Multi-Energy and Fast-Convergence Iterative Reconstruction Algorithm for Organic Material Identification Using X-ray Computed Tomography.
Mihai IoveaAndrei StanciulescuEdward HermannMarian NeaguOctavian G DuliuPublished in: Materials (Basel, Switzerland) (2023)
In order to significantly reduce the computing time while, at the same time, keeping the accuracy and precision when determining the local values of the density and effective atomic number necessary for identifying various organic material, including explosives and narcotics, a specialized multi-stage procedure based on a multi-energy computed tomography investigation within the 20-160 keV domain was elaborated. It consisted of a compensation for beam hardening and other non-linear effects that affect the energy dependency of the linear attenuation coefficient (LAC) in the chosen energy domain, followed by a 3D fast reconstruction algorithm capable of reconstructing the local LAC values for 64 energy values from 19.8 to 158.4 keV, and, finally, the creation of a set of algorithms permitting the simultaneous determination of the density and effective atomic number of the investigated materials. This enabled determining both the density and effective atomic number of complex objects in approximately 24 s, with an accuracy and precision of less than 3%, which is a significantly better performance with respect to the reported literature values.
Keyphrases
- computed tomography
- dual energy
- simultaneous determination
- machine learning
- deep learning
- positron emission tomography
- liquid chromatography tandem mass spectrometry
- electron microscopy
- magnetic resonance imaging
- image quality
- high performance liquid chromatography
- tandem mass spectrometry
- mass spectrometry
- liquid chromatography
- neural network
- magnetic resonance
- single molecule
- diffusion weighted imaging