Experimental comparison of empirical material decomposition methods for spectral CT. Phys Med Biol 2015 Apr 21;60(8):3175-91
Date
03/31/2015Pubmed ID
25813054Pubmed Central ID
PMC4459606DOI
10.1088/0031-9155/60/8/3175Scopus ID
2-s2.0-84927597582 (requires institutional sign-in at Scopus site) 52 CitationsAbstract
Material composition can be estimated from spectral information acquired using photon counting x-ray detectors with pulse height analysis. Non-ideal effects in photon counting x-ray detectors such as charge-sharing, k-escape, and pulse-pileup distort the detected spectrum, which can cause material decomposition errors. This work compared the performance of two empirical decomposition methods: a neural network estimator and a linearized maximum likelihood estimator with correction (A-table method). The two investigated methods differ in how they model the nonlinear relationship between the spectral measurements and material decomposition estimates. The bias and standard deviation of material decomposition estimates were compared for the two methods, using both simulations and experiments with a photon-counting x-ray detector. Both the neural network and A-table methods demonstrated a similar performance for the simulated data. The neural network had lower standard deviation for nearly all thicknesses of the test materials in the collimated (low scatter) and uncollimated (higher scatter) experimental data. In the experimental study of Teflon thicknesses, non-ideal detector effects demonstrated a potential bias of 11-28%, which was reduced to 0.1-11% using the proposed empirical methods. Overall, the results demonstrated preliminary experimental feasibility of empirical material decomposition for spectral CT using photon-counting detectors.
Author List
Zimmerman KC, Schmidt TGMESH terms used to index this publication - Major topics in bold
Computer SimulationModels, Theoretical
Phantoms, Imaging
Photons
Polymethyl Methacrylate
Polytetrafluoroethylene
Tomography, X-Ray Computed
X-Rays
