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Reconstruction of Radiation Dose Received by Diagnostic Radiologic Technologists in Korea.

ABSTRACT: Diagnostic medical radiation workers in Korea have been officially monitored for their occupational radiation doses since 1996. The purpose of this study was to design models for reconstructing unknown individual radiation doses to which diagnostic radiation technologists were exposed before 1996.Radiation dose reconstruction models were developed by using cross-sectional survey data and the personal badge doses of 8167 radiologic technologists. The models included calendar year and age as predictors, and the participants were grouped into six categories according to their sex and facility type. The annual doses between 1971 and 1995 for those who were employed before 1996 were estimated using these models.The calendar year and age were inversely related to the estimated radiation doses in the models of all six groups. The annual median estimated doses decreased from 9.45 mSv in 1971 to 1.26 mSv in 1995, and the associated dose variation also decreased with time. The estimated median badge doses from 1996 (1.22 mSv) to 2011 (0.30 mSv) were similar to the measured doses (1.68 mSv to 0.21 mSv) for the same years. Similar results were observed for all six groups.The reconstruction models developed in this study may be useful for estimating historical occupational radiation doses received by medical radiologic technologists in Korea.

SUBMITTER: Choi Y

PROVIDER: S-EPMC5066420 | biostudies-other | 2016 Sep

REPOSITORIES: biostudies-other

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Reconstruction of Radiation Dose Received by Diagnostic Radiologic Technologists in Korea.

Choi Yeongchull Y Kim Jaeyoung J Lee Jung Jeung JJ Jun Jae Kwan JK Lee Won Jin WJ

Journal of preventive medicine and public health = Yebang Uihakhoe chi 20160823 5

<h4>Objectives</h4>Diagnostic medical radiation workers in Korea have been officially monitored for their occupational radiation doses since 1996. The purpose of this study was to design models for reconstructing unknown individual radiation doses to which diagnostic radiation technologists were exposed before 1996.<h4>Methods</h4>Radiation dose reconstruction models were developed by using cross-sectional survey data and the personal badge doses of 8167 radiologic technologists. The models incl ...[more]

PMID: 27744670

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Project description:Importance:Diagnostic low-dose ionizing radiation has great medical benefits; however, its increasing use has raised concerns about possible cancer risks. Objective:To examine the risk of cancer after diagnostic low-dose radiation exposure. Design, Setting, and Participants:This population-based cohort study included youths aged 0 to 19 years at baseline from South Korean National Health Insurance System claim records from January 1, 2006, to December 31, 2015. Exposure to diagnostic low-dose ionizing radiation was classified as any that occurred on or after the entry date, when the participant was aged 0 to 19 years, on or before the exit date, and at least 2 years before any cancer diagnosis. Cancer diagnoses were based on International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Data were analyzed from March 2018 to September 2018. Main Outcomes and Measures:The primary analysis assessed the incidence rate ratios (IRRs) for exposed vs nonexposed individuals using the number of person-years as an offset. Results:The cohort included a total of 12 068 821 individuals (6 339 782 [52.5%] boys). There were 2 309 841 individuals (19.1%) aged 0 to 4 years, 2 951 679 individuals (24.5%) aged 5 to 9 years, 3 489 709 individuals (28.9%) aged 10 to 14 years, and 3 317 593 individuals (27.5%) aged 15 to 19 years. Of these, 1 275 829 individuals (10.6%) were exposed to diagnostic low-dose ionizing radiation between 2006 and 2015, and 10 792 992 individuals (89.4%) were not exposed. By December 31, 2015, 21 912 cancers were recorded. Among individuals who had been exposed, 1444 individuals (0.1%) received a cancer diagnosis. The overall cancer incidence was greater among exposed individuals than among nonexposed individuals after adjusting for age and sex (IRR, 1.64 [95% CI, 1.56-1.73]; P < .001). Among individuals who had undergone computed tomography scans in particular, the overall cancer incidence was greater among exposed individuals than among nonexposed individuals after adjusting for age and sex (IRR, 1.54 [95% CI, 1.45-1.63]; P < .001). The incidence of cancer increased significantly for many types of lymphoid, hematopoietic, and solid cancers after exposure to diagnostic low-dose ionizing radiation. Among lymphoid and hematopoietic cancers, incidence of cancer increased the most for other myeloid leukemias (IRR, 2.14 [95% CI, 1.86-2.46]) and myelodysplasia (IRR, 2.48 [95% CI, 1.77-3.47]). Among solid cancers, incidence of cancer increased the most for breast (IRR, 2.32 [95% CI, 1.35-3.99]) and thyroid (IRR, 2.19 [95% CI, 1.97-2.20]) cancers. Conclusions and Relevance:This study found an association of increased incidence of cancer with exposure to diagnostic low-dose ionizing radiation in a large cohort. Given this risk, diagnostic low-dose ionizing radiation should be limited to situations in which there is a definite clinical indication.

Project description:ObjectiveFew studies have explored the clinical feasibility of using deep-learning reconstruction to reduce the radiation dose of CT. We aimed to compare the image quality and lung nodule detectability between chest CT using a quarter of the low dose (QLD) reconstructed with vendor-agnostic deep-learning image reconstruction (DLIR) and conventional low-dose (LD) CT reconstructed with iterative reconstruction (IR).Materials and methodsWe retrospectively collected 100 patients (median age, 61 years [IQR, 53-70 years]) who received LDCT using a dual-source scanner, where total radiation was split into a 1:3 ratio. QLD CT was generated using a quarter dose and reconstructed with DLIR (QLD-DLIR), while LDCT images were generated using a full dose and reconstructed with IR (LD-IR). Three thoracic radiologists reviewed subjective noise, spatial resolution, and overall image quality, and image noise was measured in five areas. The radiologists were also asked to detect all Lung-RADS category 3 or 4 nodules, and their performance was evaluated using area under the jackknife free-response receiver operating characteristic curve (AUFROC).ResultsThe median effective dose was 0.16 (IQR, 0.14-0.18) mSv for QLD CT and 0.65 (IQR, 0.57-0.71) mSv for LDCT. The radiologists' evaluations showed no significant differences in subjective noise (QLD-DLIR vs. LD-IR, lung-window setting; 3.23 ± 0.19 vs. 3.27 ± 0.22; P = .11), spatial resolution (3.14 ± 0.28 vs. 3.16 ± 0.27; P = .12), and overall image quality (3.14 ± 0.21 vs. 3.17 ± 0.17; P = .15). QLD-DLIR demonstrated lower measured noise than LD-IR in most areas (P < .001 for all). No significant difference was found between QLD-DLIR and LD-IR for the sensitivity (76.4% vs. 72.2%; P = .35) or the AUFROCs (0.77 vs. 0.78; P = .68) in detecting Lung-RADS category 3 or 4 nodules. Under a noninferiority limit of -0.1, QLD-DLIR showed noninferior detection performance (95% CI for AUFROC difference, -0.04 to 0.06).ConclusionQLD-DLIR images showed comparable image quality and noninferior nodule detectability relative to LD-IR images.

Project description:BACKGROUND:Timely assessment of cancer risk from current radiation exposure among medical radiation workers can contribute to the development of strategies to prevent excessive occupational radiation exposure. The purpose of the present study is to estimate the lifetime risk of cancers induced by occupational radiation exposure among medical radiation workers. METHODS:Using estimated organ doses and the RadRAT risk assessment tool, the lifetime cancer risk was estimated among medical radiation workers who were enrolled in the Korean National Dose Registry from 1996 to 2011. Median doses were used for estimating the risk because of the skewed distribution of radiation doses. Realistic representative exposure scenarios in the study population based on sex, job start year, and occupational group were created for calculating the lifetime attributable risk (LAR) and lifetime fractional risk (LFR). RESULTS:The mean estimated lifetime cancer risk from occupational radiation exposure varied significantly by sex and occupational group. The highest LAR was observed in male and female radiologic technologists who started work in 1991 (264.4/100,000 and 391.2/100,000, respectively). Female workers had a higher risk of radiation-related excess cancer, although they were exposed to lower radiation doses than male workers. The higher LAR among women was attributable primarily to excess breast and thyroid cancer risks. LARs among men were higher than women in most other cancer sites. With respect to organ sites, LAR of colon cancer (44.3/100,000) was the highest in male radiologic technologists, whereas LAR of thyroid cancer (222.0/100,000) was the highest in female radiologic technologists among workers who started radiologic practice in 1991. Thyroid and bladder cancers had the highest LFR among radiologic technologists. CONCLUSIONS:Our findings provide an assessment of the potential cancer risk from occupational radiation exposure among medical radiation workers, based on current knowledge about radiation risk. Although the radiation-related risk was small in most cases, it varied widely by sex and occupational group, and the risk would be underestimated due to the use of median, rather than mean, doses. Therefore, careful monitoring is necessary to optimize radiation doses and protect medical radiation workers from potential health risks, particularly female radiologic technologists.

Project description:ObjectivesDeep-learning image reconstruction (DLIR) offers unique opportunities for reducing image noise without degrading image quality or diagnostic accuracy in coronary CT angiography (CCTA). The present study aimed at exploiting the capabilities of DLIR to reduce radiation dose and assess its impact on stenosis severity, plaque composition analysis, and plaque volume quantification.MethodsThis prospective study includes 50 patients who underwent two sequential CCTA scans at normal-dose (ND) and lower-dose (LD). ND scans were reconstructed with Adaptive Statistical Iterative Reconstruction-Veo (ASiR-V) 100%, and LD scans with DLIR. Image noise (in Hounsfield units, HU) and quantitative plaque volumes (in mm3) were assessed quantitatively. Stenosis severity was visually categorized into no stenosis (0%), stenosis (< 20%, 20-50%, 51-70%, 71-90%, 91-99%), and occlusion (100%). Plaque composition was classified as calcified, non-calcified, or mixed.ResultsReduction of radiation dose from ND scans with ASiR-V 100% to LD scans with DLIR at the highest level (DLIR-H; 1.4 mSv vs. 0.8 mSv, p < 0.001) had no impact on image noise (28 vs. 27 HU, p = 0.598). Reliability of stenosis severity and plaque composition was excellent between ND scans with ASiR-V 100% and LD scans with DLIR-H (intraclass correlation coefficients of 0.995 and 0.974, respectively). Comparison of plaque volumes using Bland-Altman analysis revealed a mean difference of - 0.8 mm3 (± 2.5 mm3) and limits of agreement between - 5.8 and + 4.1 mm3.ConclusionDLIR enables a reduction in radiation dose from CCTA by 43% without significant impact on image noise, stenosis severity, plaque composition, and quantitative plaque volume.Key points•Deep-learning image reconstruction (DLIR) enables radiation dose reduction by over 40% for coronary computed tomography angiography (CCTA). •Image noise remains unchanged between a normal-dose CCTA reconstructed by ASiR-V and a lower-dose CCTA reconstructed by DLIR. •There is no impact on the assessment of stenosis severity, plaque composition, and quantitative plaque volume between the two scans.

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Reconstruction of Radiation Dose Received by Diagnostic Radiologic Technologists in Korea.

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Reconstruction of Radiation Dose Received by Diagnostic Radiologic Technologists in Korea.

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