Fast and Accurate MEG Source Localization using Deep Learning

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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationMedical Imaging 2024
Subtitle of host publicationPhysics of Medical Imaging
EditorsRebecca Fahrig, John M. Sabol, Ke Li
PublisherSPIE
ISBN (Electronic)9781510671546
DOIs
StatePublished - 2024
EventMedical Imaging 2024: Physics of Medical Imaging - San Diego, United States
Duration: Feb 19 2024Feb 22 2024

Publication series

NameProgress in Biomedical Optics and Imaging - Proceedings of SPIE
Volume12925
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2024: Physics of Medical Imaging
Country/TerritoryUnited States
CitySan Diego
Period02/19/2402/22/24

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