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

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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.