Spatial-temporal Source Reconstruction for Magnetoencephalography

Magnetoencephalography (MEG) is a new non-invasive technique for the functional imaging of the human brain. It has been widely used in both research and clinical applications, for it has several superior properties, including a high-temporal resolution with no interference from the bone or the head-like fluid to the signal spatial transformation. In this thesis, we aim to develop a framework for MEG spatial-temporal current course reconstruction by introducing classical methods from the pattern recognition theory into medical imaging. These applications provide a new angle for research in MEG source reconstruction with the solution for source reconstruction at a single point, and improvements of the reconstruction on spatially and temporally. The whole thesis is based on three topics, which are designed to be parts of an integrated reconstruction process, and each of them are interrelated, rather than independent from each other. We firstly introduce the source reconstructionmethod at a single time point using the basis function extraction. In light of the assumption that the Laplacian eigenvectors of mesh can be the analogous to the basis functions that represent the cortex mesh; we build a new model to describe the current source that is distributed on each mesh vertex. This model consists of analogous basis functions and unknown weighted coefficients. In terms of experiment results, this algorithm shows good reconstructed property to the single stimulus, as well as the supercial stimulus on the cortical surface. Secondly, with respect to the spatial reconstructed sources by basis function method from the last topic, we build a new solution for improving the spatial-resolution of MEG source reconstruction at a single time point by introducing a classical method ( the Bayesian super-resolution method) from the pattern recognition theory. Although the approach is designed based on the reconstruction from basis functions, it is also feasible for other spatial reconstruction methods to improve the spatial-resolution. From the numerical experiment results, it is apparent that the spatial resolution has been effectively improved. Then, the MEG measurement system in the temporal field is assumed to be a linear dynamic system where the classical methods, Kalman filter and Kalman smoother, are applied as the solution for the estimation of source in time course. The Kalman filter is used to estimate the dynamic state while the Kalman smoother is applied for correcting the source distribution of the hidden state with the EMalgorithm. This approach shows superior performance to solve the inverse problem. It extends the improvement in source reconstruction using the temporal field. We construct the synthetic data as well as apply the realMEG data throughout all the experimental test of my work. In summary, this thesis builds three algorithms, which aim to reconstruct the MEG source distribution on spatial and temporal field respectively aided by methods from pattern recognition. This work provides a new angle of using the pattern recognition theory for MEG source reconstruction. Meanwhile, we also explore a new direction for applying the theory of pattern recognition. This work not only provides a good integration between these two fields, but also encourage future interactions.