Intelligent Acquisition and Reconstruction for Hyper-Spectral Tomography Systems
Description
We will develop artificial intelligence (AI) and machine learning (ML) algorithms to enable dramatic improvements in the throughput and performance of hyperspectral (i.e., multiple energies) computed tomography (HSCT) beamlines at DOE BES Scientific
Detailed example
We will develop artificial intelligence (AI) and machine learning (ML) algorithms to enable dramatic improvements in the throughput and performance of hyperspectral (i.e., multiple energies) computed tomography (HSCT) beamlines at DOE BES Scientific User Facilities (SUFs). We will demonstrate the utility of our algorithms by carefully designing experiments for energy materials at HSCT beamlines available at the Spallation Neutron Source (SNS) and the National Synchrotron Light Source II (NSLS-II). We will also develop AI driven data acquisition algorithms that will optimize the scanning strategy on-the-fly, in order to obtain the fewest yet most informative set of measurements (i.e. reducing beam time and/or number of projections in a data set). Our team will provide ML based reconstruction algorithms that can produce high quality reconstructions from incomplete, sparse and low signal-to-noise ratio datasets enabling real-time feedback and ensuring best possible reconstruction on co
AI / analytics pattern
Classical/Predictive Machine Learning: Models trained on data to make predictions or classifications based on identified patterns or relationships.
Automation level / stage
a) Pre-deployment – The use case is in a development or acquisition status.
Expected benefit
We will develop artificial intelligence (AI) and machine learning (ML) algorithms to enable dramatic improvements in the throughput and performance of hyperspectral (i.e., multiple energies) computed tomography (HSCT) beamlines at DOE BES Scientific User Facilities (SUFs). We will demonstrate the utility of our algorithms by carefully designing experiments for energy materials at HSCT beamlines available at the Spallation Neutron Source (SNS) and the National Synchrotron Light Source II (NSLS-II). We will also develop AI driven data acquisition algorithms that will optimize the scanning strategy on-the-fly, in order to obtain the fewest yet most informative set of measurements (i.e. reducing beam time and/or number of projections in a data set). Our team will provide ML based reconstruction algorithms that can produce high quality reconstructions from incomplete, sparse and low signal-to-noise ratio datasets enabling real-time feedback and ensuring best possible reconstruction on completion of the experiment. Finally, our efforts will be available to the user community at both facilities via a general user interface.
Audit / financial statement impact
Does not meet definition
Controls / human review
ATO: Not reported; PIA: Not published