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，其提供替代信息学基础设施，以提供在台式或移动的设备上开发和阅读高质量多维诊断图像所需的超级计算机能力。这种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成像（CTC）。本提案的目标是开发和验证移动的HPCC-虚拟结肠镜检查决策支持系统（HPCC-VC）的临床受益，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）的组合将允许可视化结肠的整个粘膜表面，而不会出现伪影。具体而言，我们假设移动的HPCC-VC系统将提高非泻药CTC（ncCTC）图像的电子净化质量，将以比传统方法快100倍的速度提供图像，并将提高读片员在移动的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，HPCC平台将使用基于CPU集群的超级计算和并行图像处理库。然后，将开发一种hpEC方案，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中结肠病变的高性能检测。这些方案将被集成到HPCC平台上的移动的HPCC-VC系统中。在开发计算EC和CAD方案方面的必要初步研究显示出希望; 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片人性能研究，以确定通过HPCC平台分析和交付的图像的临床应用和受益。 HPCC-VC的成功开发将证明该平台在改善诊断成像和促进患者高度可接受的准确、高通量结肠癌筛查方面的临床益处。从长远来看，广泛采用和使用HPCC-VC系统将有助于早期和准确的诊断，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.