Automatic spatial calibration of ultra-low-field MRI for high-accuracy hybrid MEG-MRI

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Automatic spatial calibration of ultra-low-field MRI for high-accuracy hybrid MEG-MRI. / Makinen, Antti J.; Zevenhoven, Koos C.J.; Ilmoniemi, Risto J.

In: IEEE Transactions on Medical Imaging, Vol. 38, No. 6, 8672109, 01.06.2019, p. 1317-1327.

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@article{7d8f07168d22404d86f48282f6319251,
title = "Automatic spatial calibration of ultra-low-field MRI for high-accuracy hybrid MEG-MRI",
abstract = "With a hybrid magnetoencephalography (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 the 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 helmet-shaped 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 were 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.",
keywords = "Calibration, Co-registration, Hybrid MEG-MRI, Magnetoencephalography, Sensitivity profile, Spatial accuracy, ULF MRI",
author = "Makinen, {Antti J.} and Zevenhoven, {Koos C.J.} and Ilmoniemi, {Risto J.}",
note = "| openaire: EC/H2020/686865/EU//BREAKBEN",
year = "2019",
month = "6",
day = "1",
doi = "10.1109/TMI.2019.2905934",
language = "English",
volume = "38",
pages = "1317--1327",
journal = "IEEE Transactions on Medical Imaging",
issn = "0278-0062",
publisher = "Institute of Electrical and Electronics Engineers",
number = "6",

}

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TY - JOUR

T1 - Automatic spatial calibration of ultra-low-field MRI for high-accuracy hybrid MEG-MRI

AU - Makinen, Antti J.

AU - Zevenhoven, Koos C.J.

AU - Ilmoniemi, Risto J.

N1 - | openaire: EC/H2020/686865/EU//BREAKBEN

PY - 2019/6/1

Y1 - 2019/6/1

N2 - With a hybrid magnetoencephalography (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 the 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 helmet-shaped 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 were 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.

AB - With a hybrid magnetoencephalography (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 the 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 helmet-shaped 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 were 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.

KW - Calibration

KW - Co-registration

KW - Hybrid MEG-MRI

KW - Magnetoencephalography

KW - Sensitivity profile

KW - Spatial accuracy

KW - ULF MRI

UR - http://www.scopus.com/inward/record.url?scp=85065226414&partnerID=8YFLogxK

U2 - 10.1109/TMI.2019.2905934

DO - 10.1109/TMI.2019.2905934

M3 - Article

VL - 38

SP - 1317

EP - 1327

JO - IEEE Transactions on Medical Imaging

JF - IEEE Transactions on Medical Imaging

SN - 0278-0062

IS - 6

M1 - 8672109

ER -

ID: 35318308