A system to track skin dose for neuro-interventional cone-beam computed tomography (CBCT)

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

doi:10.1117/12.2216931

Cited by 6 publications

(5 citation statements)

References 9 publications

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“…This is mainly because of overresponse characteristics owing to the relatively high effective atomic numbers of constituent materials (aluminum oxide doped with carbon, Al2O3:C) in diagnostic energy ranges [8,9]. A significant problem occurs for the use of OSLD with cone-beam CT [14], as the absorbed doses can be contributed by various overlapping projections, including the primary radiation of entrance/exit incidences and scattering from many rays [15]. Each incidence ray might possess different mean energies owing to the different degrees of beam hardening.…”

Section: Introductionmentioning

confidence: 99%

Effects of Fully Filling Deep Electron/Hole Traps in Optically Stimulated Luminescence Dosimeters in the Kilovoltage Energy Range

et al. 2022

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Background: This study investigated the characteristics of optically stimulated luminescence dosimeters (OSLDs) with fully filled deep electron/hole traps in the kV energy ranges.Materials and Methods: The experimental group consisted of InLight nanoDots, whose deep electron/hole traps were fully filled with 5 kGy pre-irradiation (OSLDexp), whereas the non-preirradiated OSLDs were arranged as a control group (OSLDcont). Absorbed doses for 75, 80, 85, 90, 95, 100, and 105 kVp with 200 mA and 40 ms were measured and defined as the unit doses for each energy value. A bleaching device equipped with a 520-nm long-pass filter was used, and the strong beam mode was used to read out signal counts. The characteristics were investigated in terms of fading, dose sensitivities according to the accumulated doses, and dose linearity.Results and Discussion: In OSLDexp, the average normalized counts (sensitivities) were 12.7%, 14.0%, 15.0%, 10.2%, 18.0%, 17.9%, and 17.3% higher compared with those in OSLDcont for 75, 80, 90, 95, 100, and 105 kVp, respectively. The dose accumulation and bleaching time did not significantly alter the sensitivity, regardless of the filling of deep traps for all radiation qualities. Both OSLDexp and OSLDcont exhibited good linearity, by showing coefficients determination (R2) > 0.99. The OSL sensitivities can be increased by filling of deep electron/hole traps in the energy ranges between 75 and 105 kVp, and they exhibited no significant variations according to the bleaching time.

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Section: Introductionmentioning

confidence: 99%

Effects of Fully Filling Deep Electron/Hole Traps in Optically Stimulated Luminescence Dosimeters in the Kilovoltage Energy Range

et al. 2022

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“…15,18,19 However, a single backscatter factor does not describe the scatter distribution sufficiently well as scatter may extend beyond the collimated area. 20 In addition, scatter decreases toward the border of the irradiated area. 21 Therefore, studies propose the expansion of scalar backscatter factors to more generic precomputed two-dimensional (2D) point spread functions (PSF) 21,22 or three-dimensional (3D) dose point kernels (DPK).…”

Section: Introductionmentioning

confidence: 99%

“…Backscatter factors have been estimated using both empiric and Monte Carlo (MC) methods and are usually modeled as a scalar factor with respect to the central x‐ray beam . 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 .…”

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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“…2–4 However, the backscatter distribution is not uniform across the entire field, but decreases towards the edge of the field and extends beyond the collimation. 5 Moreover a single backscatter factor is not sufficient to describe the backscatter variation across the entire field, when the primary dose distribution is not uniform. X-ray field shaping devices like region of interest (ROI) beam attenuators and compensation filters modify the photon fluence in the field of view by attenuating the primary × rays outside the region of interest.…”

Section: Introductionmentioning

confidence: 99%

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

Vijayan

Xiong

Rudin

et al. 2017

Medical Imaging 2017: Physics of Medical Imaging

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Beam shaping devices like ROI attenuators and compensation filters modulate the intensity distribution of the x-ray beam incident on the patient. This results in a spatial variation of skin dose due to the variation of primary radiation and also a variation in backscattered radiation from the patient. To determine the backscatter component, backscatter point spread functions (PSF) are generated using EGS Monte-Carlo software. For this study, PSF’s were determined by simulating a 1 mm beam incident on the lateral surface of an anthropomorphic head phantom and a 20 cm thick PMMA block phantom. The backscatter PSF’s for the head phantom and PMMA phantom are curve fit with a Lorentzian function after being normalized to the primary dose intensity (PSFn). PSFn is convolved with the primary dose distribution to generate the scatter dose distribution, which is added to the primary to obtain the total dose distribution. The backscatter convolution technique is incorporated in the dose tracking system (DTS), which tracks skin dose during fluoroscopic procedures and provides a color map of the dose distribution on a 3D patient graphic model. A convolution technique is developed for the backscatter dose determination for the non-uniformly spaced graphic-model surface vertices. A Gafchromic film validation was performed for shaped x-ray beams generated with an ROI attenuator and with two compensation filters inserted into the field. The total dose distribution calculated by the backscatter convolution technique closely agreed with that measured with the film.

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A system to track skin dose for neuro-interventional cone-beam computed tomography (CBCT)

Cited by 6 publications

References 9 publications

Effects of Fully Filling Deep Electron/Hole Traps in Optically Stimulated Luminescence Dosimeters in the Kilovoltage Energy Range

Effects of Fully Filling Deep Electron/Hole Traps in Optically Stimulated Luminescence Dosimeters in the Kilovoltage Energy Range

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

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

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