This $200,000 Project Grant from the National Science Foundation's Mathematical and Physical Sciences program (CFDA 47.049) will support the development of analytic and numerical methods for emerging tomography techniques at North Carolina State University from August 1, 2022 to July 31, 2025. Specifically, the awardee will address mathematical problems arising from multi-energy computed tomography (MECT) and Compton camera imaging (CCI) to advance these promising medical imaging methods. For...
This $162,510 Project Grant awarded by the National Science Foundation (NSF) Division of Mathematical Sciences will fund collaborative research at Tufts University to develop innovative, memory-efficient algorithms for high-dimensional imaging applications such as medical imaging, object inspection, and video surveillance. The key products will be accelerated reconstruction and compression techniques that can process large volumes of multi-dimensional imaging data in real-time while using...
The National Science Foundation awarded a $200,000 Project Grant to the University of Chicago under the Engineering (47.041) federal grant program. The grant will support the creation of two open-source, high-performance computing-enabled deep learning frameworks to significantly improve the reconstruction speed and quality of full waveform inversion-based ultrasound computed tomography. One framework will incorporate adjoint tomography theory into a generative adversarial network to...
This Project Grant award from the National Science Foundation (NSF) Mathematical and Physical Sciences program (CFDA 47.049) provides $183,980 to support research on the theoretical and algorithmic foundations of novel medical imaging modalities at the University of Arizona. The research aims to advance the underlying mathematics and image reconstruction algorithms for emerging modalities like photoacoustic tomography, magnetoacoustoelectric tomography, and ultrasound current density imaging....
The National Science Foundation (NSF) Division of Electrical, Communications and Cyber Systems awarded a $460,000 Project Grant to The Ohio State University for the period of September 15, 2023 to August 31, 2026. The grant is for a research project titled "HIGH ACCURACY IMAGE RECONSTRUCTION USING MICROWAVE MEASUREMENTS FROM BIO-MATCHED ANTENNAS AND DEEP LEARNING: A SYNTHESIZED X-RAY COMPUTED TOMOGRAPHY APPROACH." The key objectives are to: 1) develop a deep learning neural network...
The National Science Foundation Division of Computing and Communication Foundations awarded a $189,392 Project Grant to the University of Texas at Austin under the Computer and Information Science and Engineering federal grant program (CFDA 47.070). The five-year award will support the development of a machine learning framework for robust computational imaging system design. Key products include new deep generative physical models to separate modeling of the physical imaging system from...
This $255,187 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) supports research and development of ultra-high resolution cone-beam CT imaging technologies using pixelated scintillators. The grant aims to advance indirect-detection X-ray flat panel detectors (FPDs) by improving the performance of scintillator components that...
This five-year Project Grant from the National Science Foundation's Division of Information and Intelligent Systems and Computer and Information Science and Engineering program (CFDA 47.070) provides $330,293 to Texas A&M Engineering Experiment Station (doing business as Tees) to develop novel methods for sparse sampling and reconstruction in computer graphics rendering. The award seeks to dramatically reduce the number of photons required to generate high-quality images through importance...
This $275,000 Project Grant awarded by the National Science Foundation's (NSF) Division of Mathematical Sciences supports the development of novel approaches to solving inverse problems. The goal is to enhance the accuracy and efficiency of computational methods used in critical applications like electrical impedance tomography, inverse scattering, and cryo-electron microscopy. This research has the potential to accelerate breakthroughs in molecular biology and rapid drug development, directly...
This National Science Foundation (NSF) Division of Mathematical Sciences grant award to Drexel University provides $270,000 over three years to develop new data-driven inversion methods and image reconstruction algorithms for nonlinear media with applications in radar, medical imaging, and optical design. The key innovations include using data to generate compact wave propagation models for efficient image reconstruction, expanding the applicability to large and noisy data sets, and developing...