Ut-battelle, Inc.

Amir Ziabari

Philip R Bingham

Ryan Dehoff

Vincent C Paquit

Singanallur Venkatakrishnan

Abhishek Dubey

Michael M Kirka

Nondestructive evaluation (NDE) of objects can elucidate impacts of various process parameters and qualification of the object. Computed tomography (CT) enables rapid NDE and characterization of objects. However, CT presents challenges because of artifacts produced by standard reconstruction algorithms. Beam-hardening artifacts especially complicate and adversely impact the process of detecting defects. By leveraging computer-aided design (CAD) models, CT simulations, and a deep-neutral network high-quality CT reconstructions that are affected by noise and beam-hardening can be simulated and used to improve reconstructions. The systems and methods of the present disclosure can significantly improve the reconstruction quality, thereby enabling better detection of defects compared with the state of the art.

System and method for artifact reduction of computed tomography reconstruction leveraging artificial intelligence and a priori known model for the object of interest

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Knoxville, TN, United States of America

Amir Ziabari