CRII:SCH:A Generative Deep Learning (GDL) based Platform for Super-resolution, Virtual-Pathological Visualization of Coronary Images

冠状动脉疾病（CAD）已经影响了超过28亿美元的市场，每年进行约1，000，000次治疗程序。现有的CAD治疗指南受到有限的空间分辨率和缺乏实时详细的病理识别。本计画旨在探讨冠状动脉影像病理性超解析可视化的新计算技术。该项目如果成功，将有助于为心血管疾病的治疗提供新一代的临床指导，心血管疾病目前是美国人类死亡的主要原因。从技术上讲，将多域图像表示编码为单域图像采集的方法可以有益于其他领域，例如多摄像机监视监视、多模态生物医学成像等，大大降低了硬件成本和医疗劳动力。该项目的教育计划强调旨在指导高级设计，丰富深度学习课程的课程设置，并促进多元文化工程项目中少数民族学生的推广活动。该项目旨在开发一种数据驱动的方法，使用离线数据和训练过程，无需任何硬件修改，即可生成以前无法获得或只能在体外获得的新信息。该项目将开发生成式深度学习算法，通过在离线训练期间聚合来自高分辨率OCT图像和组织学显微图像的图像信息，为低分辨率光学相干断层扫描（OCT）图像生成额外信息。该项目将研究通过体积生成对抗网络（GAN）提高OCT的分辨率，同时保持快速扫描速率以实现超分辨率。该项目将开发一种新的不成对训练方案，通过使用基于GAN的图像转换框架将OCT图像映射到组织病理学图像。该方法将使用OCT图像和组织病理学图像的客观和主观分析进行确认。该项目预计将在计算机科学和生物医学信息学方面产生学术成果。该项目将为跨平台体积超分辨率提供深度学习解决方案，并为解决跨模态图像翻译提供生成学习方法。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Coronary artery disease (CAD) has been influencing a market over $2.8 billion with roughly 1,000,000 treatment procedures performed annually. Existing guidance for CAD treatment suffers from limited spatial resolution and lacks real-time detailed pathological identification. This project is to investigate novel computational techniques for pathological, super-resolution visualization of coronary images. The project, if successful, will contribute towards a new generation of clinical guidance for the treatment of cardiovascular disease, which is currently the leading cause of human deaths in the United States. Technically, the method of encoding multi-domain image representations into a single-domain image acquisition could benefit other fields, such as multi-camera surveillance monitoring, multimodal biomedical imaging, etc., in terms of greatly reducing hardware cost and medical labor. The educational plan in this project emphasizes activities designed to guide senior designs, enrich curriculum in deep learning courses, and facilitate outreach for minority students in multicultural engineering programs.This project aims to develop a data-driven approach to use off-line data and training process, without any hardware modifications, to generatively produce new information that could not be acquired previously or can only be obtained ex vivo. This project will develop generative deep learning algorithms to produce additional information for low-resolution optical coherence tomography (OCT) images by aggregating image information from high-resolution OCT images and histological microscopic images during off-line training. This project will investigate on improving the resolution of OCT while maintaining fast scanning rate via a volumetric generative adversarial network (GAN) for super-resolution. This project will develop a novel unpaired training scheme to map OCT image to a histopathology image by using a GAN-based image translation framework. The approach will be validated using both objective and subjective analysis on OCT images and histopathology images. This project is expected to generate academic outcomes in both computer science and biomedical informatics. This project will provide a deep learning solution for cross-platform volumetric super-resolution and a generative learning approach to address cross-modality image translation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.