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Remote Volume Rendering Pipeline for mHealth Applications

机译:适用于mHealth应用程序的远程体绘制管线

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We introduce a novel remote volume rendering pipeline for medical visualization targeted for mHealth (mobile health) applications. The necessity of such a pipeline stems from the large size of the medical imaging data produced by current CT and MRI scanners with respect to the complexity of the volumetric rendering algorithms. For example, the resolution of typical CT Angiography (CTA) data easily reaches 512^3 voxels and can exceed 6 gigabytes in size by spanning over the time domain while capturing a beating heart. This explosion in data size makes data transfers to mobile devices challenging, and even when the transfer problem is resolved the rendering performance of the device still remains a bottleneck. To deal with this issue, we propose a thin-client architecture, where the entirety of the data resides on a remote server where the image is rendered and then streamed to the client mobile device. We utilize the display and interaction capabilities of the mobile device, while performing interactive volume rendering on a server capable of handling large datasets. Specifically, upon user interaction the volume is rendered on the server and encoded into an H.264 video stream. H.264 is ubiquitously hardware accelerated, resulting in faster compression and lower power requirements. The choice of low-latency CPU- and GPU-based encoders is particularly important in enabling the interactive nature of our system. We demonstrate a prototype of our framework using various medical datasets on commodity tablet devices.
机译:我们引入了一种新颖的远程体绘制管线,用于针对mHealth(移动健康)应用程序的医学可视化。就体积渲染算法的复杂性而言,这种管道的必要性源于当前的CT和MRI扫描仪产生的大量医学成像数据。例如,典型的CT血管造影(CTA)数据的分辨率很容易达到512 ^ 3体素,并且在捕获跳动的心脏时跨越时域,大小可以超过6 GB。数据大小的爆炸式增长使向移动设备的数据传输变得充满挑战,即使解决了传输问题,设备的渲染性能仍然是瓶颈。为了解决此问题,我们提出了瘦客户端体系结构,其中所有数据都驻留在远程服务器上,在该服务器上渲染图像,然后将其流式传输到客户端移动设备。我们利用移动设备的显示和交互功能,同时在能够处理大型数据集的服务器上执行交互式体积渲染。具体而言,在用户交互时,该卷将呈现在服务器上并编码为H.264视频流。 H.264在硬件上无处不在,从而加快了压缩速度并降低了功耗要求。基于CPU和GPU的低延迟编码器的选择对于实现系统的交互性特别重要。我们使用商品平板设备上的各种医疗数据集演示了我们框架的原型。

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