Characteristics of x-ray beams in two commercial multidetector computed tomography simulators: Monte Carlo simulations

Kim, Sangroh; Song, H; Movsas, Benjamin; Chetty, Indrin J.

doi:10.1118/1.3670377

Cited by 11 publications

(10 citation statements)

References 27 publications

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“…Shown in Table 3 and Table 4, the agreement between the Monte Carlo simulations and the ion chamber measurements for the three site-specific scan protocols available on the two scanners is within 5% for all considered data points. Note that we obtained this absolute dose agreement with our source model derived from a set of measurement data without a priori knowledge of scanner specifications, and our results were comparable to those reported by other groups who carried out full Monte Carlo simulations of x-ray tubes with manufacturers’ specifications incorporated (Jarry et al , 2003; DeMarco et al , 2005; Gu et al , 2009; Lee et al , 2011; Lee et al , 2012; Kim et al , 2012). …”

Section: Resultssupporting

confidence: 87%

“…Compared to all the early works performing either full Monte Carlo simulations (Kim et al , 2012) or energy spectrum-based Monte Carlo simulations for the CT dose estimation (Jarry et al , 2003; DeMarco et al , 2005; DeMarco et al , 2007; Gu et al , 2009; Ding et al , 2010; Lee et al , 2011; Lee et al , 2012), we have shown that the developed generalized source model is accurate in calculating CT doses in the phantoms and in water for a variety of site-specific scan protocols at two widely-used CT scanners.…”

Section: Discussionmentioning

confidence: 94%

“…There have been numerous studies along this line comparing Monte Carlo simulation results to experimental measurements with various phantoms (Jarry et al , 2003; DeMarco et al , 2005; Staton et al , 2006; DeMarco et al , 2007; Deak et al , 2008; Gu et al , 2009; Ding et al , 2010; Li et al , 2011; Kim et al , 2012; Lee et al , 2012). Specifically, a full or energy spectrum-based Monte Carlo simulation was employed to compute the organ dose from CTs in both adult and pediatric patients, and even fetus, and to investigate the patient-specific cancer risk.…”

Section: Introductionmentioning

confidence: 99%

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A measurement-based generalized source model for Monte Carlo dose simulations of CT scans

et al. 2017

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The goal of this study is to develop a generalized source model (GSM) for accurate Monte Carlo dose simulations of CT scans based solely on the measurement data without a priori knowledge of scanner specifications. The proposed generalized source model consists of an extended circular source located at x-ray target level with its energy spectrum, source distribution and fluence distribution derived from a set of measurement data conveniently available in the clinic. Specifically, the central axis percent depth dose (PDD) curves measured in water and the cone output factors measured in air were used to derive the energy spectrum and the source distribution respectively with a Levenberg-Marquardt algorithm. The in-air film measurement of fan-beam dose profiles at fixed gantry was back-projected to generate the fluence distribution of the source model. A benchmarked Monte Carlo user code was used to simulate the dose distributions in water with the developed source model as beam input. The feasibility and accuracy of the proposed source model was tested on a GE LightSpeed and a Philips Brilliance Big Bore multi-detector CT (MDCT) scanners available in our clinic. In general, the Monte Carlo simulations of the PDDs in water and dose profiles along lateral and longitudinal directions agreed with the measurements within 4%/1mm for both CT scanners. The absolute dose comparison using two CTDI phantoms (16 cm and 32 cm in diameters) indicated a better than 5% agreement between the Monte Carlo-simulated and the ion chamber-measured doses at a variety of locations for the two scanners. Overall, this study demonstrated that a generalized source model can be constructed based only on a set of measurement data and used for accurate Monte Carlo dose simulations of patients’ CT scans, which would facilitate patient-specific CT organ dose estimation and cancer risk management in the diagnostic and therapeutic radiology.

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Section: Resultssupporting

confidence: 87%

Section: Discussionmentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

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A measurement-based generalized source model for Monte Carlo dose simulations of CT scans

et al. 2017

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“…The authors assumed that the source spectrum is symmetric about the isocenter in the lateral direction and constant in the axial or Z direction. This has previously been shown to be a reasonable assumption, as the energy fluence per incident particle about the x‐axis is approximately symmetric (22) …”

Section: Methodsmentioning

confidence: 89%

A measurement‐based X‐ray source model characterization for CT dosimetry computations

Sommerville

Poirier

Tambasco

2015

J Applied Clin Med Phys

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The purpose of this study was to show that the nominal peak tube voltage potential (kVp) and measured half‐value layer (HVL) can be used to generate energy spectra and fluence profiles for characterizing a computed tomography (CT) X‐ray source, and to validate the source model and an in‐house kV X‐ray dose computation algorithm (kVDoseCalc) for computing machine‐ and patient‐specific CT dose. Spatial variation of the X‐ray source spectra of a Philips Brilliance and a GE Optima Big Bore CT scanner were found by measuring the HVL along the direction of the internal bow‐tie filter axes. Third‐party software, Spektr, and the nominal kVp settings were used to generate the energy spectra. Beam fluence was calculated by dividing the integral product of the spectra and the in‐air NIST mass‐energy attenuation coefficients by in‐air dose measurements along the filter axis. The authors found the optimal number of photons to seed in kVDoseCalc to achieve dose convergence. The Philips Brilliance beams were modeled for 90, 120, and 140 kVp tube settings. The GE Optima beams were modeled for 80, 100, 120, and 140 kVp tube settings. Relative doses measured using a Capintec Farmer‐type ionization chamber (0.65 cc) placed in a cylindrical polymethyl methacrylate (PMMA) phantom and irradiated by the Philips Brilliance, were compared to those computed with kVDoseCalc. Relative doses in an anthropomorphic thorax phantom (E2E SBRT Phantom) irradiated by the GE Optima were measured using a (0.015 cc) PTW Freiburg ionization chamber and compared to computations from kVDoseCalc. The number of photons required to reduce the average statistical uncertainty in dose to <0.3% was 2×105. The average percent difference between calculation and measurement over all 12 PMMA phantom positions was found to be 1.44%, 1.47%, and 1.41% for 90, 120, and 140 kVp, respectively. The maximum percent difference between calculation and measurement for all energies, measurement positions, and phantoms was less than 3.50%. Thirty‐five out of a total of 36 simulation conditions were within the experimental uncertainties associated with measurement reproducibility and chamber volume effects for the PMMA phantom. The agreement between calculation and measurement was within experimental uncertainty for 19 out of 20 simulation conditions at five points of interest in the anthropomorphic thorax phantom for the four beam energies modeled. The source model and characterization technique based on HVL measurements and nominal kVp can be used to accurately compute CT dose. This accuracy provides experimental validation of kVDoseCalc for computing CT dose.PACS numbers: 87.57.Q‐, 87.57.uq, 87.10.Rt

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“…Monte Carlo (MC) method has been widely used for simulating x-ray scatter radiation and X-ray imaging [34][35][36]. And the accuracy of many MC simulation packages (e.g.…”

Section: Image Acquisitionmentioning

confidence: 99%

A scatter correction method for dual-energy digital mammography: Monte Carlo simulation

Gao

2014

Journal of X-Ray Science and Technology

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PURPOSE: To develop a novel scatter correction method without additional patient dose for dual-energy digital mammography (DEDM) to reduce scatter's impacts and enhance microcalcification detectability in dual-energy X-ray subtraction image. METHODS: Combining scatter radiation is lower spatial frequency component and calcifications are sparsely distributed in digital mammogram, we develop a new scatter correction strategy. First, an adaptive sampling scheme is presented to find possible noncalcification (zero calcification) pixels. Then the maximum likelihood expectation maximization (MLEM) algorithm is applied to evaluate initial scatter surface. The accurate scatter radiation of sampling pixels is obtained by solving dual-energy computational formula with zero calcification constraint and scatter surface constraint. RESULTS: After scatter correction, the scatter-to-primary ratio (SPR) of wedge phantom is reduced from ∼36.0% to ∼3.1% for low-energy (LE) image and ∼29.6% to ∼0.6% for high-energy (HE) image. For step phantom, the SPR is reduced from ∼42.1% and ∼30.3% to ∼3.9% and ∼0.9% for LE and HE image, respectively. The calcification contrast-to-noise ratio is improved by two orders of magnitudes in calcification images. CONCLUSIONS: The proposed method shows an excellent performance on scatter reduction and calcification detection. Compared with hardware based scatter correction strategy, our method need no extra exposure and is easy to implementation.

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Characteristics of x-ray beams in two commercial multidetector computed tomography simulators: Monte Carlo simulations

Abstract: The authors demonstrate the full MC simulations of two commercial MDCT simulators to characterize their x-ray beams. This study may be useful to establish a patient-specific dosimetry for the MDCT systems.

Cited by 11 publications

References 27 publications

A measurement-based generalized source model for Monte Carlo dose simulations of CT scans

A measurement-based generalized source model for Monte Carlo dose simulations of CT scans

A measurement‐based X‐ray source model characterization for CT dosimetry computations

A scatter correction method for dual-energy digital mammography: Monte Carlo simulation

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