Yucatan minipigs have become increasingly common animal models in neuroscience where recent studies, investigating blast-induced traumatic brain injury, stroke, and glioblastoma, aim to uncover brain injury mechanisms [1-3]. Magnetic Resonance Imaging (MRI) has the potential to validate and optimize unknown parameters in controlled populations. The key to group-level MRI analysis within a species is to align (or register) subject scans to the same volumetric space using a brain template. However, large animal brain templates are lacking, which limits the use of MRI as an effective research tool to study group effects. The objective of this study was to create an MRI-based Yucatan minipig brain template allowing for uniform group-level analysis of this animal model in a standard volumetric space to characterize brain mechanisms. To do this, 5-7 month old, male Yucatan minipigs were scanned using a 3 Tesla whole-body scanner (Siemens AG, Erlangen) in accordance with IACUC. T1-weighted anatomical volumes (resolution = 1×1×1 mm3; TR = 2300 ms; TE= 2.89 ms; TI = 900 ms; FOV = 256 mm2 ; FA = 8 deg) were collected with a three-dimensional magnetization prepared rapid acquisition gradient echo (MPRAGE) pulse sequence [4]. The volumes were preprocessed, co-registered, and averaged using both linear and non-linear registration algorithms in AFNI [5] to create four templates (n=58): linear brain, non-linear brain, linear head, and non-linear head. To validate the templates, tissue probability maps (TPMs) and variance maps were created, and landmark variation was measured. TPMs computed in FSL [6] and AFNI show enhanced tissue probability and contrast in the non-linear template. Additionally, variance maps showed a more uniform spatial variance in the non-linear template compared to the linear. Registration variation within the brain template was within 1.5 mm and displayed improved landmark variation in the non-linear brain template. External evaluation subjects (n=12), not included in the template, were registered to the four templates to assess functionality. The results indicate that the developed templates provide acceptable registration accuracy to enable population comparisons. With these templates, researchers will be able to use MRI as a tool to further neurological discovery and collaborate in a uniform space. / M.S. / Magnetic resonance imaging (MRI) is commonly used in neuroscience as a non-invasive diagnostic tool with the potential to reveal unknown brain injury mechanisms. MRI is particularly useful in large animal models to validate and optimize unknown parameters in controlled populations. The key to group-level MRI analysis within a species is to align (or register) subject scans to the same volumetric space using a brain template. However, large animal brain templates are lacking, which limits the use of MRI as an effective research tool to study group effects. The objective of this study was to create an MRI-based Yucatan minipig brain template allowing for uniform group-level analysis of this animal model in a standard volumetric space to better characterize brain mechanisms. The neuroanatomy of the Yucatan minipig, which is characterized by an increased brain size and gyrencephalic intricacies similar to humans, has made it an increasingly common animal model in neuroscience. Linear and non-linear registration methods were performed in Analysis of Functional NeuroImages (AFNI) software to create both brain and head templates for 5-7 month old, male Yucatan minipigs (n=58). This study was validated looking at template variance, tissue probability maps (TPMs) of segmented grey matter, white matter, and cerebrospinal fluid, and landmark variation. The results indicate that the developed templates provide acceptable registration accuracy to enable population comparisons. With these templates, researchers will be able to use MRI as a tool to further neurological discovery and collaborate in a uniform space.
| Identifer | oai:union.ndltd.org:VTETD/oai:vtechworks.lib.vt.edu:10919/89642 |
| Date | 05 April 2019 |
| Creators | Norris, Caroline N. |
| Contributors | Biomedical Engineering and Mechanics, VandeVord, Pamela J., Friedlander, Michael J., McNeil, Elizabeth M., LaConte, Stephen M. |
| Publisher | Virginia Tech |
| Source Sets | Virginia Tech Theses and Dissertation |
| Language | en_US |
| Detected Language | English |
| Type | Thesis |
| Format | ETD, application/pdf |
| Rights | Creative Commons Attribution-NonCommercial-NoDerivs 3.0 United States, http://creativecommons.org/licenses/by-nc-nd/3.0/us/ |
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