Evaluating the Performance of Ultra-Low-Field MRI for in-vivo 3D Current Density Imaging of the Human Head

Hömmen, Peter; Mäkinen, Antti; Hunold, Alexander; Machts, René; Haueisen, Jens; Zevenhoven, Koos C. J.; Ilmoniemi, Risto J.; Körber, Rainer

doi:10.3389/fphy.2020.00105

Cited by 6 publications

(7 citation statements)

References 31 publications

(64 reference statements)

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“…Several studies [8], [14] have indicated that low-eld MRI scanners have a low signal-to-noise ratio (SNR), resulting in noisy images. This was supported by Hömmen et al [10] that found out that image artifacts in uence the reconstruction quality.…”

Section: Introductionmentioning

confidence: 59%

“…In developing countries, low-eld MRI equipment can provide an economical, long-term, and safe imaging option to high-eld MRI and computed tomography (CT) for brain imaging [8]. Moreover, Hömmen et al [10] revealed that existing ultra low-eld MRI systems can produce a su cient signal-to-noise ratio (SNR) required for clinical imaging. Huang et al [11] revealed that portable low-cost MRI systems can provide a point of care and timely MRI diagnosis especially to low-income countries where there are less than 0.1 MRI scans per 1,000,000 people [12] [13].…”

Section: Introductionmentioning

confidence: 99%

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Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

Obungoloch

Ahishakiye

2021

Preprint

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Background: Magnetic Resonance Imaging (MRI) and spectroscopic techniques are frequently employed for clinical diagnostics as well as basic research in areas like cognitive neuroimaging. MRI is a widely used imaging modality for intracranial diseases. However, conventional MRI is expensive to purchase, maintain and sustain, limiting their use in low-income countries. Low field MRI can provide an economical, long-term, and safe imaging option to high-field MRI and computed tomography (CT) for brain imaging. This paper offers a review of the image reconstruction techniques used in low field magnetic resonance imaging (MRI). It is aimed at familiarizing the readers with the relevant knowledge, literature, and the latest updates on the state-of-art image reconstruction techniques that have been used in low field MRI citing their strengths, and areas for improvement. Methods: An in-depth keyword-based search was undertaken for publications on image reconstruction approaches in low-field MRI in the top scientific databases such as Google Scholar, Wiley, Science Direct, Springer, IEEE, Scopus, Nature, Elsevier, and PubMed throughout this study. This research also contained relevant postgraduate theses. For the selection of relevant research publications, the PRISMA flow diagram and protocol were also used.Results: Studies revealed that Inhomogeneities are present in low field MRI, implying that the traditional method of acquiring the image, using the inverse Fourier Transform, is no longer viable. The image reconstruction techniques reviewed include iterative methods, dictionary learning methods, and deep learning methods. Experimental results from the literature revealed improved image quality of the reconstructed images using data driven and learning based methods (deep learning and dictionary learning methods). Conclusion: The study revealed that there is limited literature on the image reconstruction approaches in low field MRI even if though there are sufficient studies on the subject in high field MRI. Data driven and learning based methods improves image reconstruction quality when compared to analytic and iterative approaches.

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Section: Introductionmentioning

confidence: 59%

Section: Introductionmentioning

confidence: 99%

Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

Obungoloch

Ahishakiye

2021

Preprint

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“…Technologies such as transcranial electric stimulation (TES), transcranial current density imaging (CDI) and neuronal source imaging based on electroencephalography (EEG) and magnetoencephalography (MEG), require methodologies for veri cation and validation. The assessment of new measurement and analysis chains can be pursued; applying (i) computational modeling and simulation [1] and (ii) metrological inspections [2,3]. While computational modeling and simulations provide a convenient way of assessing the above technologies, only metrological inspections allow the inclusion of real-world environmental in uences and can provide validation for computational modeling and simulations based on ground truth.…”

Section: Introductionmentioning

confidence: 99%

Head phantoms for bioelectromagnetic applications: a material study

Hunold

Machts

Haueisen

2020

Preprint

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Background Assessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms. Phantoms provide well-defined volume conduction configurations with realistic geometries.We aim to characterize the electrical conductivity of materials for modeling head compartments to establish reproducible and stable physical head phantoms. We analyzed sodium chloride (NaCl) solution, agarose hydrogel, gypsum and reed sticks as surrogate materials for the intracranial volume, scalp, skull and anisotropic conductivity structures. We measured the impedance of all materials when immersed in NaCl solution using a four-point setup. The electrical conductivity values of each material were calculated from the temperature compensated impedances considering the sample geometries. Results We obtained conductivities of 0.332 S/m (0.17 % NaCl solution), 0.0425 S/m and 0.0017 S/m (gypsum with and without NaCl), 0.314 S/m, 0.30 S/m, 0.311 S/m (2 %, 3 %, 4 % agarose). The reed sticks were tested in longitudinal and transversal direction and showed anisotropic conductivity with a ratio of 1:2.8. Conclusion We conclude that the tested materials NaCl solution, gypsum and agarose can serve as stable representation of the three main conductivity compartments of the head, intracranial volume, skull and scalp. An anisotropic conductivity structure such as a piece of white matter can be modeled using tailored reed sticks inside a volume conductor.

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“…Technologies such as transcranial electric stimulation (TES), transcranial current density imaging (CDI), and neuronal source imaging based on electroencephalography (EEG) and magnetoencephalography (MEG), require methodologies for veri cation and validation. The evaluation of new measurement and analysis chains can be addressed by (i) computational modeling and simulation [1] and (ii) metrological inspections [2,3]. While computational modeling and simulations provide a convenient way of assessing the above technologies, only metrological inspections allow the inclusion of real-world environmental in uences and provide the validation for computational modeling and simulations based on ground truth.…”

Section: Introductionmentioning

confidence: 99%

Head phantoms for bioelectromagnetic applications: a material study

Hunold

Machts

Haueisen

2020

Preprint

Self Cite

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Background: Assessments of source reconstruction procedures in electroencephalography and computations of transcranial electrical stimulation profiles require verification and validation with the help of ground truth configurations as implemented by physical head phantoms.. For these phantoms, synthetic materials are needed, which are mechanically and electrochemically stable and possess conductivity values similar to the modeled human head tissues. Typical three-compartment head models comprise a scalp layer with a conductivity range from 0.137 S/m to 2.1 S/m, a skull layer with conductivity values between 0.066 S/m and 0.00275 S/m, and an intracranial volume with an often-used average conductivity value of 0.33 S/m. To establish a realistically shaped physical head phantom with a well-defined volume conduction configuration we here characterize the electrical conductivity of synthetic materials for modeling head compartments. We analyze agarose hydrogel, gypsum, and sodium chloride (NaCl) solution as surrogate materials for scalp, skull, and intracranial volume. We measure the impedance of all materials when immersed in NaCl solution using a four-electrode setup. The measured impedance values, temperature compensated to 25°C, were used to calculate the electrical conductivity values of each material. Further, the conductivities in the longitudinal and transversal directions of reed sticks immersed in NaCl solution were measured to test their suitability for mimicking the anisotropic conductivity of white matter tracts.Results: We obtained conductivities of 0.314 S/m, 0.30 S/m, 0.311 S/m (2 %, 3 %, 4 % agarose), 0.0425 S/m and 0.0017 S/m (gypsum with and without NaCl in the compound), and 0.332 S/m (0.17 % NaCl solution). These values are within the range of the conductivity values used for EEG and TES modeling. The reed sticks showed anisotropic conductivity with a ratio of 1:2.8. Conclusion: We conclude that the tested materials agarose, gypsum, and NaCl solution can serve as stable representations of the three main conductivity compartments of the head scalp, skull, and intracranial volume. An anisotropic conductivity structure such as a fiber track in white matter can be modeled using tailored reed sticks inside a volume conductor.

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Evaluating the Performance of Ultra-Low-Field MRI for in-vivo 3D Current Density Imaging of the Human Head

Cited by 6 publications

References 31 publications

Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

Approaches for Image Reconstruction in Low-Field Magnetic Resonance Imaging

Head phantoms for bioelectromagnetic applications: a material study

Head phantoms for bioelectromagnetic applications: a material study

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