Automatic spatial calibration of ultra-low-field MRI for high-accuracy hybrid MEG-MRI
Research output: Contribution to journal › Article › Scientific › peer-review
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.
|Number of pages||11|
|Journal||IEEE Transactions on Medical Imaging|
|Publication status||Published - 1 Jun 2019|
|MoE publication type||A1 Journal article-refereed|
- Calibration, Co-registration, Hybrid MEG-MRI, Magnetoencephalography, Sensitivity profile, Spatial accuracy, ULF MRI