In vivoconductivity imaging of canine male pelvis using a 3T MREIT system

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

PURPOSE

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.

METHODS

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.

RESULTS

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.

CONCLUSION

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.

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.

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.

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.