How accurately can the peak skin dose in fluoroscopy be determined using indirect dose metrics?

Jones, A. Kyle; Ensor, Joe; Pasciak, Alexander S.

doi:10.1118/1.4884020

Cited by 33 publications

(23 citation statements)

References 19 publications

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“…Limitations of this study include comparison of multi-vendor systems at a single site, no calculation of peak skin dose to the patient, subjective image quality assessment, retrospective study design, and lack of proprietary information about the dose modulation and image processing features on Allura-ClarityIQ and Artis Q systems (that contributed to the reported differences in radiation dose and image quality). Peak skin dose calculation is based on CAK values reported by the system taking into account other factors like source to detector distance (24), but these methods have been shown to have uncertainty as high as 50% (25). In the present study, impact of differences in source to detector distances (a factor of 1.1) was less than the observed differences in median CAK values (45%) for the subset study.…”

Section: Discussionmentioning

confidence: 53%

Investigation of Radiation Dose Estimates and Image Quality Between Commercially Available Interventional Fluoroscopy Systems for Fluoroscopically Guided Interventional Procedures

et al. 2021

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Rationale and Objectives: To investigate differences in radiation dose and image quality for single-plane flat-panel-detector based interventional fluoroscopy systems from two vendors using phantom study and clinical procedures. Materials and Methods: AlluraClarityIQ (Philips) and Artis Q (Siemens-Healthineers) interventional fluoroscopy systems were evaluated. Phantom study included comparison of system-reported air-kerma rates (AKR) for clinical protocols with simulated patient thicknesses (20À40 cm). Differences in system-reported radiation dose estimates, cumulative-air-kerma (CAK) and kerma-area-product (KAP), for different clinical procedures were investigated. Subset analysis investigated differences in CAK, KAP and other factors affecting radiation dose when the same patients underwent repeat embolization procedures performed by the same physician on the two different fluoroscopy systems. Two blinded interventional radiologists reviewed image-quality for these procedures using a five-point scale (1À5; 5-best) for five parameters. Results: Phantom study revealed that air-kerma rates was significantly higher for Artis Q system for 30À40cm of simulated patient thicknesses (p < 0.05). Overall data analysis from 4381 clinical cases revealed significant differences in CAK and KAP for certain procedures (p < 0.05); with significantly lower values for AlluraClarityIQ systems (median CAK lower by: 29%À58%). Subset analysis with 40 patients undergoing repeat embolization procedures on both systems revealed that median CAK and KAP were significantly lower for AlluraClari-tyIQ systems (p < 0.02) by 45% and 31%, respectively. Image quality scores for AlluraClarityIQ systems were significantly greater (mean difference range for five parameters: 1.3À1.6; p < 0.005). Conclusion: Radiation dose and image quality differences were observed between AlluraClarityIQ and Artis Q systems. AlluraClarityIQ systems showed lower radiation utilization and an increase in subjective perception of image quality.

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Section: Discussionmentioning

confidence: 53%

Investigation of Radiation Dose Estimates and Image Quality Between Commercially Available Interventional Fluoroscopy Systems for Fluoroscopically Guided Interventional Procedures

et al. 2021

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“…The dose deposited in the skin of the head of the patient can only be estimated with an error of up to 22.4%. This error, however, is smaller than the uncertainties associated with experimental film dosimetry; therefore, conventional skin dosimetry is in general expected to be unlikely to be more accurate than 50% . The higher errors at the head might be related to differences in material composition, high quantity of irradiated air, and overall less irradiation of the phantom concerning the head.…”

Section: Discussionmentioning

confidence: 95%

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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“…Currently, PSD is usually determined indirectly from measured quantities such as reference air kerma (K a,r ) or kerma area product (KAP) [4]. Although this approach estimates PSD in real-time, its accuracy is limited as PSD depends on several parameters such as patient size, type of intervention and gantry angulation, and therefore bears large uncertainty [5][6][7].…”

Section: Aims and Objectivesmentioning

confidence: 99%

Monte Carlo simulation of a prototypical patient dosimetry system for fluoroscopic procedures

et al. 2015

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The purpose of this study is to investigate feasibility of a novel real-time dosimetry method for fluoroscopically guided interventions utilizing thin-film detector arrays in several potential locations with respect to the patient and x-ray equipment. We employed Monte Carlo (MC) simulation to establish the fluoroscopic beam model to determine dosimetric quantities directly from measured doses in thin-film detector arrays at three positions: A-attached to the x-ray source, B-on the couch under the patient and C-attached to the fluoroscopic imager. Next, we developed a calibration method to determine skin dose at the entry of the beam ([Formula: see text]) as well as the dose distribution along each ray of the beam in a water-equivalent patient model. We utilized the concept of water-equivalent thickness to determine the dose inside the patient based on doses measured outside of the patient by the thin-film detector array layers: (a) A, (b) B, or (c) B and C. In the process of calibration we determined a correction factor that characterizes the material-specific response of the detector, backscatter factor and attenuation factor for slab water phantoms of various thicknesses. Application of this method to an anthropomorphic phantom showed accuracy of about 1% for [Formula: see text] and up to about 10% for integral dose along the beam path when compared to a direct simulation of dose by MC.

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How accurately can the peak skin dose in fluoroscopy be determined using indirect dose metrics?

Cited by 33 publications

References 19 publications

Investigation of Radiation Dose Estimates and Image Quality Between Commercially Available Interventional Fluoroscopy Systems for Fluoroscopically Guided Interventional Procedures

Investigation of Radiation Dose Estimates and Image Quality Between Commercially Available Interventional Fluoroscopy Systems for Fluoroscopically Guided Interventional Procedures

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

Monte Carlo simulation of a prototypical patient dosimetry system for fluoroscopic procedures

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