Dry phantom for magnetoencephalography —Configuration, calibration, and contribution

Oyama, Daisuke; Adachi, Yoshiaki; Yumoto, Masato; Hashimoto, Isao; Uehara, Gen

doi:10.1016/j.jneumeth.2015.05.004

Cited by 26 publications

(18 citation statements)

References 23 publications

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“…In this experiment, the smallest source localization error was 4.0 mm when I = 100 A and the averaging number is 400. This error was much larger than that obtained by the SQUID-based MEG systems 7) . A lower signal-to-noise ratio, smaller measuring points, and a lack of accuracy of sensor positioning are considered to be the causes of the larger source localization error.…”

Section: Discussionmentioning

confidence: 56%

“…Quantitative evaluation can be achieved using phantom experiments instead of examination of a human subject. The authors have developed a new phantom and an associated calibration method designed for quantitative evaluation of MEG systems 7) . This phantom was calibrated and its uncertainty was determined so as to ensure reproducibility and reliability.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

2017

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In recent years, many kinds of magnetic sensors have been developed for biomagnetic measurement, such as magnetocardiography (MCG) and magnetoencephalography (MEG). However, it is difficult to evaluate their performance using only actual MCG or MEG measurements. In this paper, we propose the use of the calibrated MEG phantom for quantitative evaluation of magnetic sensors and present the experimental method. We choose a magneto-impedance (MI) sensor as an example of the magnetic sensor to be evaluated. The magnetic field distribution near the phantom was measured using the MI sensor and a signal source was localized with different averaging numbers and different signal source intensities. The results suggest that the MEG signal cannot be observed in the usual averaging time (i.e., 100), even when the sensor is located near the head; 4.0 mm of source localization accuracy can be achieved with 400-times averaging if the sensor noise decreases to 1/10. The use of the calibrated phantom, instead of examination with human subjects, is effective for quantitative evaluation of the performance of magnetic sensors.

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

2017

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“…However, any error in the ULF MRI calibration could then produce errors in the sensor-array geometry. To keep the calibration of the array geometry independent of the ULF MRI calibration, we assumed the sensor array was calibrated, e.g., with a geometrically accurate electrical phantom [13], [28], [29]. Nonetheless, it is not known if alternating iterations of spatial ULF MRI calibrations and sensor array calibrations using only ULF MRI data could be used to perform both calibrations.…”

Section: Discussionmentioning

confidence: 99%

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG–MRI

Mäkinen

Zevenhoven

Ilmoniemi

2019

IEEE Trans. Med. Imaging

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With a hybrid MEG-MRI device that uses the same sensors for both modalities, the co-registration of MRI and MEG data can be replaced by an automatic calibration step. Based on the highly accurate signal model of ultra-low-field (ULF) MRI, we introduce a calibration method that eliminates the error sources of traditional co-registration. The signal model includes complex sensitivity profiles of the superconducting pickup coils. In ULF MRI, the profiles are independent of the sample and therefore well-defined. In the most basic form, the spatial information of the profiles, captured in parallel ULF-MR acquisitions, is used to find the exact coordinate transformation required. We assessed our calibration method by simulations assuming a helmetshaped pickup-coil-array geometry. Using a carefully constructed objective function and sufficient approximations, even with low-SNR images, sub-voxel and sub-millimeter calibration accuracy was achieved. After the calibration, distortion-free MRI and high spatial accuracy for MEG source localization can be achieved. For an accurate sensor-array geometry, the co-registration and associated errors are eliminated, and the positional error can be reduced to a negligible level.

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“…This head dry phantom contains 32 current dipoles. The phantom is based on the mathematical fact that an equilateral triangular line current produces equivalent magnetic field distribution to that of a tangential current dipole in a spherical conductor, provided that the vertex of the triangle and the origin of the conducting sphere coincide [43]. The phantom dipoles are energized using an internal signal generator to get a dipole moment of 12.7 nAm.…”

Section: A Simulated Meg Recordings With a Brain Phantommentioning

confidence: 99%

“…Recordings were first post-processed with SSS [43] after identifying bad channels and periods contaminated by artifacts. Epochs from 2.5 seconds before to 2.5 seconds after the beep sound were extracted.…”

Section: Squidsmentioning

confidence: 99%

Magnetoencephalography With Optically Pumped⁴He Magnetometers at Ambient Temperature

Labyt¹,

Corsi²,

Fourcault³

et al. 2019

IEEE Trans. Med. Imaging

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In this paper, we present the first proof of concept confirming the possibility to record magnetoencephalographic (MEG) signals with Optically Pumped Magnetometers (OPMs) based on the parametric resonance of 4He atoms. The main advantage of this kind of OPM is the possibility to provide a tri-axis vector measurement of the magnetic field at room-temperature (the 4He vapor is neither cooled nor heated). The sensor achieves a sensitivity of 210 fT/√Hz in the bandwidth [2 Hz - 300 Hz]. MEG simulation studies with a brain phantom were cross-validated with real MEG measurements on a healthy subject. For both studies, MEG signal was recorded consecutively with OPMs and Superconducting Quantum Interference Devices (SQUIDs) used as reference sensors. For healthy subject MEG recordings, three MEG proofs of concept were carried out: auditory and visual evoked fields (AEF, VEF), and spontaneous activity. M100 peaks have been detected on evoked responses recorded by both OPMs and SQUIDs with no significant difference in latency. Concerning spontaneous activity, an attenuation of the signal power between 8-12 Hz (alpha band) related to eyes opening has been observed with OPM similarly to SQUID. All these results confirm that the room temperature vector 4He OPMs can record MEG signals and provide reliable information on brain activity.

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Dry phantom for magnetoencephalography —Configuration, calibration, and contribution

Cited by 26 publications

References 23 publications

Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG–MRI

Magnetoencephalography With Optically Pumped⁴He Magnetometers at Ambient Temperature

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Dry phantom for magnetoencephalography —Configuration, calibration, and contribution

Cited by 26 publications

References 23 publications

Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

Evaluation Method of Magnetic Sensors Using the Calibrated Phantom for Magnetoencephalography

Automatic Spatial Calibration of Ultra-Low-Field MRI for High-Accuracy Hybrid MEG–MRI

Magnetoencephalography With Optically Pumped4He Magnetometers at Ambient Temperature

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Product

Resources

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Magnetoencephalography With Optically Pumped⁴He Magnetometers at Ambient Temperature