NEUP CINRII-24-31308: Enhancing Multimodal Tomography for Nuclear Applications using Machine Learning

We will develop methods based on machine learning for upsampling and denoising muon, gamma and neutron tomographic images of spent nuclear fuel casks. We will develop methods based on machine learning to improve both the resolution and signal-to-noise ratios of passive multimodal tomography images. This work will dramatically enhance the sensitivity of dry cask imaging technologies that are essential for the long-term sustainability of nuclear power in the United States.

Additional Info

Field	Value
Awarded Institution	Colorado School of Mines
PI	Andrew Osborne