US 12,406,104 B2
System and method for artifact reduction of computed tomography reconstruction leveraging artificial intelligence and a priori known model for the object of interest
Amir Ziabari, Knoxville, TN (US); Singanallur Venkatakrishnan, Knoxville, TN (US); Philip R. Bingham, Knoxville, TN (US); Michael M. Kirka, Knoxville, TN (US); Vincent C. Paquit, Knoxville, TN (US); Ryan R. Dehoff, Knoxville, TN (US); and Abhishek Dubey, Rockville, MD (US)
Assigned to UT-Battelle, LLC, Oak Ridge, TN (US)
Filed by UT-Battelle, LLC, Oak Ridge, TN (US)
Filed on Aug. 3, 2021, as Appl. No. 17/392,645.
Claims priority of provisional application 63/060,450, filed on Aug. 3, 2020.
Prior Publication US 2022/0035961 A1, Feb. 3, 2022
Int. Cl. G06F 30/10 (2020.01); G06N 3/045 (2023.01); G06T 7/00 (2017.01); G06T 19/20 (2011.01)
CPC G06F 30/10 (2020.01) [G06N 3/045 (2023.01); G06T 7/0002 (2013.01); G06T 19/20 (2013.01); G06T 2207/10081 (2013.01)] 33 Claims
OG exemplary drawing
 
1. An artifact reduction artificial intelligence training system for computed tomography (CT) of an object of interest, the system comprising:
a computer-aided design (CAD) model representing the object of interest, stored in memory;
an artifact characterization, stored in memory;
one or more computer subsystems; and
one or more components executed by the one or more computer subsystems, wherein the one or more components include:
a computed tomography (CT) simulator to generate a plurality of CT simulated projections based on the CAD model, wherein a subset of the plurality of CT simulated projections include simulated artifacts based on the artifact characterization, wherein the artifact characterization includes a set of beam-hardening parameters and detector noise parameters for simulating artifacts caused by beam hardening and detector noise during an actual CT scan, wherein the CT simulator is configured to generate at least a subset of the plurality of CT simulated projections based on the CAD model, the beam-hardening parameters, and the detector noise parameters, wherein the generated plurality of corresponding artifact CT simulated projections are realistically noisy, and wherein a subset of the plurality of the CT simulated projections lack simulated artifacts; and
a deep learning component configured to train a deep learning artifact reduction model based on the plurality of CT simulated projections and generate a set of deep learning artifact reduction model parameters.