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首页> 外文期刊>Computer Animation and Virtual Worlds >Automated brain extraction and immersive exploration ofits layers in virtual reality for the rhesus macaque MRI data sets
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Automated brain extraction and immersive exploration ofits layers in virtual reality for the rhesus macaque MRI data sets

机译:恒河猴MRI数据集在虚拟现实中的自动大脑提取和沉浸式探索 r nits层

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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.
机译:众所周知,猕猴作为非人类的灵长类动物与人类在遗传学上非常相似。此外,近年来,在许多现代医学研究中,例如在心血管和脑血管疾病中,它已经在动物模型解剖学和生理学中变得至关重要。本文描述了从原始恒河猴猕猴磁共振成像数据到其层的直观3D检查的管道。脑部提取是进行后续分析的第一步,但是到目前为止,大多数现有方法都是针对人脑设计的,不适用于恒河猴。首先,我们提出了一种可靠且有效的方法,该方法基于将大脑分为多个块,从磁共振成像数据集中提取大脑。然后,我们基于基于计算统一设备体系结构(CUDA)的实时体射线投射设计梯形不透明度传递函数,该函数专用于体绘制以使检查更加直观。此外,为了进行虚拟现实的沉浸式探索,有益于了解体积数据集,因此,我们还为分段的猕猴大脑数据集设计了一个层过滤器,该层过滤器通过直观的双手操作方便了对感兴趣区域(ROI)内部的检查。通过Leap Motion传感器进行交互。我们的实验证明了可用性和效率。

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