A machine learning decision criterion for reducing scan time for hyperspectral neutron computed tomography systems.

ABSTRACT: We present the first machine learning-based autonomous hyperspectral neutron computed tomography experiment performed at the Spallation Neutron Source. Hyperspectral neutron computed tomography allows the characterization of samples by enabling the reconstruction of crystallographic information and elemental/isotopic composition of objects relevant to materials science. High quality reconstructions using traditional algorithms such as the filtered back projection require a high signal-to-noise ratio across a wide wavelength range combined with a large number of projections. This results in scan times of several days to acquire hundreds of hyperspectral projections, during which end users have minimal feedback. To address these challenges, a golden ratio scanning protocol combined with mode

SUBMITTER: Tang S 

PROVIDER: S-EPMC11220078 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature