CAREER: Developing Algorithms for Object-Adaptive Super-Resolution in Biomedical Imaging

职业：开发生物医学成像中对象自适应超分辨率算法

• 批准号: 2239810
• 负责人: Yu Gan
• 金额: $ 60万
• 依托单位: Stevens Institute of Technology
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2028-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2239810&HistoricalAwards=false
• 关键词: CAREER; Developing; Algorithms; Object; Adaptive

Advanced biomedical imaging technology has revolutionized diagnosis and treatment by providing structural and functional details. Spatial resolution of biomedical images, however, sometimes do not suffice for specific applications due to constraints of image acquisition time. Conventional software-based improvement bears high costs in computation and visualization, opening a niche to optimize the framework towards super-resolution at reasonable costs. It aligns with NSF’s mission to promote the process of computer science and to advance the national health. This project is to investigate novel algorithm development to adaptively improve digital resolution and minimize the cost of computation in super-resolution process. Technically, this method combines the effort of object detection and super-resolution to bring a generalizable tool to potentially benefit multiple biomedical imaging modalities, such as optical coherence tomography (OCT), histological microscopy, confocal images, MRI, ultrasound, etc. The educational emphasizes activities to broaden the participation of underrepresented groups in biomedical pursuits.This project aims to develop intelligent object-adaptive super-resolution algorithms to improve resolutions of biomedical images in a robust, efficient, and generalizable manner. This project will develop robust object detection neural network to identify regions to be super-resolved. A scale factor will be determined for adaptive super-resolution. This project will investigate on computationally efficient algorithms to super-resolve biomedical images to multiple scale factors during a complex-valued image reconstruction process. This project will also develop a transferrable framework such that the super-resolution technology developed in one image modality can be adapted into the super-resolution technology developed by a different imaging modality. The approaches will be validated using OCT data and the domain adaption will be validated by transferring from OCT domain to histopathological domain. The research outcome will also result in artificial intelligence-based educational materials and software to reduce the need of biomedical facilities that are conventionally required but not cost-effective to underrepresented groups. In addition, this project includes outreach activities to promote biomedical participation in regions with limited access to biomedical resources and a new model to mentor a diverse and inclusive team and create motivation to the next generation of researchers.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.

先进的生物医学成像技术通过提供结构和功能细节，彻底改变了诊断和治疗。然而，由于图像采集时间的限制，生物医学图像的空间分辨率有时不足以满足特定的应用。传统的基于软件的改进在计算和可视化方面成本很高，这为以合理的成本优化超分辨率框架开辟了一个利基市场。它符合NSF的使命，以促进计算机科学的进程和促进国民健康。本计画旨在探讨新的演算法发展，以自适应地提升数位解析度，并减少超解析处理的运算量。从技术上讲，这种方法结合了目标检测和超分辨率的努力，带来了一种可推广的工具，可能有利于多种生物医学成像模式，如光学相干断层扫描（OCT），组织学显微镜，共聚焦图像，MRI，超声，该项目旨在开发智能对象自适应超级计算机，分辨率算法，以稳健、高效和可推广的方式提高生物医学图像的分辨率。该项目将开发强大的目标检测神经网络，以识别待超分辨率的区域。将确定用于自适应超分辨率的比例因子。本计画将研究在复值影像重建过程中，将生物医学影像超解析至多重比例因子的高效率演算法。 该项目还将开发一个可转移的框架，以便在一种成像模式下开发的超分辨率技术可以适应不同成像模式开发的超分辨率技术。将使用OCT数据对这些方法进行验证，并将通过从OCT域转移到组织病理学域来验证域自适应。研究成果还将产生基于人工智能的教育材料和软件，以减少对传统上需要但对代表性不足的群体不具成本效益的生物医学设施的需求。此外，该项目还包括推广活动，以促进生物医学资源有限地区的生物医学参与，以及指导多元化和包容性团队并为下一代研究人员创造动力的新模式。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。