TY - GEN
T1 - Fast and Accurate MEG Source Localization using Deep Learning
AU - Wang, Hanchen
AU - Feng, Shihang
AU - Zhang, Qian
AU - Kim, Young Jin
AU - Savukov, Igor
AU - Yang, Lan
AU - Lin, Youzuo
N1 - Publisher Copyright:
© 2024 SPIE.
PY - 2024
Y1 - 2024
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85193550436&partnerID=8YFLogxK
U2 - 10.1117/12.3006914
DO - 10.1117/12.3006914
M3 - Conference contribution
AN - SCOPUS:85193550436
T3 - Progress in Biomedical Optics and Imaging - Proceedings of SPIE
BT - Medical Imaging 2024
A2 - Fahrig, Rebecca
A2 - Sabol, John M.
A2 - Li, Ke
PB - SPIE
T2 - Medical Imaging 2024: Physics of Medical Imaging
Y2 - 19 February 2024 through 22 February 2024
ER -