9 March 2017 Empirical neural network forward model for maximum likelihood material decomposition in spectral CT
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Abstract
CT measurements using photon counting detectors provide spectral information that can be used to estimate a material's composition. This material decomposition task is complicated by pulse pileup and charge-sharing phenomena. Physics-based methods that use maximum likelihood to estimate a material's composition rely on accurate modeling of the forward spectral measurement process, including the source spectrum and detector response. An empirical projection-domain decomposition method is proposed that uses energy-bin measurements from known basis material path lengths. The known basis material path lengths and energy-bin measurements are used to train a neural network to model the forward spectral measurement process. The neural network is used with a maximum likelihood algorithm to estimate basis material path lengths with optimal noise properties. The method does not require a model of the source spectrum or detector response. Simulations of a step-wedge phantom containing 10 path lengths of polymethyl methacrylate and 10 path lengths of aluminum resulted in 100 calibration measurements for training. Path lengths not included in calibration were used to evaluate the estimator's performance. Projections of the test path lengths contained 1000 Poisson noise realizations and the bias and variance of the estimated path lengths were used as evaluation metrics. The proposed method had less than 2% bias in the test path lengths and had a variance that achieved the Cramèr-Rao lower bound. The proposed method is an efficient estimator that estimates basis material path lengths with optimal noise properties.
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Kevin C. Zimmerman, Adam Petschke, "Empirical neural network forward model for maximum likelihood material decomposition in spectral CT", Proc. SPIE 10132, Medical Imaging 2017: Physics of Medical Imaging, 101323S (9 March 2017); doi: 10.1117/12.2255953; https://doi.org/10.1117/12.2255953
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