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Paper Abstract and Keywords
Presentation 2024-03-03 10:17
[Short Paper] Electric field regression from head MR image by transformers for TMS
Toyohiro Maki, Tatsuya Yokota, Akimasa Hirata, Hidekata Hontani (NIT) MI2023-36
Abstract (in Japanese) (See Japanese page) 
(in English) Transcranial Magnetic Stimulation (TMS) is a non-invasive stimulation method by electric field induced by a coil placed above the head. It is necessary to compute the electric field accurately to identify the position stimulated by the coil. The electric field can be computed by electromagnetic simulation from magnetic resonance (MR) images of the subject. The electromagnetic simulation consist of two processes: the development of volume conductor model (VCM) that represent the spatial distribution of electrical conductivity in the head, and the computation of the electric field from the VCM. However, both process encounter bottlenecks that prevent real-time estimation. Several methods that use deep neural networks (DNN) for electric field regression have been proposed in recent years. These methods used convolutional neural networks and regressed restricted region below the coil. In this study, we estimated the electric field in real-time by transformer. From electromagnetism, the electric field is not determined only in the local region.
Hence, we adapt transformer that has large field of view, and regress to whole brain.
In addition, we pretrained the transformer using approximate two thousand MR images.
It has been known that segmentation accuracy is improved by pretraing transformer in medical image domain.
The VCM is constructed by segmenting MRI images by brain tissue and assigning the corresponding conductivity.
We hope that regression accuracy of the electric field is improved by the operation that can be improved the segmentation accuracy.
Keyword (in Japanese) (See Japanese page) 
(in English) Transcranial Magnetic Stimulation / Deep learning / Electric field estimation / / / / /  
Reference Info. IEICE Tech. Rep., vol. 123, no. 411, MI2023-36, pp. 21-24, March 2024.
Paper # MI2023-36 
Date of Issue 2024-02-25 (MI) 
ISSN Online edition: ISSN 2432-6380
Copyright
and
reproduction		 All rights are reserved and no part of this publication may be reproduced or transmitted in any form or by any means, electronic or mechanical, including photocopy, recording, or any information storage and retrieval system, without permission in writing from the publisher. Notwithstanding, instructors are permitted to photocopy isolated articles for noncommercial classroom use without fee. (License No.: 10GA0019/12GB0052/13GB0056/17GB0034/18GB0034)
Download PDF MI2023-36

Conference Information
Committee MI  
Conference Date 2024-03-03 - 2024-03-04 
Place (in Japanese) (See Japanese page) 
Place (in English) OKINAWAKEN SEINENKAIKAN 
Topics (in Japanese) (See Japanese page) 
Topics (in English) Medical Imaging, etc. 
Paper Information
Registration To MI 
Conference Code 2024-03-MI 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) Electric field regression from head MR image by transformers for TMS 
Sub Title (in English)  
Keyword(1) Transcranial Magnetic Stimulation  
Keyword(2) Deep learning  
Keyword(3) Electric field estimation  
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1st Author's Name Toyohiro Maki  
1st Author's Affiliation Nagoya Institute of Technology (NIT)
2nd Author's Name Tatsuya Yokota  
2nd Author's Affiliation Nagoya Institute of Technology (NIT)
3rd Author's Name Akimasa Hirata  
3rd Author's Affiliation Nagoya Institute of Technology (NIT)
4th Author's Name Hidekata Hontani  
4th Author's Affiliation Nagoya Institute of Technology (NIT)
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Speaker Author-1 
Date Time 2024-03-03 10:17:00 
Presentation Time 12 minutes 
Registration for MI 
Paper # MI2023-36 
Volume (vol) vol.123 
Number (no) no.411 
Page pp.21-24 
#Pages 4 
Date of Issue 2024-02-25 (MI) 

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