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来彻底改变诊断和治疗。但是，生物医学图像的空间分辨率有时由于图像采集时间的限制而无法为特定的应用提供。传统的基于软件的改进在计算和可视化方面具有很高的成本，以合理的成本开放了一个利基市场，以优化框架朝着超分辨率进行优化。它符合NSF的使命，旨在促进计算机科学的过程并促进国家健康。该项目旨在研究新型算法开发，以适应性地改善数字分辨率并最大程度地减少超分辨率过程中的计算成本。从技术上讲，这种方法结合了对象检测和超分辨率的努力，以携带可推广的工具，使可能受益多种生物医学成像模式，例如光学相干性层析成像（OCT），组织学显微镜，组织学显微镜，MRI，超声，超声等。教育强调的活动以扩大未成年人的范围，以拓宽了对对位置的影响，以开发了对对象的无关紧要的对对象的参与。超分辨率算法以稳健，高效且可推广的方式改善生物医学图像的分辨率。该项目将开发可靠的对象检测神经网络，以确定要超级分辨的区域。将确定适应性超分辨率的量表因子。该项目将在计算有效的算法上调查在复杂评估的图像重建过程中，以将生物医学图像与多个量表因子进行研究。该项目还将开发一个可转让的框架，以便可以将一种图像模式开发的超分辨率技术改编成不同成像方式开发的超分辨率技术。这些方法将使用OCT数据验证，并且将通过从OCT域转移到组织病理学域来验证域的适应性。研究结果还将导致基于人工智能的教育材料和软件，以减少通常需要但对代表性不足的群体成本效益的生物医学设施的需求。此外，该项目还包括宣传活动，以促进在获得生物医学资源有限的地区的生物医学参与，并为心理多样性和包容性团队提供新的模型，并为下一代研究人员创造动力。该奖项反映了NSF的法定任务，并通过使用该基金会的智力功能和广泛的影响来评估NSF的法定任务，并被认为是诚实的支持。