A hybrid Monte Carlo model for the energy response functions of X-ray photon counting detectors

Abstract In photon counting computed tomography (CT), it is vital to know the energy response functions of the detector for noise estimation and system optimization. Empirical methods lack flexibility and Monte Carlo simulations require too much knowledge of the detector. In this paper, we proposed a hybrid Monte Carlo model for the energy response functions of photon counting detectors in X-ray medical applications. GEANT4 was used to model the energy deposition of X-rays in the detector. Then numerical models were used to describe the process of charge sharing, anti-charge sharing and spectral broadening, which were too complicated to be included in the Monte Carlo model. Several free parameters were introduced in the numerical models, and they could be calibrated from experimental measurements such as X-ray fluorescence from metal elements. The method was used to model the energy response function of an XCounter Flite X1 photon counting detector. The parameters of the model were calibrated with fluorescence measurements. The model was further tested against measured spectrums of a VJ X-ray source to validate its feasibility and accuracy.

[1]  Feng Zhang,et al.  Charge sharing in common-grid pixelated CdZnTe detectors , 2011 .

[2]  E. Roessl,et al.  Cramér–Rao lower bound of basis image noise in multiple-energy x-ray imaging , 2009, Physics in medicine and biology.

[3]  F Verhaegen,et al.  SpekCalc: a program to calculate photon spectra from tungsten anode x-ray tubes , 2009, Physics in medicine and biology.

[4]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[5]  C. Ponchut,et al.  Correction of the charge sharing in photon-counting pixel detector data , 2008 .

[6]  Michael Campbell,et al.  Medipix3: A 64 k pixel detector readout chip working in single photon counting mode with improved spectrometric performance , 2011 .

[7]  H. Nilsson,et al.  Simulation of photon and charge transport in X-ray imaging semiconductor sensors , 2002 .

[8]  Tuomas Poikela,et al.  Review of hybrid pixel detector readout ASICs for spectroscopic X-ray imaging , 2016 .

[9]  K. Taguchi,et al.  Vision 20/20: Single photon counting x-ray detectors in medical imaging. , 2013, Medical physics.

[10]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[11]  Xiaochuan Pan,et al.  A robust method of x-ray source spectrum estimation from transmission measurements: Demonstrated on computer simulated, scatter-free transmission data , 2005 .

[12]  Rainer Storn,et al.  Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces , 1997, J. Glob. Optim..

[13]  Axel Thran,et al.  Sensitivity of Photon-Counting Based ${\rm K}$-Edge Imaging in X-ray Computed Tomography , 2011, IEEE Transactions on Medical Imaging.

[14]  J. Schlomka,et al.  Experimental feasibility of multi-energy photon-counting K-edge imaging in pre-clinical computed tomography , 2008, Physics in medicine and biology.

[15]  Katsuyuki Taguchi,et al.  Modeling the performance of a photon counting x-ray detector for CT: energy response and pulse pileup effects. , 2011, Medical physics.

[16]  H.-E. Nilsson,et al.  Monte Carlo simulation of charge sharing effects in silicon and GaAs photon-counting X-ray imaging detectors , 2004, IEEE Transactions on Nuclear Science.

[17]  H Toyokawa,et al.  The PILATUS 1M detector. , 2006, Journal of synchrotron radiation.

[18]  A. Küçükönder The X-ray fluorescence cross-section for bromide and iodide compounds , 2001 .

[19]  E. Kessler,et al.  X-ray transition energies: new approach to a comprehensive evaluation , 2003 .