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

先进的生物医学成像技术提供了结构和功能的细节，使诊断和治疗发生了革命性的变化。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能满足特定的应用。传统的基于软件的改进在计算和可视化方面承担着高昂的成本，为以合理的成本优化框架走向超分辨率开辟了一个利基市场。它与NSF促进计算机科学进程和促进国民健康的使命是一致的。该项目旨在研究新的算法开发，以自适应地提高数字分辨率，并将超分辨率过程中的计算成本降至最低。在技术上，这种方法结合了目标检测和超分辨率的努力，带来了一个可推广的工具，潜在地有利于多种生物医学成像模式，如光学相干层析成像(OCT)、组织显微镜、共焦图像、磁共振成像、超声等。教育强调扩大未被代表的群体参与生物医学追求的活动。本项目旨在开发智能的对象自适应超分辨率算法，以稳健、高效和可推广的方式提高生物医学图像的分辨率。该项目将开发稳健的目标检测神经网络来识别待超分辨的区域。将确定自适应超分辨率的比例因子。该项目将研究在复值图像重建过程中将生物医学图像超分辨到多个尺度因子的计算效率算法。该项目还将开发一个可转让的框架，使在一种成像方式下开发的超分辨率技术可以适应由另一种成像方式开发的超分辨率技术。这些方法将使用OCT数据进行验证，并通过从OCT域转移到组织病理学域来验证域的适应性。研究成果还将产生基于人工智能的教育材料和软件，以减少对生物医疗设施的需求，这些设施是传统上所需的，但对代表性不足的群体来说成本效益不高。此外，该项目还包括在生物医学资源有限的地区促进生物医学参与的推广活动，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智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.