In vivoconductivity imaging of human knee using 3 mA injection current in MREIT

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.

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.

A feasibility study of magnetic resonance electrical impedance tomography for prostate cancer detection

Magnetic resonance electrical impedance tomography (MREIT) is an imaging technique that reconstructs the conductivity distribution inside the subject using magnetic flux density or current density measurements acquired by a magnetic resonance imaging system. Since the primary prostate cancer diagnostic method, prostate biopsy, has limited accuracy in cancer diagnosis and malignant tissues have shown significantly different electrical properties from normal or benign tissues, MREIT has potential application in prostate cancer detection. The feasibility of utilizing MREIT in detecting prostate cancer was evaluated via a series of well-designed computer simulations in the present study. MREIT techniques with three different electrode configurations (external, trans-rectal, and trans-urethral electrode arrays) and two different reconstruction algorithms (J-substitution algorithm and harmonic Bz algorithm) were successfully developed. The performance of different MREIT techniques were evaluated and compared based on the imaging accuracy of the reconstructed conductivity distribution in the prostate. Without the presence of noise, the external MREIT achieves a better imaging accuracy than the two endo-MREIT (trans-rectal and trans-urethral) techniques, while the trans-urethral MREIT achieves the best imaging accuracy in noisy environments. We also found that the J-substitution reconstruction algorithm consistently offered better imaging accuracy than the harmonic Bz algorithm. When Gaussian distributed random noise with a standard deviation of 0.25 nT was added, the relative errors (RE) between the reconstructed and target conductivity distributions inside the prostate were observed to be 14.18% and 17.35% by the trans-urethral MREIT with the J-substitution and harmonic Bz algorithms respectively. The lower REs of 9.64% and 11.17% were achieved respectively when the standard deviation of noise was reduced to 0.05 nT. The simulation results demonstrate the feasibility of applying MREIT for prostate cancer detection.

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.