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

## TL;DR

This paper presents an automatic calibration method for hybrid MEG--MRI systems using ultra-low-field MRI signal models, achieving high spatial accuracy and eliminating traditional co-registration errors.

## Contribution

The introduced calibration technique leverages ULF MRI signal models to automatically determine sensor positions, reducing errors in hybrid MEG--MRI co-registration.

## Key findings

- Achieved sub-millimeter calibration accuracy in simulations.
- Eliminated co-registration errors for high-precision MEG source localization.
- Effective even with low-SNR images.

## Abstract

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 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 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.