Development of realistic head models for electromagnetic source imaging of the human brain

Akahn, Z.
Acar, C.E.
Gençer, Nevzat Güneri
In this work, a methodology is developed to solve the forward problem of electromagnetic source imaging using realistic head models. For this purpose, first segmentation of the 3 dimensional MR head images is performed. Then triangular, quadratic meshes are formed for the interfaces of the tissues. Thus, realistic meshes, representing scalp, skull, CSF, brain and eye tissues, are formed. At least 2000 nodes for the scalp and 5000 for the cortex are needed to obtain reasonable geometrical approximation. Solution of the forward problem using our previous Boundary Element Method (BEM) formulation with quadratic elements remains to be made.


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Akalm, Z; Gençer, Nevzat Güneri (2003-09-21)
In this study, the forward problem of electrical source imaging (ESI) is solved using the Boundary Element method (BEM) with realistic head models. The realistic model consists of scalp, skull, CSF, brain and eyes. To get more accurate results quadratic elements are used in the meshes-and a new method is proposed for the generation of the quadratic mesh. An accelerated method is proposed for the solution of the potential field.
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This paper presents the primitive results of high resolution Magnetic Resonance (MR) Imaging experiments that are performed for spinal cord segmentation purposes. In the study, it is aimed to image the epidural space, the cerebrospinal fluid, the white matter and the gray matter tissues in the lower cervical and upper thoracic regions of the spine with a maximum voxel size of 1x1x1 mm(3). For this purpose, the MRI sequences providing T2 and T2* images and used for spinal cord segmentation in the literature ...
Error prediction in electromagnetic simulations using machine learning
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© 2019 IEEE.We present a novel approach of using deep convolutional neural networks (CNN) to predict electromagnetic scattering errors in iterative solutions of electrically large three-dimensional objects. Deep CNN models are constructed and trained by using surface current images to predict far-zone scattering errors. Numerical experiments demonstrate successful predictions with more than 95% accuracy. The constructed models can be useful to quickly assess the accuracy of candidate solutions of current di...
Development of D -type fiber optic sensors for detection of refractive index variation in evanescent wave field /
Güleryüz, Burcu; Durucan, Caner; Aslan, Mustafa M.; Department of Metallurgical and Materials Engineering (2014)
The purpose of this study is to design, construct, develop and test D-type multimode fiber optic (F/O) sensors based on evanescent wave field sensing. A comprehensive work has been performed both theoretically and experimentally using a geometrical modification approach to improve the sensors response in different manners for detecting the refractive index (RI) variations and bio-molecular interactions in aqueous environment. In this study, the D-type F/O sensors performance was improved utilizing optical w...
Citation Formats
Z. Akahn, C. E. Acar, and N. G. Gençer, “Development of realistic head models for electromagnetic source imaging of the human brain,” 2002, Accessed: 00, 2020. [Online]. Available: