A novel multilayer MV imager computational model for component optimization

Purpose: A novel Megavoltage (MV) multilayer imager (MLI) design featuring higher detective quantum efficiency and lower noise than current conventional MV imagers in clinical use has been recently reported. Optimization of the MLI design for multiple applications including tumor tracking, MV‐CBCT and portal dosimetry requires a computational model that will provide insight into the physics processes that affect the overall and individual components' performance. The purpose of the current work was to develop and validate a comprehensive computational model that can be used for MLI optimization. Methods: The MLI model was built using the Geant4 Application for Tomographic Emission (GATE) application. The model includes x‐ray and charged‐particle interactions as well as the optical transfer within the phosphor. A first prototype MLI device featuring a stack of four detection layers was used for model validation. Each layer of the prototype contains a copper buildup plate, a phosphor screen and photodiode array. The model was validated against measured data of Modulation Transfer Function (MTF), Noise‐Power Spectrum (NPS), and Detective Quantum Efficiency (DQE). MTF was computed using a slanted slit with 2.3° angle and 0.1 mm width. NPS was obtained using the autocorrelation function technique. DQE was calculated from MTF and NPS data. The comparison metrics between simulated and measured data were the Pearson's correlation coefficient (r) and the normalized root‐mean‐square error (NRMSE). Results: Good agreement between measured and simulated MTF and NPS values was observed. Pearson's correlation coefficient for the combined signal from all layers of the MLI was equal to 0.9991 for MTF and 0.9992 for NPS; NRMSE was 0.0121 for MTF and 0.0194 for NPS. Similarly, the DQE correlation coefficient for the combined signal was 0.9888 and the NRMSE was 0.0686. Conclusions: A comprehensive model of the novel MLI design was developed using the GATE toolkit and validated against measured MTF, NPS, and DQE data acquired with a prototype device featuring four layers. This model will be used for further optimization of the imager components and configuration for clinical radiotherapy applications.

[1]  Philip M Evans,et al.  Optical photon transport in powdered-phosphor scintillators. Part II. Calculation of single-scattering transport parameters. , 2013, Medical physics.

[2]  P. Barber.  Absorption and scattering of light by small particles , 1984 .

[3]  S. Incerti,et al.  Geant4 developments and applications , 2006, IEEE Transactions on Nuclear Science.

[4]  P. Evans,et al.  Optimization of the scintillation detector in a combined 3D megavoltage CT scanner and portal imager. , 1998, Medical physics.

[5]  Hyoung-Koo Lee,et al.  Monte Carlo analyses of X-ray absorption, noise and detective quantum efficiency considering therapeutic X-ray spectrum in portal imaging detector , 2000 .

[6]  Qihua Zhao,et al.  High-DQE EPIDs based on thick, segmented BGO and CsI:Tl scintillators: performance evaluation at extremely low dose. , 2009, Medical physics.

[7]  Kunio Doi,et al.  A simple method for determining the modulation transfer function in digital radiography , 1992, IEEE Trans. Medical Imaging.

[8]  David Sarrut,et al.  Simulation of a 6 MV Elekta Precise Linac photon beam using GATE/GEANT4 , 2011, Physics in medicine and biology.

[9]  Ioannis S Kandarakis,et al.  Modeling granular phosphor screens by Monte Carlo methods. , 2006, Medical physics.

[10]  S. Shalev,et al.  Monte Carlo optimization of metal/phosphor screens at megavoltage energies. , 1993, Medical physics.

[11]  I. Cunningham,et al.  Segmented crystalline scintillators: an initial investigation of high quantum efficiency detectors for megavoltage x-ray imaging. , 2005, Medical physics.

[12]  L. Antonuk.  Electronic portal imaging devices: a review and historical perspective of contemporary technologies and research. , 2002, Physics in medicine and biology.

[13]  R. K. Swank.  Measurement of absorption and noise in an x‐ray image intensifier , 1974 .

[14]  George Marsaglia.  Generating discrete random variables in a computer , 1963, CACM.

[15]  Aldo Badano,et al.  Accelerating Monte Carlo simulations of photon transport in a voxelized geometry using a massively parallel graphics processing unit. , 2009, Medical physics.

[16]  P. Munro,et al.  Clinical use of electronic portal imaging: report of AAPM Radiation Therapy Committee Task Group 58. , 2001, Medical physics.

[17]  J Yorkston,et al.  Signal, noise power spectrum, and detective quantum efficiency of indirect-detection flat-panel imagers for diagnostic radiology. , 1998, Medical physics.

[18]  George Loudos,et al.  A review of the use and potential of the GATE Monte Carlo simulation code for radiation therapy and dosimetry applications. , 2014, Medical physics.

[19]  Ehsan Samei.  Performance of Digital Radiographic Detectors : Quantification and Assessment Methods 1 , 2001 .

[20]  R. Sloboda,et al.  Comprehensive Monte Carlo calculation of the point spread function for a commercial a-Si EPID. , 2005, Medical physics.

[21]  Ross Berbeco,et al.  A novel EPID design for enhanced contrast and detective quantum efficiency , 2016, Physics in medicine and biology.

[22]  L Maigne,et al.  Comparison of GATE/GEANT4 with EGSnrc and MCNP for electron dose calculations at energies between 15 keV and 20 MeV , 2011, Physics in medicine and biology.

[23]  Rebecca Fahrig,et al.  Rapid Monte Carlo simulation of detector DQE(f). , 2014, Medical physics.

[24]  Rebecca Fahrig,et al.  A piecewise-focused high DQE detector for MV imaging. , 2015, Medical physics.

[25]  V. Vasileiou.  Monte Carlo Simulation of the Milagro Gamma-ray Observatory , 2007 .

[26]  Vesna Cuplov,et al.  Extension of the GATE Monte-Carlo simulation package to model bioluminescence and fluorescence imaging , 2014, Journal of biomedical optics.

[27]  Zdenka Kuncic,et al.  Characterization of optical transport effects on EPID dosimetry using Geant4. , 2013, Medical physics.

[28]  Martin J Butson,et al.  Multilayer Gafchromic film detectors for breast skin dose determination in vivo. , 2002, Physics in medicine and biology.

[29]  S Stute,et al.  GATE V6: a major enhancement of the GATE simulation platform enabling modelling of CT and radiotherapy , 2011, Physics in medicine and biology.

[30]  O Dössel,et al.  Monte Carlo simulations of the imaging performance of metal plate/phosphor screens used in radiotherapy. , 1999, Medical physics.

[31]  G. Lubberts,et al.  Random Noise Produced by X-Ray Fluorescent Screens* , 1968 .

[32]  Qihua Zhao,et al.  Performance evaluation of polycrystalline HgI2 photoconductors for radiation therapy imaging. , 2010, Medical physics.

[33]  L. C. Henyey,et al.  Diffuse radiation in the Galaxy , 1940 .

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

[35]  J. Rowlands,et al.  Monte Carlo simulation of a quantum noise limited Čerenkov detector based on air-spaced light guiding taper for megavoltage x-ray imaging. , 2014, Medical physics.

[36]  P. Keall,et al.  Modeling the truebeam linac using a CAD to Geant4 geometry implementation: dose and IAEA-compliant phase space calculations. , 2011, Medical physics.