Mr. Kevin C. Zimmerman Profile

Kevin C. Zimmerman

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Publications (7)

Proceedings Article | 1 March 2019 Paper

CZT modeling for photon counting computer tomography

Xiaochun Lai, Liang Cai, Kevin Zimmerman, Maduri Kaul, Xiaohui Zhan, Yi Qiang, Mattew Veale, Richard Thompson

Proceedings Volume 10948, 109484G (2019) https://doi.org/10.1117/12.2512531

KEYWORDS: Sensors, X-rays, Photon counting, Computed tomography, X-ray detectors

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Proceedings Article | 1 March 2019 Paper

Simulation of scattered radiation with various anti-scatter grid designs in a photon counting CT

Xiaohui Zhan, Kevin Zimmerman, Liang Cai, Xiaochun Lai, Madhuri Kaul, Richard Thompson

Proceedings Volume 10948, 109484Q (2019) https://doi.org/10.1117/12.2512494

KEYWORDS: Photon counting, Monte Carlo methods, X-ray computed tomography, Computed tomography, Physics

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Proceedings Article | 1 March 2019 Paper

Photon-counting detector simulation: Monte-Carlo energy deposition, physics-based charge transport and current induction, and SPICE electronics

Kevin Zimmerman, Liang Cai, Xiaochun Lai, Richard Thompson

Proceedings Volume 10948, 109484H (2019) https://doi.org/10.1117/12.2512245

KEYWORDS: Sensors, Monte Carlo methods, Device simulation, Electronics, Photodetectors, Computer simulations, Clouds, Electrons, Software development, X-ray computed tomography

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Proceedings Article | 9 March 2017 Paper

Empirical neural network forward model for maximum likelihood material decomposition in spectral CT

Kevin Zimmerman, Adam Petschke

Proceedings Volume 10132, 101323S (2017) https://doi.org/10.1117/12.2255953

KEYWORDS: Sensors, Neural networks, Photon counting, Calibration, Spectral models, Evolutionary algorithms, Detection and tracking algorithms, Aluminum, Data modeling, Signal attenuation, Statistical analysis

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Proceedings Article | 25 March 2016 Paper

Comparison of quantitative k-edge empirical estimators using an energy-resolved photon-counting detector

Kevin Zimmerman, Taly Gilat Schmidt

Proceedings Volume 9783, 97831S (2016) https://doi.org/10.1117/12.2217233

KEYWORDS: Neural networks, Photon counting, Error analysis, Calibration, Data modeling, Photodetectors, X-ray computed tomography, X-ray detectors, X-rays, Gadolinium, Aluminum, Polymethylmethacrylate, Chest

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Proceedings Article | 19 March 2014 Paper

Effects of energy-bin acquisition methods on noise properties in photon-counting spectral CT

Taly Gilat Schmidt, Kevin Zimmerman, Emil Sidky

Proceedings Volume 9033, 90331B (2014) https://doi.org/10.1117/12.2043932

KEYWORDS: X-rays, Sensors, Polymethylmethacrylate, Aluminum, Computer simulations, Photons, Data acquisition, X-ray imaging, Photodetectors, Spectral models

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Proceedings Article | 19 March 2014 Paper

Experimental study of two material decomposition methods using multi-bin photon counting detectors

Kevin Zimmerman, Emil Sidky, Taly Gilat Schmidt

Proceedings Volume 9033, 90333G (2014) https://doi.org/10.1117/12.2043679

KEYWORDS: Calibration, Polymethylmethacrylate, Aluminum, Sensors, Photodetectors, Photon counting, Signal attenuation, X-rays, Nickel, Error analysis

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Photon-counting detectors with multi-bin pulse height analysis (PHA) are capable of extracting energy dependent information which can be exploited for material decomposition. Iterative decomposition algorithms have been previously implemented which require prior knowledge of the source spectrum, detector spectral response, and energy threshold settings. We experimentally investigated two material decomposition methods that do not require explicit knowledge of the source spectrum and spectral response. In the first method, the effective spectrum for each energy bin is estimated from calibration transmission measurements, followed by an iterative maximum likelihood decomposition algorithm. The second investigated method, first proposed and tested through simulations by Alvarez, uses a linearized maximum likelihood estimator which requires calibration transmission measurements. The Alvarez method has the advantage of being non-iterative. This study experimentally quantified and compared the material decomposition bias, as a percentage of material thickness, and standard deviation resulting from these two material decomposition estimators. Multi-energy x-ray transmission measurements were acquired through varying thicknesses of Teon, Delrin, and neoprene at two different flux settings and decomposed into PMMA and aluminum thicknesses using the investigated methods. In addition, a series of 200 equally spaced projections of a rod phantom were acquired over 360°. The multi-energy sinograms were decomposed using both empirical methods and then reconstructed using filtered backprojection producing two images representing each basis material. The Alvarez method decomposed Delrin into PMMA with a bias of 0.5-19% and decomposed neoprene into aluminum with a bias of less than 3%. The spectral estimation method decomposed Delrin into PMMA with a bias of 0.6-16% and decomposed neoprene into aluminum with a bias of 0.1-58%. In general, the spectral estimation method resulted in larger bias than the Alvarez method. Both methods demonstrated similar standard deviations of less than 1 mm. Both decomposition methods resulted in similar bias and standard deviation when comparing performance at the two flux levels. The results suggest preliminary feasibility of two empirical methods that use calibration measurements rather than prior knowledge of system parameters to estimate thicknesses of the basis materials.

Showing 5 of 7 publications

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