Current-density imaging using ultra-low-field MRI with zero-field encoding

Vesanen, Panu T.; Nieminen, Jaakko O.; Zevenhoven, Koos C. J.; Hsu, Yi‐Cheng; Ilmoniemi, Risto J.

doi:10.1016/j.mri.2014.01.012

Cited by 22 publications

(29 citation statements)

References 34 publications

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“…In addition, ULF MRI could provide information on electrical conductivity of different head compartments; this information could be used to further improve localization accuracy [ 50 ]. However, current ULF-MRI systems compatible with MEG allow to image only a part of the brain to keep the measurement duration reasonable; also, the contrast and resolution in these images are still inadequate to build an accurate volume-conductor model.…”

Section: Discussionmentioning

confidence: 99%

Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

et al. 2018

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The prototypes of ultra-low-field (ULF) MRI scanners developed in recent years represent new, innovative, cost-effective and safer systems, which are suitable to be integrated in multi-modal (Magnetoencephalography and MRI) devices. Integrated ULF-MRI and MEG scanners could represent an ideal solution to obtain functional (MEG) and anatomical (ULF MRI) information in the same environment, without errors that may limit source reconstruction accuracy. However, the low resolution and signal-to-noise ratio (SNR) of ULF images, as well as their limited coverage, do not generally allow for the construction of an accurate individual volume conductor model suitable for MEG localization. Thus, for practical usage, a high-field (HF) MRI image is also acquired, and the HF-MRI images are co-registered to the ULF-MRI ones. We address here this issue through an optimized pipeline (SWIM—Sliding WIndow grouping supporting Mutual information). The co-registration is performed by an affine transformation, the parameters of which are estimated using Normalized Mutual Information as the cost function, and Adaptive Simulated Annealing as the minimization algorithm. The sub-voxel resolution of the ULF images is handled by a sliding-window approach applying multiple grouping strategies to down-sample HF MRI to the ULF-MRI resolution. The pipeline has been tested on phantom and real data from different ULF-MRI devices, and comparison with well-known toolboxes for fMRI analysis has been performed. Our pipeline always outperformed the fMRI toolboxes (FSL and SPM). The HF–ULF MRI co-registration obtained by means of our pipeline could lead to an effective integration of ULF MRI with MEG, with the aim of improving localization accuracy, but also to help exploit ULF MRI in tumor imaging.

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Section: Discussionmentioning

confidence: 99%

Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

et al. 2018

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“…The sensitivities of single pickup coils were calculated using B s from Eq. (8). Evaluating the line integral required the coil path ∂S to be discretized.…”

Section: Methods For Numerical Studymentioning

confidence: 99%

“…For some cases, the detected flux can be calculated analytically using Eq. (8). First, as a simple example, consider a dipole at the origin, and a circular magnetometer pickup loop of radius R parallel to the xy plane at z = l, centered on the z axis.…”

Section: Sensitivity Patterns and Signal Scalingmentioning

confidence: 99%

Superconducting receiver arrays for magnetic resonance imaging

Zevenhoven

Mäkinen²,

Ilmoniemi³

2020

Biomed. Phys. Eng. Express

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Superconducting QUantum-Interference Devices (SQUIDs) make magnetic resonance imaging (MRI) possible in ultra-low microtesla-range magnetic fields. In this work, we investigate the design parameters affecting the signal and noise performance of SQUID-based sensors and multichannel magnetometers for MRI of the brain. Besides sensor intrinsics, various noise sources along with the size, geometry and number of superconducting detector coils are important factors affecting the image quality. We derive figures of merit based on optimal combination of multichannel data and provide tools for understanding the signal detection and the different noise mechanisms, as well as a guide to making design decisions for both MRI-and sensor-oriented readers.

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“…Magnetic resonance imaging (MRI) is affected by local magnetic fields, such as the magnetic field B J (r) associated with a current density J(r) at points r in the imaging volume. In particular, if also the main magnetic field B 0 can be switched on and off during the pulse sequence, it is possible to measure full-tensor information of the effects of B J (r), providing a way to directly estimate J(r) [6,7]. The field switching can be achieved [8,9] in ultra-low-field (ULF) MRI, where the main field is not produced by a persistent superconducting magnet as in conventional highfield MRI.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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Magnetic fields associated with currents flowing in tissue can be measured non-invasively by means of zero-field-encoded ultra-low-field magnetic resonance imaging (ULF MRI) enabling current-density imaging (CDI) and possibly conductivity mapping of human head tissues. Since currents applied to a human are limited by safety regulations and only a small fraction of the current passes through the relatively highly-resistive skull, a sufficient signal-to-noise ratio (SNR) may be difficult to obtain when using this method. In this work, we study the relationship between the image SNR and the SNR of the field reconstructions from zero-field-encoded data. We evaluate these results for two existing ULF-MRI scanners-one ultra-sensitive single-channel system and one whole-head multi-channel system-by simulating sequences necessary for current-density reconstruction. We also derive realistic current-density and magnetic-field estimates from finite-element-method simulations based on a three-compartment head model. We found that existing ULF-MRI systems reach sufficient SNR to detect intra-cranial current distributions with statistical uncertainty below 10%. However, the results also reveal that image artifacts influence the reconstruction quality. Further, our simulations indicate that current-density reconstruction in the scalp requires a resolution <5 mm and demonstrate that the necessary sensitivity coverage can be accomplished by multi-channel devices.

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Current-density imaging using ultra-low-field MRI with zero-field encoding

Cited by 22 publications

References 34 publications

Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

Optimized 3D co-registration of ultra-low-field and high-field magnetic resonance images

Superconducting receiver arrays for magnetic resonance imaging

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

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