Project Grant R01EB036992
- This Project Grant award, funded 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), supports the development of a dynamic, customizable, and vendor-neutral patient projection data library and software platform for training and evaluating AI-based algorithms in CT imaging. The $641,063 award will enable the creation of a diverse range of simulated patient...
- The federal Project Grant award of $1,148,652 was made on April 1, 2025 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). The grant supports research and development of an ultra-high spatial resolution photon-counting computed tomography (CT) system with multiple focal spots. The key products to be delivered include: Developing system models and...
- This Project Grant award of $616,033 from the National Institute of Biomedical Imaging and Bioengineering (CFDA 93.286 - Discovery and Applied Research for Technological Innovations to Improve Human Health) supports the development and application of a PET-enabled dual-energy CT (DECT) imaging method at the University of California, Davis. The key objectives of this project are to develop a DECT imaging approach that leverages the inherent annihilation-photon attenuation properties of a PET...
- This federal Project Grant award, funded 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), aims to develop and validate diffusion-model enabled computational observers for evaluating the performance of deep learning CT image reconstruction and post-processing algorithms. The $428,152 award, which runs from September 1, 2025 to August 31, 2027, will...
- This federal Project Grant award of $695,814 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) supports research to develop artificial intelligence (AI) technologies that enable online adaptive radiation therapy for proton therapy. The key products and services to be delivered through this grant include: 1) Developing an asymmetric autoencoder network...
- This Project Grant award of $742,261.00 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), aims to advance head computed tomography (CT) interpretation through three interconnected approaches focused on knowledge representation, image-based report verification, and longitudinal analysis of sequential scans. The project, led by President and Fellows of...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $594,891 Project Grant under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" (CFDA 93.286) program to the Illinois Institute of Technology (IIT). The grant aims to develop deep-learning anthropomorphic model observers (AMO) as a substitute for human observers in studies evaluating image quality and diagnostic performance. The project will create annotated image...
- This $1,434,445.00 Project Grant award from the National Institutes of Health's Trans-NIH Research Support program (CFDA 93.310) supports the University of Southern California (USC) in developing new deep learning-based architectures, algorithms and training mechanisms to address key challenges in magnetic resonance imaging (MRI) reconstruction. The project aims to create a robust, reliable and trustworthy toolkit for reducing MRI acquisition time, enabling high-quality reconstruction with...
- The National Institute of Biomedical Imaging and Bioengineering (NIBIB) awarded a $502,841 Project Grant under the "Discovery and Applied Research for Technological Innovations to Improve Human Health" (CFDA 93.286) program to The Regents of the University of California, operating as Lawrence Berkeley National Laboratory (LBNL). The funding will support research and development of a novel detector readout concept for monolithic scintillation detectors that enables direct digitization...
- This federal Project Grant award, titled "Automated Multi-Timepoint CT Analysis for Enhanced Metastatic Disease Evaluation in Colorectal Cancer: An Anatomy-Aware Vision-Language AI Approach," was provided 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). The $718,456 award, with a performance period from July 1, 2025 to May 31, 2029, supports...
This Project Grant award, funded 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), supports the development and validation of a novel technique called Synthetic Nephrographic Phase Images in CT (SNICT). The SNICT technique uses deep learning image processing to reconstruct nephrographic phase images from non-contrast and urographic phase CT data, effectively reducing a 3-phase CT urography study to 2 phases and decreasing radiation dose by 33%. The $1,475,636 award will support further improving the SNICT technique, including developing a diffusion-based deep learning model to enhance image quality, and adapting SNICT for dual-energy CT urography which could provide a 2X reduction in overall exam time and 66% radiation dose reduction. The research team will also perform a rigorous clinical validation of the SNICT technique to assess its diagnostic accuracy. This award aims to transform the standard CT urography protocol through innovative image synthesis methods, improving both efficiency and patient safety.
Mod # | Description | ReasonForModification | Federal Obligation | Date |
|---|---|---|---|---|
| Not listed | $1.5m | 7/29/25 |