Skin dose mapping for non-uniform x-ray fields using a backscatter point spread function

Vijayan, Sarath; Xiong, Zhenyu; Rudin, Stephen; Bednarek, Daniel R.

doi:10.1117/12.2254257

Cited by 6 publications

(6 citation statements)

References 21 publications

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“…6, 7, 8 In that case, the exposure was delivered cross-table to avoid the forward scatter dose contribution from the patient table, and hence included only the primary dose and backscatter dose distribution. For the current study, the skin dose distribution is determined for a posterior-anterior vertical x-ray beam incident on a 20 cm thick PMMA block placed over the patient table using the DTS and XR-QA2 Gafchromic film.…”

Section: Methodsmentioning

confidence: 99%

Calculation of forward scatter dose distribution at the skin entrance from the patient table for fluoroscopically guided interventions using a pencil beam convolution kernel

Vijayan¹,

Xiong²,

Guo³

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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The forward-scatter dose distribution generated by the patient table during fluoroscopic interventions and its contribution to the skin dose is studied. The forward-scatter dose distribution to skin generated by a water table-equivalent phantom and the patient table are calculated using EGSnrc Monte-Carlo and Gafchromic film as a function of x-ray field size and beam penetrability. Forward scatter point spread function’s (PSFn) were generated with EGSnrc from a 1×1 mm simulated primary pencil beam incident on the water model and patient table. The forward-scatter point spread function normalized to the primary is convolved over the primary-dose distribution to generate scatter-dose distributions. The utility of PSFn to calculate the entrance skin dose distribution using DTS (dose tracking system) software is investigated. The forward-scatter distribution calculations were performed for 2.32 mm, 3.10 mm, 3.84 mm and 4.24 mm Al HVL x-ray beams for 5×5 cm, 9×9 cm, 13.5×13.5 cm sized x-ray fields for water and 3.1 mm Al HVL x-ray beam for 16.5×16.5 cm field for the patient table. The skin dose is determined with DTS by convolution of the scatter dose PSFn’s and with Gafchromic film under PMMA “patient-simulating” blocks for uniform and for shaped x-ray fields. The normalized forward-scatter distribution determined using the convolution method for water table-equivalent phantom agreed with that calculated for the full field using EGSnrc within ±6%. The normalized forward-scatter dose distribution calculated for the patient table for a 16.5×16.5 cm FOV, agreed with that determined using film within ±2.4%. For the homogenous PMMA phantom, the skin dose using DTS was calculated within ±2 % of that measured with the film for both uniform and non-uniform x-ray fields. The convolution method provides improved accuracy over using a single forward-scatter value over the entire field and is a faster alternative to performing full-field Monte-Carlo calculations.

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Section: Methodsmentioning

confidence: 99%

Calculation of forward scatter dose distribution at the skin entrance from the patient table for fluoroscopically guided interventions using a pencil beam convolution kernel

Vijayan¹,

Xiong²,

Guo³

et al. 2018

Medical Imaging 2018: Physics of Medical Imaging

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“…However, a single backscatter factor does not describe the scatter distribution sufficiently well as scatter may extend beyond the collimated area . In addition, scatter decreases toward the border of the irradiated area . Therefore, studies propose the expansion of scalar backscatter factors to more generic precomputed two‐dimensional (2D) point spread functions (PSF) or three‐dimensional (3D) dose point kernels (DPK) .…”

Section: Introductionmentioning

confidence: 99%

Physics‐driven learning of x‐ray skin dose distribution in interventional procedures

et al. 2019

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Purpose Radiation doses accumulated during very complicated image‐guided x‐ray procedures have the potential to cause stochastic, but also deterministic effects, such as skin rashes or even hair loss. To monitor and reduce radiation‐related risks to patients’ skin, x‐ray imaging devices are equipped with online air kerma monitoring components. Traditionally, such measurements have been used to estimate skin entrance dose by (a) estimating air kerma at the interventional reference point (IRP), (b) forward projecting the dose distribution, and (c) considering a backscatter factor among other correction factors. Unfortunately, the complicated interaction between incident x‐ray photons, secondary electrons, and skin tissue cannot be properly accounted for by assuming a linear relationship between forward projected air kerma and a backscatter factor. Gold standard skin dose models are therefore determined using Monte Carlo (MC) techniques. However, MC simulations are computationally complex in general and possible acceleration mainly depends on the employed hardware and variance reduction techniques. To obtain reliable and fast dose estimates, we propose to combine MC‐based simulations with learning‐based methods. Methods The basic idea of our method is to approximate the radiation physics to calculate a first‐order exposure estimate quickly. This initial estimate is then refined using prior knowledge derived from MC simulations. To this end, the primary photon propagation inside a voxelized patient model is estimated using a less accurate but fast photon ray casting (RC) simulation based on the Beer–Lambert law. The results of the RC simulation are then fed into a convolutional neural network (CNN), which maps the propagation of primary photons to the dose deposition inside the patient model. Additionally, the patient model itself including anatomy and material properties, such as mass density and mass energy‐absorption coefficients, are fed into the CNN as well. The CNN is trained using smoothed results of MC simulations as output and RC simulations of identical imaging settings and patient models as input. Results In total, 163 MC and associated RC simulations are carried out for the head, thorax, abdomen, and pelvis in three different voxel phantoms. We used 108 or 109 primarily emitted photons sampled from a 125 kV peak voltage spectrum, respectively. Edge‐preserving smoothing (EPS) is applied to reduce (a) general stochastic uncertainties and (b) stochastic uncertainty concerning MC simulations of less primary photons. The CNN is trained using seven imaging settings of the abdomen in a single phantom. Testing its performance on the remaining datasets, the CNN is capable of estimating skin dose with an error of below 10% for the majority of test cases. Conclusion The combination of deep neural networks and MC simulation of particle physics has the potential to decrease the computational complexity of accurate skin dose estimation. The proposed approach can provide dose distributions in under one second when runni...

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“…To improve the accuracy of dose calculation, we proposed a method for the estimation and automatic mapping of cumulative skin dose for spatially-shaped X-ray fields through the convolution of a backscatter “point spread function” (PSF) with the primary dose distribution. 21,22 To improve the accuracy of our backscatter PSF convolution method, this study investigated the variation of backscatter PSF with variation of a number of parameters such as beam energy, incident angle, and object properties.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo investigation of backscatter point spread function for x-ray imaging examinations

Xiong

Vijayan

Rudin

et al. 2017

Medical Imaging 2017: Physics of Medical Imaging

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X-ray imaging examinations, especially complex interventions, may result in relatively high doses to the patient’s skin inducing skin injuries. A method was developed to determine the skin-dose distribution for non-uniform x-ray beams by convolving the backscatter point-spread-function (PSF) with the primary-dose distribution to generate the backscatter distribution that, when added to the primary dose, gives the total-dose distribution. This technique was incorporated in the dose-tracking system (DTS), which provides a real-time color-coded 3D-mapping of skin dose during fluoroscopic procedures. The aim of this work is to investigate the variation of the backscatter PSF with different parameters. A backscatter PSF of a 1-mm x-ray beam was generated by EGSnrc Monte-Carlo code for different x-ray beam energies, different soft-tissue thickness above bone, different bone thickness and different entrance-beam angles, as well as for different locations on the SK-150 anthropomorphic head phantom. The results show a reduction of the peak scatter to primary dose ratio of 48% when X-ray beam voltage is increased from 40 keV to 120 keV. The backscatter dose was reduced when bone was beneath the soft tissue layer and this reduction increased with thinner soft tissue and thicker bone layers. The backscatter factor increased about 21% as the angle of incidence of the beam with the entrance surface decreased from 90° (perpendicular) to 30°. The backscatter PSF differed for different locations on the SK-150 phantom by up to 15%. The results of this study can be used to improve the accuracy of dose calculation when using PSF convolution in the DTS.

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Skin dose mapping for non-uniform x-ray fields using a backscatter point spread function

Cited by 6 publications

References 21 publications

Calculation of forward scatter dose distribution at the skin entrance from the patient table for fluoroscopically guided interventions using a pencil beam convolution kernel

Calculation of forward scatter dose distribution at the skin entrance from the patient table for fluoroscopically guided interventions using a pencil beam convolution kernel

Physics‐driven learning of x‐ray skin dose distribution in interventional procedures

Monte Carlo investigation of backscatter point spread function for x-ray imaging examinations

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