Cloud-computer-aided diagnostic imaging decision support system

描述（由申请人提供）：高性能云计算（HPCC）是高性能计算（HPC）与云计算的集成，它提供了一种替代的信息学基础设施，以提供在桌面或移动设备上开发和读取高质量、多维诊断图像所需的超级计算机能力。这样的基础设施将促进高通量癌症筛查的使用，如虚拟结肠镜检查（VC），也称为计算机断层扫描结肠镜检查（CTC）。本提案的目标是开发和验证移动hpc -虚拟结肠镜决策支持系统（hpc - vc）的临床效益，该系统集成了新型高性能电子清洗和计算机辅助检测方案。高性能电子清洗（hpEC）与高性能计算机辅助检测（hpCAD）的结合将使结肠整个粘膜表面的可视化无伪影。具体来说，我们假设移动hpc - vc系统将提高非宣泄性CTC （ncCTC）图像的电子清洗质量，将比传统方法快100倍地提供图像，并将提高阅读器在移动高分辨率显示设备上分析的图像中检测结肠病变的性能。为了实现这些目标，将利用基于cpu集群的超级计算和并行图像处理库建立HPCC平台。然后，hpEC方案将开发有效地去除ncCTC图像中的残留粪便物质。同时，将开发高分辨率hpCAD方案，以支持ncCTC结肠病变的高性能检测。这些方案将被集成到HPCC平台上的移动HPCC- vc系统中。对计算机电子商务和计算机辅助设计方案进行必要的初步研究显示了前景；HPCC平台的开发和部署将提高结肠癌筛查的高性能、多维成像的质量、速度和实用性。将进行一项全面的阅读器性能研究，以确定通过HPCC平台分析和传递的图像的临床应用和益处。hpc - vc的成功开发将证明该平台在改善诊断成像和促进准确、高通量结肠癌筛查方面的临床益处，这是患者高度可接受的。从长远来看，广泛采用和使用hpc - vc系统将有助于早期和准确诊断，从而降低结肠癌的死亡率。

DESCRIPTION (provided by applicant): High-performance cloud computing (HPCC) is an integration of high-performance computing (HPC) with cloud computing that provides an alternative informatics infrastructure to deliver the supercomputer power needed for developing and reading high quality, multidimensional diagnostic images on desktop or mobile devices. Such infrastructure would advance the use of high-throughput cancer screening like virtual colonoscopy (VC), also known as computed tomography colonography (CTC). The goals of this proposal are to develop and validate the clinical benefits of a mobile HPCC-Virtual Colonoscopy decision support system (HPCC-VC) that integrates novel high-performance electronic cleansing and computer-aided detection schemes. The combination of high performance electronic cleansing (hpEC) with high performance computer aided detection (hpCAD) will allow visualization of the entire mucosal surface of the colon without artifact. Specifically, we hypothesize that the mobile HPCC-VC system will improve the quality of electronic cleansing of non-cathartic CTC (ncCTC) images, will deliver images 100 times faster than conventional approaches, and will improve reader performance in the detection of colonic lesions in the images analyzed on mobile high-resolution display devices. To achieve these goals, an HPCC platform will be established by use of a CPU-cluster-based supercomputing and parallel image processing libraries. Then, an hpEC scheme will be developed that effectively removes the residual fecal materials in ncCTC images. In parallel, a high-resolution hpCAD scheme will be developed to support high-performance detection of colonic lesions in ncCTC. These schemes will be integrated into a mobile HPCC-VC system on the HPCC platform. Necessary preliminary studies in the development of computed EC and CAD schemes show promise; and the development and deployment of the HPCC platform will advance the quality, speed, and utility of high performance, multidimensional imaging for colon cancer screening. A comprehensive reader performance study will be conducted to determine the clinical application and benefit of images analyzed and delivered through an HPCC platform. Successful development of HPCC-VC will demonstrate the clinical benefit of the platform for improved diagnostic imaging and facilitation of accurate, high-throughput colon cancer screening that is highly acceptable to patients. In the longer term, broad adoption and use of the HPCC-VC system will facilitate early and accurate diagnoses, and thus reduce mortality from colon cancer.