C13 Mr-based Stereotaxic Standard Brain Atlas Of The Lib chov Minipig

Abstract

<h3>Background</h3> In the TRACK TgHD minipig study, we aim to investigate characteristics of the transgenic Libéchov minipig as a model for HD. Annual MRI scans are performed, including anatomical, spectroscopic and diffusion weighted imaging (DWI) sequences. To be able to perform quantitative analysis like the fractional anisotropy (FA), a standard brain is needed as target for registration of imaging data. To date, no standard brain atlas for the Libéchov minipig exists. <h3>Aims</h3> To provide a stereotaxic standard brain atlas of the Libéchov minipig that serves primarily as target for registration of diffusion weighted MR images. <h3>Methods/techniques</h3> 36 T1 weighted, sagittal acquired MRI volumes of 32 Libéchov minipigs were transformed into a common coordinate system and skull-stripped with a BET algorithm. The extracted brains were coregistered with a 12 parameter affine registration within multiple iteration steps. To define the anatomical structures, an available MR-based statistical atlas of the Göttinger minipig brain was registered on the averaged standard brain and the different regions were carefully compared. Binary maps of 34 segmented brain structures were created. The T1 weighted images were acquired with a 3T Philips Achieva scanner. <h3>Results/outcome</h3> T1-weighted 3D volumes of brains of 32 minipigs have successfully been acquired. A standard brain template could be created as target for registration. The mapping of structural regions to create a brain atlas is still in progress and will first be presented at the EHDN meeting. <h3>Conclusions</h3> Most quantitative analysis methods for MR images require registration of the images to a standard volume. Such templates already exist for several species, but not for the Libéchov minipig. The resulting brain template serves as registration target for diffusion weighted MR images and is the first step in creating a Libéchov minipig FA template for quantitative analysis of cross sectional and longitudinal DWI data. This study was supported by a grant of the CHDI Foundation.