Paper
18 March 2008 Experimental validation of a Monte Carlo-based kV x-ray projection model for the Varian linac-mounted cone-beam CT imaging system
Dimitrios Lazos, Damodar Pokhrel, Zhong Su, Jun Lu, Jeffrey F. Williamson
Author Affiliations +
Abstract
Fast and accurate modeling of cone-beam CT (CBCT) x-ray projection data can improve CBCT image quality either by linearizing projection data for each patient prior to image reconstruction (thereby mitigating detector blur/lag, spectral hardening, and scatter artifacts) or indirectly by supporting rigorous comparative simulation studies of competing image reconstruction and processing algorithms. In this study, we compare Monte Carlo-computed x-ray projections with projections experimentally acquired from our Varian Trilogy CBCT imaging system for phantoms of known design. Our recently developed Monte Carlo photon-transport code, PTRAN, was used to compute primary and scatter projections for cylindrical phantom of known diameter (NA model 76-410) with and without bow-tie filter and antiscatter grid for both full- and half-fan geometries. These simulations were based upon measured 120 kVp spectra, beam profiles, and flat-panel detector (4030CB) point-spread function. Compound Poisson- process noise was simulated based upon measured beam output. Computed projections were compared to flat- and dark-field corrected 4030CB images where scatter profiles were estimated by subtracting narrow axial-from full axial width 4030CB profiles. In agreement with the literature, the difference between simulated and measured projection data is of the order of 6-8%. The measurement of the scatter profiles is affected by the long tails of the detector PSF. Higher accuracy can be achieved mainly by improving the beam modeling and correcting the non linearities induced by the detector PSF.
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Dimitrios Lazos, Damodar Pokhrel, Zhong Su, Jun Lu, and Jeffrey F. Williamson "Experimental validation of a Monte Carlo-based kV x-ray projection model for the Varian linac-mounted cone-beam CT imaging system", Proc. SPIE 6913, Medical Imaging 2008: Physics of Medical Imaging, 69133Q (18 March 2008); https://doi.org/10.1117/12.772873
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Cited by 3 scholarly publications.
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KEYWORDS
Monte Carlo methods

Sensors

X-rays

X-ray imaging

Lead

Data modeling

Imaging systems

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