This Thesis concentrates on methods for modeling and analyzing the magnetic field in magnetic brain imaging. The work is motivated by the combination of two brain imaging modalities, magnetoencephalography (MEG) and magnetic resonance imaging (MRI), in a single MEG–MRI device. In magnetoencephalography, brain functions are studied by recording the magnetic-field distribution generated by electrical brain activity. With magnetic resonance imaging, the structure of the head can be analyzed. The combination of these two imaging methods is enabled by applying ultra-low magnetic fields for MRI (ULF MRI). Besides the imaging structure, ultra-low-field MRI can be made sensitive to the magnetic field generated by small currents, enabling current density imaging (CDI). This method can be utilized to estimate the current flow in the head, which is needed for modeling the neuronal magnetic field in MEG and especially the electric field studied by electroencephalography (EEG). Magnetic-field modeling was first applied for spatial calibration of ULF MRI, which enables enhancing the spatial accuracy of MEG when measured with the hybrid MEG–MRI device. Second, MR imaging of magnetic fields generated by injected currents inside the human head was simulated to study the performance of CDI. Third, the electric and magnetic fields generated by brain activity were analyzed to study the effect of field sampling in MEG and EEG. Last, general computational tools were developed for modeling and designing magnetic fields produced, e.g., by the electromagnetic coils used in MRI. Altogether, this Thesis provides computational and methodological tools that facilitate the analysis and design of biomagnetic experiments for brain research.
|Translated title of the contribution||Magneettikentän mallinnuksen sovelluksia yhdistettyä MEG–MRI-kuvantamista varten|
|Publication status||Published - 2020|
|MoE publication type||G5 Doctoral dissertation (article)|
- magnetic resonance imaging
- magnetic field
- computational methods