Project Grant R01EB036541

Award Date 8/1/25
Completion Date 5/31/29
Dollars Obligated $641K
Federal Grant Program
93.286
Assistance Type
Project Grant
Place of Performance
Minnesota, USA
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This federal Project Grant award, valued at $594,891 and provided by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under CFDA 93.286 "Discovery and Applied Research for Technological Innovations to Improve Human Health", aims to develop deep-learning anthropomorphic model observers (AMOs) as a substitute for human observers in studies assessing image quality for clinical diagnostic performance. The key objectives are to create AMOs that can accurately...
This $536,954 Project Grant awarded by the National Institute of Biomedical Imaging and Bioengineering (NIBIB) under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286) will fund research and development of a novel ultra-high-spatial resolution computed tomography (UHR-CT) system using photon-counting detectors and multiple x-ray focal spots. The research aims to overcome limitations of current high-resolution CT systems by developing...
This Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the Discovery and Applied Research for Technological Innovations to Improve Human Health program (CFDA 93.286), provides $616,033 to the University of California, Davis to develop and validate a PET-enabled dual-energy CT (DECT) imaging method. The goal is to create a technique that can derive a high-energy gamma-ray CT (GCT) image from standard PET/CT emission data, and combine it with...
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This Project Grant award from the National Institute of Biomedical Imaging and Bioengineering (NIBIB), under the CFDA 93.286 "Discovery and Applied Research for Technological Innovations to Improve Human Health" program, provides $641,063 to develop a dynamic, customizable, vendor-neutral patient projection data library and virtual imaging trial (VIT) software platform (DICOM-CTPD-VIT). This platform will enable the generation of a diverse range of imaging conditions, including radiation dose, reconstruction parameters, and lesion characteristics, to facilitate automated image quality and diagnostic performance evaluation of deep learning reconstruction and noise reduction algorithms in CT imaging. The goal is to create a resource that allows for effective development and safe implementation of these AI-based algorithms in clinical practice, while also having potential to generate an unlimited number of cases with ground truth for training and evaluating other AI-based diagnostic tools. The award has a project period from Aug 1, 2025 to May 31, 2029.

Generated 8/5/25, 6:25 AM