Practical Realization of Magnetic Resonance Conductivity Tensor Imaging (MRCTI)

Değirmenci, Evren; Eyüboğlu, B. Murat

doi:10.1109/tmi.2012.2231872

Cited by 9 publications

(8 citation statements)

References 9 publications

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“…Because only a minority of the blood vessels included in our model was within the white matter compartment, we expect no major insights for the questions addressed in the present study from modeling white matter anisotropy. Recent advances in electrical impedance tomography (EIT) and more specifically in magnetic resonance EIT (Zhang et al, 2008; Woo and Seo, 2008; Meng et al, 2013; Degirmenci and Eyuboglu, 2013; Kim et al, 2008) suggest that using individualized anisotropic and inhomogeneous conductivities for head modeling may be possible in the future, opening up exciting new possibilities in volume conductor head modeling.…”

Section: Discussionmentioning

confidence: 99%

The role of blood vessels in high-resolution volume conductor head modeling of EEG

et al. 2016

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Reconstruction of the electrical sources of human EEG activity at high spatiotemporal accuracy is an important aim in neuroscience and neurological diagnostics. Over the last decades, numerous studies have demonstrated that realistic modeling of head anatomy improves the accuracy of source reconstruction of EEG signals. For example, including a cerebrospinal fluid compartment and the anisotropy of white matter electrical conductivity were both shown to significantly reduce modeling errors. Here, we for the first time quantify the role of detailed reconstructions of the cerebral blood vessels in volume conductor head modeling for EEG. To study the role of the highly arborized cerebral blood vessels, we created a submillimeter head model based on ultra-high-field-strength (7 T) structural MRI datasets. Blood vessels (arteries and emissary/intraosseous veins) were segmented using Frangi multi-scale vesselness filtering. The final head model consisted of a geometry-adapted cubic mesh with over 17 × 106 nodes. We solved the forward model using a finite-element-method (FEM) transfer matrix approach, which allowed reducing computation times substantially and quantified the importance of the blood vessel compartment by computing forward and inverse errors resulting from ignoring the blood vessels. Our results show that ignoring emissary veins piercing the skull leads to focal localization errors of approx. 5 to 15 mm. Large errors (>2 cm) were observed due to the carotid arteries and the dense arterial vasculature in areas such as in the insula or in the medial temporal lobe. Thus, in such predisposed areas, errors caused by neglecting blood vessels can reach similar magnitudes as those previously reported for neglecting white matter anisotropy, the CSF or the dura — structures which are generally considered important components of realistic EEG head models. Our findings thus imply that including a realistic blood vessel compartment in EEG head models will be helpful to improve the accuracy of EEG source analyses particularly when high accuracies in brain areas with dense vasculature are required.

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Section: Discussionmentioning

confidence: 99%

The role of blood vessels in high-resolution volume conductor head modeling of EEG

et al. 2016

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“…The best performance in the simulation applications belongs to AHEPP algorithm where, in the experimental applications AJS algorithm shows the best results and AHEPP is in the second place. The conductivity images reconstructed in this study, in comparison to the results obtained in [13], using 0.15T METU-EEE MRI system and injecting 20mA current pulses, show perceptual improvement due to better signal to noise ratio (SNR) and spatial resolution. This improvement is more visible in the results of AJS algorithm.…”

Section: Discussionmentioning

confidence: 68%

“…In addition, due to better SNR of the 3T MRI scanner in comparison to the 0.15T METU-EEE MRI system, total data acquisition time is reduced by reducing the number of signal averaging. Furthermore, the amplitude of the injected currents into the imaging slice in this study is one half of the value applied in [13], which is more desirable and promising to be reduced even more for application of the technique to in vivo imaging.…”

Section: Discussionmentioning

confidence: 99%

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

Sadighi

Göksu

Eyüboğlu

2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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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.

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“…It is known that the conductivities of certain biological tissues such as muscle or white matter are anisotropic at low frequencies . For the reconstruction of anisotropic conductivity, some methods have been developed in the field of MREIT . In these methods, currents are injected through different pairs of surface electrodes so that several independent (not collinear) current density distributions are generated inside the subject in separate experiments, and the magnetic fields due to each current density distribution are measured.…”

Section: Discussionmentioning

confidence: 99%

Feasibility of conductivity imaging using subject eddy currents induced by switching of MRI gradients

Oran

Ider

2016

Magnetic Resonance in Med

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Purpose: To investigate the feasibility of low-frequency conductivity imaging based on measuring the magnetic field due to subject eddy currents induced by switching of MRI zgradients. Methods: We developed a simulation model for calculating subject eddy currents and the magnetic fields they generate (subject eddy fields). The inverse problem of obtaining conductivity distribution from subject eddy fields was formulated as a convection-reaction partial differential equation. For measuring subject eddy fields, a modified spin-echo pulse sequence was used to determine the contribution of subject eddy fields to MR phase images. Results: In the simulations, successful conductivity reconstructions were obtained by solving the derived convectionreaction equation, suggesting that the proposed reconstruction algorithm performs well under ideal conditions. However, the level of the calculated phase due to the subject eddy field in a representative object indicates that this phase is below the noise level and cannot be measured with an uncertainty sufficiently low for accurate conductivity reconstruction. Furthermore, some artifacts other than random noise were observed in the measured phases, which are discussed in relation to the effects of system imperfections during readout. Conclusion: Low-frequency conductivity imaging does not seem feasible using basic pulse sequences such as spin-echo on a clinical MRI scanner. Magn Reson

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Practical Realization of Magnetic Resonance Conductivity Tensor Imaging (MRCTI)

Cited by 9 publications

References 9 publications

The role of blood vessels in high-resolution volume conductor head modeling of EEG

The role of blood vessels in high-resolution volume conductor head modeling of EEG

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

Feasibility of conductivity imaging using subject eddy currents induced by switching of MRI gradients

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