Fast and accurate MEG source localization using deep learning

Hanchen Wang; Shihang Feng; Qian Zhang; Young Jin Kim; Igor Savukov; Lan Yang; Youzuo Lin

doi:10.1117/12.3006914

1 April 2024 Fast and accurate MEG source localization using deep learning

Hanchen Wang, Shihang Feng, Qian Zhang, Young Jin Kim, Igor Savukov, Lan Yang, Youzuo Lin

Proceedings Volume 12925, Medical Imaging 2024: Physics of Medical Imaging; 129254E (2024) https://doi.org/10.1117/12.3006914
Event: SPIE Medical Imaging, 2024, San Diego, California, United States

Conference Poster

Abstract

Magnetoencephalography (MEG) is a pivotal neuroimaging technique for diagnosing and treating brain disorders, known for its precise measurement of the brain’s magnetic fields due to electrical activity. Accurate brain source localization is essential for neurosurgical planning, but the MEG inverse problem—determining brain source locations from MEG data—is complex and inherently ill-posed. This article introduces a novel, data-driven approach to enhance MEG source localization and brain activity characterization. We compare three encoder models, VGGNet, ViT, and ResNet, assessing their performance across varying noise levels. Our findings reveal the effectiveness of neural networks in addressing challenging neuroimaging problems, underscoring their potential in advancing MEG applications.

Citation Download Citation

Hanchen Wang, Shihang Feng, Qian Zhang, Young Jin Kim, Igor Savukov, Lan Yang, and Youzuo Lin "Fast and accurate MEG source localization using deep learning", Proc. SPIE 12925, Medical Imaging 2024: Physics of Medical Imaging, 129254E (1 April 2024); https://doi.org/10.1117/12.3006914

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available