SimDoseCT: dose reporting software based on Monte Carlo simulation for a 320 detector-row cone-beam CT scanner and ICRP computational adult phantoms

Cros, M.; Joemai, Raoul M. S.; Geleijns, Jacob; Molina, Diego M.

doi:10.1088/1361-6560/aa77ea

Cited by 12 publications

(9 citation statements)

References 29 publications

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“…However, owing to the large number of CT examinations performed annually (more than 100 million worldwide), even small risks may translate into a large‐scaled number of future cancers. In light of these risks, accurate estimation of the absorbed dose profile and associated risk factors for the exposed patients in CT examinations is necessary . Different approaches have been adopted to estimate the absorbed dose to patients from CT scans, including experimental measurements using dosimeters embedded within physical anthropomorphic phantoms and Monte Carlo calculations using computational models.…”

Section: Introductionmentioning

confidence: 99%

“…In light of these risks, accurate estimation of the absorbed dose profile and associated risk factors for the exposed patients in CT examinations is necessary. 6,7 Different approaches have been adopted to estimate the absorbed dose to patients from CT scans, including experimental measurements using dosimeters embedded within physical anthropomorphic phantoms and Monte Carlo calculations using computational models. However, these approaches inherently bear a number of limitations including the difficulty of matching physical phantoms to the location of internal organs within the patient's body, the heavy workload involved for constructing patient-specific computational models and the inherent assumptions in measurements and simulation setups, which might contribute significant uncertainties to the estimated absorbed dose.…”

Section: Introductionmentioning

confidence: 99%

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Construction of patient‐specific computational models for organ dose estimation in radiological imaging

2019

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Purpose Diagnostic imaging procedures require optimization depending on the medical task at hand, the apparatus being used, and patient physical and anatomical characteristics. The assessment of the radiation dose and associated risks plays a key role in safety and quality management for radiation protection purposes. In this work, we aim at developing a methodology for personalized organ‐level dose assessment in x‐ray computed tomography (CT) imaging. Methods Regional voxel models representing reference patient‐specific computational phantoms were generated through image segmentation of CT images for four patients. The best‐fitting anthropomorphic phantoms were selected from a previously developed comprehensive phantom library according to patient's anthropometric parameters, then registered to the anatomical masks (skeleton, lung, and body contour) of patients to produce a patient‐specific whole‐body phantom. Well‐established image registration metrics including Jaccard's coefficients for each organ, organ mass, body perimeter, organ‐surface distance, and effective diameter are compared between the reference patient model, registered model, and anchor phantoms. A previously validated Monte Carlo code is utilized to calculate the absorbed dose in target organs along with the effective dose delivered to patients. The calculated absorbed doses from the reference patient models are then compared with the produced personalized model, anchor phantom, and those reported by commercial dose monitoring systems. Results The evaluated organ‐surface distance and body effective diameter metrics show a mean absolute difference between patient regional voxel models, serving as reference, and patient‐specific models around 4.4% and 4.5%, respectively. Organ‐level radiation doses of patient‐specific models are in good agreement with those of the corresponding patient regional voxel models with a mean absolute difference of 9.1%. The mean absolute difference of organ doses for the best‐fitting model extracted from the phantom library and Radimetrics™ commercial dose tracking software are 15.5% and 41.1%, respectively. Conclusion The results suggest that the proposed methodology improves the accuracy of organ‐level dose estimation in CT, especially for extreme cases [high body mass index (BMI) and large skeleton]. Patient‐specific radiation dose calculation and risk assessment can be performed using the proposed methodology for both monitoring of cumulative radiation exposure of patients and epidemiological studies. Further validation using a larger database is warranted.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

2019

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“…6 The characteristics of the filter determine to a large extent, the dose distribution in the patient's part being exposed which help in obtaining more accurate dose calculations using Monte Carlo simulations. [7][8][9][10][11][12] In addition, knowledge of the BTF transmission profile can be used in dose reduction simulation and in iterative reconstruction algorithms. [13][14][15] Several methods to measure BTF profiles have been presented.…”

Section: And (C)mentioning

confidence: 99%

“…While some vendors/models use a single shape of a BTF; others utilize various sizes for the different protocols and/or body parts (e.g., head, body, small, medium, large) . The characteristics of the filter determine to a large extent, the dose distribution in the patient's part being exposed which help in obtaining more accurate dose calculations using Monte Carlo simulations . In addition, knowledge of the BTF transmission profile can be used in dose reduction simulation and in iterative reconstruction algorithms …”

Section: Introductionmentioning

confidence: 99%

A method to estimate transmission profiles of bow‐tie filters using rotating tube measurements

Al-Senan

2018

Medical Physics

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Purpose The purpose of this paper was to present a method of determining the dose profile of the beam bow‐tie filter (BTF) without the need for fixing the x ray tube in a position or using special instruments or dosimeters other than the ordinary types of ion chambers used for CT dosimetry (e.g., Farmer chamber). Methods The idea behind this method is to try to invert the integral of exposures from axial measurements by decomposing it into fractions per degree of tube rotation. Measurements of the CT tube output were taken with a full tube rotation while the chamber was fixed in air. Starting with isocenter the output measurements were performed at 1‐cm interval above the isocenter. Measurements were repeated for three sizes of BTFs; small, medium, and large. Maximum fan angle per chamber position was computed and an effective fan angle was defined to account for the new angular range encountered per chamber position. Variation due to inverse‐square law was isolated from each measurement and contribution from the effective fan angle was computed. Resulted profiles from this method were then compared to profiles obtained with direct measurements, when the tube was in a fixed position. Effects of over and under 360° rotation per scan on results accuracy were also investigated. Results Using the direct measurements as the gold standard, results from this method were accurate to 4% for most of the BTFs angular ranges. The average relative error in the small BTF was 1.5%. In the medium BTF, the average relative error was <3% for up to 16° fan angle. With the large BTF, the mean error was about 4% for up to 22°. The relative error appeared to increase at larger fan angles especially with the large BTF; reaching an average of about 32% for fan angles between 22° and 27.5°. Conclusion The presented method is relatively easy to perform and provides BTFs profiles with reasonably good accuracy. Associated errors of >10% only appear in high angles of large and medium BTFs.

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“…Lowering the radiation results in a lower signal-to-noise ratio and therefore in a poorer image quality. The most common way to determine the lowest possible radiation dose in CT protocols is clinical evaluation by physicians (Singh et al 2009, Guimarães et al 2010, Sollmann et al 2019, Cros et al 2017, Mittone et al 2014. One method is to perform repeated scans with different radiation levels on the same patient and select the lowest exposure scan with minimum acceptable information.…”

Section: Introductionmentioning

confidence: 99%

Low-dose x-ray CT simulation from an available higher-dose scan

Elhamiasl¹

2020

Phys. Med. Biol.

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In CT-imaging an optimal compromise between the radiation burden and the image quality for the imaging task is needed. Lower-dose CT is desirable, however, lowering the dose results in a lower signal-to-noise ratio and therefore in a reduced image quality. In this research, we aim to develop a tool to simulate lower-dose scans from an existing standard-dose scan. The main application of this tool is to determine the lowest possible radiation dose that still produces sufficient clinical information. The X-ray tube current reduction is modeled by estimating the noise equivalent number of photons in the high exposure scan and applying a thinning technique to reduce that number. The proposed method accounts for the bowtie filter, for the electronic system noise, for the noise correlation between neighboring detector elements, for the beam hardening effect, and for the non-linear smoothing filter in very low dose scans. Several phantom studies with different acquisition protocols were performed to evaluate the accuracy of the proposed framework. The results demonstrate a close agreement between the noise magnitude and texture of the measured and the simulated lowerdose scans. For instance, the standard deviation of noise in the simulation of lowerdose scans with 90% tube current reduction matches the reconstructions from the real scans with less than 1% and 3% error for sequential and helical scans, respectively. The noise texture was also assessed by analyzing the noise power spectrum of the simulated lower-dose images which matches those from the real scans. Furthermore, the relation between the measured and predicted noise in projection domain is very close to the line of identity which confirms the accuracy of the model.

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SimDoseCT: dose reporting software based on Monte Carlo simulation for a 320 detector-row cone-beam CT scanner and ICRP computational adult phantoms

Cited by 12 publications

References 29 publications

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

Construction of patient‐specific computational models for organ dose estimation in radiological imaging

A method to estimate transmission profiles of bow‐tie filters using rotating tube measurements

Low-dose x-ray CT simulation from an available higher-dose scan

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