Automated brain extraction and immersive exploration of its layers in virtual reality for the rhesus macaque MRI data sets. (28th May 2018) - Luo, Yanlin; Gao, Bin; Deng, Yiyi; Zhu, Xiaoming; Jiang, Tianzi; Zhao, Xudong; Yang, Zhengyi
Abstract: As we know, the rhesus macaque as a nonhuman primate is quite similar to a human being in genetics. Moreover, it has become essential in animal model anatomy and physiology in many modern medicine research such as the cardiovascular and cerebrovascular diseases in the recent years. This paper describes a pipeline from the raw rhesus macaque brain magnetic resonance imaging data to intuitive 3D inspection of its layers. Brain extraction is an initial step for subsequent analyses, but most of the existing methods so far are designed for human brain, which does not work well with the rhesus macaque. Firstly, we propose a reliable and efficient method to extract the brain from magnetic resonance imaging data sets based on dividing the brain into blocks. Then, we design a trapezoid opacity transfer function based on Compute Unified Device Architecture (CUDA)‐based real‐time volume ray casting, which is dedicated to volume rendering to make the inspection more intuitive. Besides, for an immersive exploration in virtual reality benefits understanding the volumetric data sets, so we also design a layer filter for the segmented rhesus macaque brain data sets, which facilitates the inspection of the interior in Region of Interest (ROI) by an intuitive bimanual interaction via a Leap Motion sensor. Our experiments prove usability and efficiency. Abstract : We firstly extract the brain from magnetic resonance imaging data sets based on our divide‐and‐conquer strategy by dividing the head image into blocks with similar image contrast. Then design a trapezoid opacity transfer function for CUDA‐based real‐time volume visualization of the segmented rhesus macaque brain data sets. Finally we take immersive exploration of its inner layers in an immersive virtual environment by an intuitive bimanual interaction via a Leap Motion sensor.