Solution to the inverse problem in selective excitation in magnetic resonance imaging

1992 ◽  
Author(s):  
Joseph W. Carlson
Keyword(s):  
Magnetic Resonance Imaging ◽  
Inverse Problem ◽  
Magnetic Resonance ◽  
Selective Excitation ◽  
Resonance Imaging

The Need for and Road to Hybrid Magnetoencephalography–Magnetic Resonance Imaging

2020 ◽  
pp. 95-103
Author(s):  
Risto J. Ilmoniemi
Keyword(s):  
Magnetic Resonance Imaging ◽  
Inverse Problem ◽  
Magnetic Resonance ◽  
Structural Mri ◽  
Data Sets ◽  
Resonance Imaging ◽  
Anatomical Images ◽  
Meg Inverse Problem ◽  
Magnetic Resonance Imaging Mri ◽  
Priori Information

This chapter addresses the need for hybrid magnetoencephalography (MEG) and magnetic resonance imaging (MRI) systems. The importance of combining MEG with MRI was realized early. The most important benefit of MEG over the widely available electroencephalography (EEG) is its ability to locate brain activity. To relate the location coordinates to individual anatomy, structural MRI is needed. In addition, structural MRI can help constrain the estimated source currents to the cortex, making the three-dimensional source volume a two-dimensional layer. Later, after the invention of functional MRI (fMRI), it was realized that the new kind of data could be used as additional information to help solve the MEG inverse problem. Thus, structural MRI benefits MEG data interpretation in three main ways: first, MEG localization results can be displayed on top of anatomical images; second, one obtains geometrical information for the analysis of the inverse problem, for example, in beamforming; third, a priori information regarding source locations will be more accurate. Since MEG and MRI are normally done separately, the two data sets have to be combined. This requires co-registration of the MEG and MRI coordinate systems.

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