Bayesian adaptive beamformer for robust electromagnetic brain imaging of correlated sources in high spatial resolution
Autor(in); Institution
Cai, Chang; Faculty of Artificial Intelligence in Education, Central China Normal University, Wuhan, CHINA; Department of Radiology and Biomedical Imaging University of California San Francisco San Francisco, CA, USA
Long, Yuanshun; National Engineering Research Center of Educational Big Data, Central China Normal University, Wuhan, CHINA
Ghosh, Sanjay; Department of Radiology and Bio-medical Imaging, University of California at San Francisco, USA
Hashemi, Ali; Inverse Modeling and Machine Learn- ing Group, Technische Universität Berlin, Berlin, Uncertainty, GERMANY; Machine Learning Group, Faculty IV Electrical Engineering and Computer Science, Institute of Software Engineering and Theo- retical Computer Science, Technische Universität Berlin, Berlin, GERMANY
Gao, Yijing; Department of Radiology and Bio-medical Imaging, University of California at San Francisco, USA
Diwakar, Mithun; Department of Radiology, University of Colorado Anschutz, Denver, USA
Haufe, Stefan; 8.4, Mathematische Modellierung und Datenanalyse, PTB-Berlin; Inverse Modeling and Machine Learning Group, Technische Universität Berlin, Berlin, Uncertainty, GERMANY; Charité - Universitätsmedizin Berlin, Berlin, GERMANY
et al,
Quelle/Jahr
IEEE Transactions on Medical Imaging: 42
(2023), 9, 2502
- 2512
EEG ; Bayesian Adaptive Beamformer for Robust Electromagnetic ; High Spatial Resolution
Zusammenfassung
Reconstructing complex brain source activity at a high spatiotemporal resolution from magnetoencephalography (MEG) or electroencephalography (EEG) remains a challenging problem. Adaptive beamformers are routinely deployed for this imaging domain using the sample data covariance. However adaptive beamformers have long been hindered by high degree of correlation between multiple brain sources, and interference and noise embedded in sensor measurements. This study develops a novel framework for minimum variance adaptive beamformers that uses a model data covariance learned from data using a sparse Bayesian learning algorithm (SBL-BF). The learned model data covariance effectively removes influence from correlated brain sources and is robust to noise and interference without the need for baseline measurements. A multiresolution framework for model data covariance computation and parallelization of the beamformer implementation enables efficient high-resolution reconstruction images. Results with both simulations and real datasets indicate that multiple highly correlated sources can be accurately reconstructed, and that interference and noise can be sufficiently suppressed. Reconstructions at 2-2.5mm resolution ( ∼ 150K voxels) are possible with efficient run times of 1–3 minutes. This novel adaptive beamforming algorithm significantly outperforms the state-of-the-art benchmarks. Therefore, SBL-BF provides an effective framework for efficiently reconstructing multiple correlated brain sources with high resolution and robustness to interference and noise.
Themenbereich der Metrologie
Metrologie in der Medizin
Zitierung
Cai, C., Long, Y., Ghosh, S., Hashemi, A., Gao, Y., Diwakar, M., Haufe, S., & et al. (2023). Bayesian adaptive beamformer for robust electromagnetic brain imaging of correlated sources in high spatial resolution. IEEE Transactions on Medical Imaging, 42(9), 2502–2512. https://doi.org/10.1109/TMI.2023.3256963
