up to 10-fold. Volumetric coverage of Bz maps obtained in the phantom, pork, and human calf illustrated that SATE can reliably measure volumetric Bz distributions within clinically acceptable time. The proposed SATE sequence provides a fast and effective approach for volumetric coverage of Bz measurements, greatly facilitating the clinical applications of MREIT and MRCDI techniques.

Magnetic Resonance Current Density Imaging (MR-CDI).

Current density imaging (CDI) was developed with the aim of determining the three-dimensional distribution of externally applied electric current pathways inside a conductive medium, using measurements of magnetic flux density [Formula: see text] data. While the B field may be measurable using instruments such as a magnetometer, in magnetic resonance current density imaging (MR-CDI), an MRI scanner is used to measure the magnetic flux density data induced by current flow. In MR-CDI, the object must be rotated inside the MRI machine to find all three components of the B-field, as only the component of B parallel to the magnet main magnetic field can be measured. In principle, once the all three components of the B field have been obtained from an MR imaging experiment, the current density distribution [Formula: see text] can be reconstructed from Ampere's law [Formula: see text]. However, the need to rotate the object within the MRI scanner limits the usability of this technique. To overcome this problem, researchers have investigated the current density reconstruction problem using only one component of the magnetic flux density Bq, where q = x, y, z. In this chapter, we discuss numerical algorithms developed to reconstruct the distribution of J information from the measured B-field.

Human in-vivo magnetic resonance current density imaging of the brain by optimizing head tissue conductivities

Abstract Introduction: Magnetic resonance current density imaging (MRCDI) aims to reconstruct the current flow of transcranial electrical stimulation (TES) in the brain from MR-measurements of the current-induced magnetic field Bz. Aim: We test the performance of a standard reconstruction algorithm (“projected current density algorithm”, PCD, Jeong et al. 2014) for human brain data. We compare it with current flow simulations using personalized head models. Methods:1. We generated ground-truth data for the TES current flow and Bz-field using a detailed head model and SimNIBS (www.simnibs.org). We applied the PCD algorithm to the Bz -field and quantified the reconstruction performance by comparison with the ground-truth current flow. We additionally compared the PCD results with simulations using a simple head model (“3c” with scalp, bone and a homogeneous intracranial compartment). 2. We reconstructed the current flow from in-vivo MRCDI data (Göksu et al, 2018) with the PCD algorithm. We also used head models of different complexities (“3c” and “4c”: scalp, skull, CSF & brain) and optimized their conductivities to minimize the root-mean-square difference between the measured and simulated Bz. Results: 1. For simulated Bz data, the PCD algorithm only coarsely reconstructed the true current flow. Even the simple head model performed better. 2. For measured Bz data, current flows obtained with personalized head models and fitted conductivities explained the measurements better than the current flow reconstructed with the PCD algorithm. This was already the case for the simple head model (3c). The more detailed model (4c) resulted in further statistically significant improvements. However, for all models, the unexplained variance stayed above the noise floor, indicating remaining differences to unknown true current flow. Conclusions: The PCD algorithm has low accuracy for MRCDI data of the brain. However, MRCDI is useful for evaluations and improvements of current flow simulations with anatomically detailed personalized head models. Keywords: magnetic resonance current density imaging, personalized electric field calculation, dose control, transcranial electrical stimulation

Four-channel current switching device to enable multi-electrode magnetic resonance current density imaging.

Neurostimulation with multiple scalp electrodes has shown enhanced effects in recent studies. However, visualizations of stimulation-induced internal current distributions in brain is only possible through simulated current distributions obtained from computer model of human head. While magnetic resonance current density imaging (MRCDI) has a potential for direct in-vivo measurement of currents induced in brain with multi-electrode stimulation, existing MRCDI methods are only developed for two-electrode neurostimulation. A major bottleneck is the lack of a current switching device which is typically used to convert the DC current of neurostimulation devices into user-defined waveforms of positive and negative polarity with delays between them. In this work, we present a design of a four-electrode current switching device to enable simultaneous switching of current flowing through multiple scalp electrodes.

SNR and total acquisition time analysis of multi-echo FLASH pulse sequence for current density imaging

Magnetic Resonance Current Density Imaging (MRCDI) is an imaging modality providing cross-sectional current density (J¯) information inside the body. The clinical applicability of MRCDI is highly dependent on the sensitivity of the acquired noisy current-induced magnetic flux density (B∼z) distributions.Here, a novel analysis is developed to investigate the combined effect of relevant parameters of the RF spoiled gradient echo (FLASH) pulse sequence on the SNR level and the total acquisition time (TAT) of the acquired B∼z images. The proposed analysis then is expanded for a multi-echo FLASH (ME-FLASH) pulse sequence to take advantage of combining the multiple echoes to achieve B∼zcomb distribution with a higher SNR than the one achievable with a single echo acquisition.The optimized sequence parameters to acquire a B∼z distribution with the highest possible SNR for a given acquisition time or the desired SNR in the shortest scan time are estimated using the proposed analysis. The analysis also provides different sets of sequence parameters to acquire B∼z distributions with the same SNR at almost the same TAT. Furthermore, the effects of intensive utilization of the gradients and the magnetohydrodynamic (MHD) flow velocity on the acquired B∼z distribution in MRCDI experiments is investigated.The analytical results of the proposed analysis are validated experimentally using an imaging phantom having the conductivity and the relaxation parameters of the brain white matter tissue.

On the reconstruction of magnetic resonance current density images of the human brain: Pitfalls and perspectives

Magnetic resonance current density imaging (MRCDI) of the human brain aims to reconstruct the current density distribution caused by transcranial electric stimulation from MR-based measurements of the current-induced magnetic fields. So far, the MRCDI data acquisition achieves only a low signal-to-noise ratio, does not provide a full volume coverage and lacks data from the scalp and skull regions. In addition, it is only sensitive to the component of the current-induced magnetic field parallel to the scanner field. The reconstruction problem thus involves coping with noisy and incomplete data, which makes it mathematically challenging. Most existing reconstruction methods have been validated using simulation studies and measurements in phantoms with simplified geometries. Only one reconstruction method, the projected current density algorithm, has been applied to human in-vivo data so far, however resulting in blurred current density estimates even when applied to noise-free simulated data.We analyze the underlying causes for the limited performance of the projected current density algorithm when applied to human brain data. In addition, we compare it with an approach that relies on the optimization of the conductivities of a small number of tissue compartments of anatomically detailed head models reconstructed from structural MR data. Both for simulated ground truth data and human in-vivo MRCDI data, our results indicate that the estimation of current densities benefits more from using a personalized volume conductor model than from applying the projected current density algorithm. In particular, we introduce a hierarchical statistical testing approach as a principled way to test and compare the quality of reconstructed current density images that accounts for the limited signal-to-noise ratio of the human in-vivo MRCDI data and the fact that the ground truth of the current density is unknown for measured data. Our results indicate that the statistical testing approach constitutes a valuable framework for the further development of accurate volume conductor models of the head. Our findings also highlight the importance of tailoring the reconstruction approaches to the quality and specific properties of the available data.

Sensitivity and resolution improvement for in vivo magnetic resonance current‐density imaging of the human brain

Magnetic resonance current-density imaging (MRCDI) combines MRI with low-intensity transcranial electrical stimulation (TES; 1-2 mA) to map current flow in the brain. However, usage of MRCDI is still hampered by low measurement sensitivity and image quality. Recently, a multigradient-echo-based MRCDI approach has been introduced that presently has the best-documented efficiency. This MRCDI approach has now been advanced in three directions and has been validated by phantom and in vivo experiments. First, the importance of optimum spoiling for brain imaging was verified. Second, the sensitivity and spatial resolution were improved by using acquisition weighting. Third, navigators were added as a quality control measure for tracking physiological noise. Combining these advancements, the optimized MRCDI method was tested by using 1 mA TES for two different injection profiles. For a session duration of 4:20 min, the new MRCDI method was able to detect TES-induced magnetic fields at a sensitivity level of 84 picotesla, representing a twofold efficiency increase against our original method. A comparison between measurements and simulations based on personalized head models showed a consistent increase in the coefficient of determination of ΔR2 = 0.12 for the current-induced magnetic fields and ΔR2 = 0.22 for the current flow reconstructions. Interestingly, some of the simulations still clearly deviated from the measurements despite the strongly improved measurement quality. This highlights the utility of MRCDI to improve head models for TES simulations. The achieved sensitivity improvement is an important step from proof-of-concept studies toward a broader application of MRCDI in clinical and basic neuroscience research.

Fast evaluation of the Biot-Savart integral using FFT for electrical conductivity imaging

Magnetic resonance electrical impedance tomography (MREIT) and current density imaging (MRCDI) are emerging as new methods to non-invasively assess the electric conductivity of and current density distributions within biological tissue in vivo. The accurate and fast computation of magnetic fields caused by low frequency electrical currents is a central component of the development, evaluation and application of reconstruction methods that underlie the estimations of the conductivity and current density, respectively, from the measured MR data. However, methods for performing these computations have not been well established in the literature. In the current work, we describe a fast and efficient technique to evaluate the Biot-Savart integral based on the fast Fourier transform (FFT), and evaluate its convergence. We show that the method can calculate magnetic fields in realistic human head models in one minute on a standard computer, while keeping error below 2%.

The stray magnetic fields in Magnetic Resonance Current Density Imaging (MRCDI)

PurposeMR Current Density Imaging (MRCDI) involves weak current-injection into the head. The resulting magnetic field changes are measured by MRI. Stray fields pose major challenges since these can dominate the fields caused by tissue currents. We analyze the sources and influences of stray fields. MethodsFirst, we supply validation data for a recently introduced MRCDI method with an unprecedented noise floor of ∼0.1 nT in vivo. Second, we assess the accuracy limit of the method and our corresponding cable current correction in phantoms ensuring high signal-to-noise ratio (SNR). Third, we simulate the influence of stray fields on current flow reconstructions for various realistic experimental set-ups. Fourth, we experimentally determine the physiological field variations. Finally, we explore the consequences of head positioning in an exemplary head coil, since off-center positioning provides space for limiting cable-induced fields. ResultsThe cable correction method performs well except near the cables. Unless correcting for cable currents, the reconstructed current flow is easily misestimated by up to 45% for a realistic experimental set-up. Stray fields dominating the fields caused by tissue currents can occur, e.g. due to a wire segment 20 cm away from the imaged region, or due to a slight cable misalignment of 3°. The noise is increased by 40% due to physiological factors. Minor patient movements can cause field changes of ∼40 nT. Off-centered head positioning can locally reduce SNR by e.g. 30%. ConclusionsQuantification of stray fields showed that MRCDI requires careful field correction. After cable correction, physiological noise is a limiting factor.

OA019] Human in-vivo Magnetic Resonance Current Density Imaging (MRCDI) and MR Electrical Impedance Tomography (MREIT)

Purpose Information on the electrical tissue conductivity might be useful for the diagnosis and characterization of pathologies such as tumors [1] . MRCDI and MREIT are two emerging non-invasive techniques for imaging of weak currents and ohmic conductivities. In this study, we demonstrated human in vivo brain MRCDI to pave the way for its clinical use [2] , [3] . Methods In short, weak alternating currents up to 1–2 mA are injected into human head in synchrony with tailored phase-sensitive MRI. The currents create a magnetic field Δ B z , c , which shifts the precession frequency of the magnetization and modulates the acquired MR images. The acquired images are used to measure Δ B z , c and reconstruct the current flow and conductivity distributions. We employed a steady-state free precession free-induction-decay (SSFP-FID) sequence in five subjects, and injected currents of 1 mA by an MR-conditional current source via electrodes attached to the scalp (two current profiles: Right-left (RL), electrodes placed near the temporoparietal junctions; anterior-posterior (AP), one attached to the forehead and one above the inion). Additionally, an ultra-short-echo-time sequence was performed to track the feeding cables for correcting the stray magnetic fields induced by cable currents. Corrected Δ B z , c measurements were used to calculate current flow distributions and compared with Finite-Element simulations of the current flow based on individualized head models [4] . Results The current-induced magnetic field Δ B z , c with ≤ 1 nT was reliably measured and the reconstructed current flows showed good agreement with the simulations (average coefficient of determination R2= 71%). The injected current flow differed substantially among individuals according to the electrode placements and anatomical differences. The calculated currents are stronger in CSF-filled highly conductive regions, e.g. the longitudinal fissure. Conclusions The strong correlation between the simulations and measurements validates the accuracy of the method and demonstrates the potential of the method for determining accurate brain tissue conductivities. These initial current flow recordings pave the way for human brain MREIT that might complement standard MR methods for tumor characterization.

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 ΔBz,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 ΔBz,c for the reconstruction of the current flow and ohmic conductivity.Here, we demonstrate reliable ΔBz,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 ΔBz,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 ΔBz,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 ΔBz,c fields as weak as 1 nT, caused by currents of 1 mA strength. Comparison of the ΔBz,c measurements with simulated ΔBz,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 ΔBz,c data. Reconstructions from non-corrected ΔBz,c measurements systematically overestimate the current densities. Comparing the current densities reconstructed from corrected ΔBz,c measurements and from simulated ΔBz,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.

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

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.

Experimental realization of induced current magnetic resonance current density imaging.

In this paper, recently proposed Induced Current Magnetic Resonance Current Density Imaging (ICMRCDI) is experimentally realized. The reconstructed current density images from the simulated measurements and from the physical measurements are in agreement. The proposed method is promising in reconstructing images of electrical conductivity as well as images of induced current density distribution within the body.

Magnetic Resonance Current Density Imaging for Customizing Transcranial Direct Current Stimulation - A Simulation Study

Transcranial direct current stimulation (tDCS) involves passing low currents through the brain and is a promising tool for inducing alteration of cortical excitability. However, tDCS presents challenges in terms of optimization of the electrode placement and stimulation parameters especially in cases of heterogeneously damaged cortical structures. The analysis of a simplified multi-shell computational head-model indicated that the neuronal fibers lying perpendicular to the electrode-scalp interface in the white matter (WM) beneath the stimulating anode are subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface are exposed to a hyperpolarizing drive, but in the gray matter (GM) the opposite was observed. Moreover, parameter sensitivity analysis revealed that the neuronal fibers lying perpendicular to the electrode-scalp interface in the GM beneath the anode were subjected to a depolarizing drive while those lying parallel to the electrode-scalp interface were exposed to a hyperpolarizing drive when the WM and GM conductivity were 0.13S/m and 0.2S/m respectively. However, when the WM conductivity was increased to 1.1 S/m (GM at 0.2 S/m), the results were reversed. This highlighted the importance of accurate conductivity values in computational head-model in the cases of diseased brain tissue. Here, anisotropic conductivity and fiber trajectories can be estimated from diffusion magnetic resonance imaging (dMRI) which provides excellent anatomical details, and can be used to differentiate between healthy, ischemic, and perilesional brain tissue. Such dMRI-guided patient-specific head-models can be used to optimize electrode placement and stimulation parameters that is based on the distributions of the generalized activating function. Here, a Magnetic Resonance Current Density Imaging (MRCDI) based method is proposed for dMRI-guided subject-specific parameter estimation and model validation that is a novel approach in our opinion.

Realization of magnetic resonance current density imaging at 3 tesla.

Magnetic Resonance Current Density Imaging (MRCDI) is an imaging modality, which reconstructs electrical current density distribution inside a material by using Magnetic Resonance Imaging (MRI) techniques. In this study, a current source with maximum current injection capability of 224.7mA, under 1kΩ resistive load is used. Experiments are performed with a 2D uniform phantom, in which a current steering insulator is inserted. Magnetic flux density distributions are measured, and current density images are reconstructed. The reconstructed images are in agreement with the reconstructions obtained with simulated measurements.

Practical Realization of Magnetic Resonance Conductivity Tensor Imaging (MRCTI)

Magnetic resonance conductivity tensor imaging (MRCTI) is an emerging modality which reconstructs images of anisotropic conductivity distribution within a volume conductor. Images are reconstructed based on magnetic flux density distribution induced by an externally applied probing current, together with a resultant surface potential value. The induced magnetic flux density distribution is measured using magnetic resonance current density imaging techniques. In this study, MRCTI data acquisition is experimentally implemented and anisotropic conductivity images of test phantoms are reconstructed using recently proposed MRCTI reconstruction algorithms.

Magnetic resonance current density imaging using one component of magnetic flux density

Current density distribution generated inside a volume conductor by externally applied currents can be calculated by using spatial distribution of its magnetic flux density, . The imaging modality used to reconstruct images of the current density distribution is known as magnetic resonance current density imaging (MRCDI). In MRCDI, spatial distribution of the current‐induced magnetic flux density is measured on a magnetic resonance imaging (MRI) platform. Calculation of current density distribution from magnetic flux density measurements requires all three components of . As only the component of which is in parallel with the main field of the MRI system can be measured, rotation of the subject is necessary to be able to acquire all three components of for a given current injection pattern. Rotating the subject inside the magnet is not trivial even for small‐sized objects and remains as a strong limitation to clinical applicability of the technique. In this study, a novel MRCDI reconstruction algorithm using only one component of is proposed to eliminate the need for subject rotation. Reconstruction performance of the proposed algorithm is evaluated on numerical and experimental models. Images obtained by the proposed and the conventional MRCDI algorithms, which utilizes three components of , are compared.

Fourier transform magnetic resonance current density imaging (FT-MRCDI) from one component of magnetic flux density

Fourier transform (FT)-based algorithms for magnetic resonance current density imaging (MRCDI) from one component of magnetic flux density have been developed for 2D and 3D problems. For 2D problems, where current is confined to the xy-plane and z-component of the magnetic flux density is measured also on the xy-plane inside the object, an iterative FT-MRCDI algorithm is developed by which both the current distribution inside the object and the z-component of the magnetic flux density on the xy-plane outside the object are reconstructed. The method is applied to simulated as well as actual data from phantoms. The effect of measurement error on the spatial resolution of the current density reconstruction is also investigated. For 3D objects an iterative FT-based algorithm is developed whereby the projected current is reconstructed on any slice using as data the Laplacian of the z-component of magnetic flux density measured for that slice. In an injected current MRCDI scenario, the current is not divergence free on the boundary of the object. The method developed in this study also handles this situation.

Noninvasive Imaging of Head-Brain Conductivity Profiles

Magnetic resonance electrical impedance tomography (MREIT) is a recently introduced noninvasive conductivity imaging modality, which combines the magnetic resonance current density imaging (MRCDI) and the traditional electrical impedance tomography (EIT) techniques. MREIT is aimed at providing high-spatial-resolution images of electrical conductivity because it avoids solving the well-known ill-posed problem in the traditional EIT. In this article, we review our research activities in MREIT imaging of head-brain tissue conductivity profiles. We have developed several imaging algorithms and conducted a series of computer simulations for MREIT imaging of the head-brain tissues. Our work suggests that MREIT brain imaging may become a useful tool in imaging conductivity distributions of the brain and head.