Conference PaperPDF Available

Construction and segmentation of pediatric head tissue atlases for electrical head modeling

Authors:
Construction and segmentation of pediatric head tissue atlases for
electrical head modeling
Presented During: Poster Session
Monday, June 26, 2017: 12:45 PM - 02:45 PM
Presented During: Poster Session
Monday, June 26, 2017: 12:45 PM - 02:45 PM
Stand-By Time
Monday, June 26, 2017: 12:45 PM - 2:45 PM
Submission No:
1648
Submission Type:
Abstract Submission
On Display:
Monday, June 26 & Tuesday, June 27
Authors:
David Hammond1, Nick Price2, Sergei Turovets2
Institutions:
1Oregon Institute of Technology - Wilsonville, Wilsonville, OR, 2Electrical Geodesics, Inc, Eugene, OR
First Author:
David Hammond - Lecture Information | Contact Me
Oregon Institute of Technology - Wilsonville
Wilsonville, OR
Introduction:
Accurate population models of head tissue geometry and conductivity are essential for accurate source localization in
electroencephalography (EEG) and precise targeting in transcranial electrical stimulation (TES). The work described
here is part of a larger effort to build a comprehensive set of age-specific pediatric head models, based on merging
multiple imaging modalities (structural MRI, CT, diffusion-weighted MRI, Electrical Impedance Tomography (EIT)) to
enable creation of atlases of soft tissues, skull mineralization density, and anisotropic electrical conductivity of white
matter (supported by NIH grant R44 MH106421).
Here we describe our construction of average T1 MRI atlas images, and compare two approaches for segmentation.
Using images obtained from the NIH Pediatric MR Database (Evans 2006, Almli et al 2007), we calculated a series of
atlases in 4 month age increments from 0 to 1 year, from 1 to 2 years, then in 2 year age increments (2-4 years, 4-6
years, etc.), from ages 2 to 18.
Our ultimate goal being the production of head conductivity models, we are especially interested in accurate
segmentation of these atlases; electrical head models (i.e. lead field matrices) can then be computed based on
assigning electrical conductivities for different tissue types consistent with known values from the literature (e.g.
Salman 2005). We use BrainK (Li et al, 2016), which computes a segmentation based on a single MRI image, using
internal skull templates for skull segmentation. For an atlas computed from a collection of individual images, such a
tool can be used in two distinct ways: a) directly segmenting the average atlas ("direct atlas segmentation"), or b)
segmenting each individual MRI image, and then aggregating the individual segmentations ("aggregate atlas
segmentation").
Methods:
Our average MRI atlases were based on a set of 736 T1-weighted MRI images obtained from the Pediatric MRI Data
Repository, created as part of the NIH MRI Study of Normal Brain Development
(https://pediatricmri.nih.gov/nihpd/info/index.html). For each age group, we computed atlas images by iterating the
following : 1) Given a current template, compute transformations (from a set of transformations allowed at this
iteration) registering each individual image onto the the template; 2) Average these transformed images to give the
template for the next iteration. We organized our calculations in stages, the first allowing only rigid rotations, the
second only affine transformations, and the third allowing nonlinear transformations. All transformations were
computed using the ANTs software (Tustison et al, 2014), using symmetric normalization (SyN) based on the
neighborhood cross correlation (CC) metric.
As an example, for the 12-14 age group (63 original images) we computed the aggregate segmentation by
segmenting the original images, transforming these segmentations into the atlas space using the same nonlinear
transformations computed during the atlas construction, then aggregating them into a single "maximum probability"
aggregate segmentation by "voting": i.e. by choosing at each voxel the tissue type that was present most often
amongst the individual transformed segmentations.
Results:
Axial slices of selected atlases are shown in Fig 1. Fig 2 shows axial slices of the aggregate atlas segmentation and
of the direct atlas segmentation, and two representative individual segmentations (out of 63 total) mapped onto the
atlas space.
·Figure 1, Selected average atlas images
·Figure 2, Selected individual segmentation images, and atlas segmentation images (aggregate and direct)
Conclusions:
The direct atlas segmentation and the aggregate atlas segmentation are much smoother than the individual
segmentations. Both are quite similar, and give reasonable segmented images. For the ultimate purpose of computing
electrical head models, the probability maps (i.e. images with probabilities of each tissue type) can be used to
interpolate tissue conductivities in a manner consistent with the inferred uncertainty in the tissue segmentation, an
advantage not available from the direct segmentation method.
Imaging Methods:
Anatomical MRI
Informatics:
Brain Atlases 1
Modeling and Analysis Methods:
Image Registration and Computational Anatomy
Segmentation and Parcellation 2
Poster Session:
Poster Session - Monday
Keywords:
Atlasing
MRI
Segmentation
STRUCTURAL MRI
1|2Indicates the priority used for review
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Yes
I would be willing to discuss my abstract with members of the press should my abstract be marked
newsworthy:
Yes
Please indicate below if your study was a "resting state" or "task-activation” study.
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distribute the presentation in any format, including video, audio print and electronic text through OHBM
OnDemand, social media channels or other electronic media and on the OHBM website.
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Internal Review Board (IRB) or Animal Use and Care Committee (AUCC) Approval. Please indicate approval
below. Please note: Failure to have IRB or AUCC approval, if applicable will lead to automatic rejection of
abstract.
Not applicable
Please indicate which methods were used in your research:
Structural MRI
For human MRI, what field strength scanner do you use?
1.5T
Which processing packages did you use for your study?
Other, Please list - ANTs, BrainK
Provide references in author date format
C.R. Almli et al, "The NIH MRI study of normal brain development
(Objective-2): Newborns, infants, toddlers, and preschoolers",
NeuroImage, Volume 35, Issue 1, March 2007, Pages 308-325, ISSN
1053-8119, http://dx.doi.org/10.1016/j.neuroimage.2006.08.058
A.C. Evans (2006). "The NIH MRI study of normal brain development". NeuroImage 30 (2006) 184-202
Kai Li et al, "BrainK for Structural Image Processing: Creating Electrical Models of the Human Head", Computational
Intelligence and Neuroscience, vol. 2016, Article ID 1349851, 25 pages, 2016. doi:10.1155/2016/1349851
N. J. Tustison et al, "Large-scale evaluation of ANTs and FreeSurfer cortical thickness measurements", NeuroImage,
Volume 99, 1 October 2014, Pages 166-179, ISSN 1053-8119, http://dx.doi.org/10.1016/j.neuroimage.2014.05.044.
A. Salman et al (2005), "Computational modeling of human head conductivity", Proceedings of
the 5th International Conference on Computational Science, pp 631-638

Supplementary resource (1)

Article
Full-text available
BrainK is a set of automated procedures for characterizing the tissues of the human head from MRI, CT, and photogrammetry images. The tissue segmentation and cortical surface extraction support the primary goal of modeling the propagation of electrical currents through head tissues with a finite difference model (FDM) or finite element model (FEM) created from the BrainK geometries. The electrical head model is necessary for accurate source localization of dense array electroencephalographic (dEEG) measures from head surface electrodes. It is also necessary for accurate targeting of cerebral structures with transcranial current injection from those surface electrodes. BrainK must achieve five major tasks: image segmentation, registration of the MRI, CT, and sensor photogrammetry images, cortical surface reconstruction, dipole tessellation of the cortical surface, and Talairach transformation. We describe the approach to each task, and we compare the accuracies for the key tasks of tissue segmentation and cortical surface extraction in relation to existing research tools (FreeSurfer, FSL, SPM, and BrainVisa). BrainK achieves good accuracy with minimal or no user intervention, it deals well with poor quality MR images and tissue abnormalities, and it provides improved computational efficiency over existing research packages.
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The computational environment for estimation of unknown regional electrical conductivities of the human head, based on reali stic geometry from seg- mented MRI up to2563 resolution, is described. A finite difference alternating d i- rection implicit (ADI) algorithm, parallelized using OpenMP, is used to solve the forward problem describing the electrical field distributi on throughout the head given known electrical sources. A simplex search in the multi-dimensional para- meter space of tissue conductivities is conducted in parall el using a distributed system of heterogeneous computational resources. The theoretical and computa- tional formulation of the problem is presented. Results from test studies are pro- vided, comparing retrieved conductivities to known solutions from simulation. Performance statistics are also given showing both the scaling of the forward problem and the performance dynamics of the distributed search.
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