The irradiation system and dose estimation joint-system for NCT wider application in Kyoto University

2004 ◽  
Vol 61 (5) ◽  
pp. 829-833 ◽  
Author(s):  
Y Sakurai ◽  
A Maruhashi ◽  
K Ono
Radiation ◽  
2021 ◽  
Vol 1 (2) ◽  
pp. 79-94
Author(s):  
Peter K. Rogan ◽  
Eliseos J. Mucaki ◽  
Ben C. Shirley ◽  
Yanxin Li ◽  
Ruth C. Wilkins ◽  
...  

The dicentric chromosome (DC) assay accurately quantifies exposure to radiation; however, manual and semi-automated assignment of DCs has limited its use for a potential large-scale radiation incident. The Automated Dicentric Chromosome Identifier and Dose Estimator (ADCI) software automates unattended DC detection and determines radiation exposures, fulfilling IAEA criteria for triage biodosimetry. This study evaluates the throughput of high-performance ADCI (ADCI-HT) to stratify exposures of populations in 15 simulated population scale radiation exposures. ADCI-HT streamlines dose estimation using a supercomputer by optimal hierarchical scheduling of DC detection for varying numbers of samples and metaphase cell images in parallel on multiple processors. We evaluated processing times and accuracy of estimated exposures across census-defined populations. Image processing of 1744 samples on 16,384 CPUs required 1 h 11 min 23 s and radiation dose estimation based on DC frequencies required 32 sec. Processing of 40,000 samples at 10 exposures from five laboratories required 25 h and met IAEA criteria (dose estimates were within 0.5 Gy; median = 0.07). Geostatistically interpolated radiation exposure contours of simulated nuclear incidents were defined by samples exposed to clinically relevant exposure levels (1 and 2 Gy). Analysis of all exposed individuals with ADCI-HT required 0.6–7.4 days, depending on the population density of the simulation.


Atmosphere ◽  
2021 ◽  
Vol 12 (2) ◽  
pp. 268
Author(s):  
Todd C. Harris ◽  
Laurent Vuilleumier ◽  
Claudine Backes ◽  
Athanasios Nenes ◽  
David Vernez

Epidemiology and public health research relating to solar ultraviolet (UV) exposure usually relies on dosimetry to measure UV doses received by individuals. However, measurement errors affect each dosimetry measurement by unknown amounts, complicating the analysis of such measurements and their relationship to the underlying population exposure and the associated health outcomes. This paper presents a new approach to estimate UV doses without the use of dosimeters. By combining new satellite-derived UV data to account for environmental factors and simulation-based exposure ratio (ER) modelling to account for individual factors, we are able to estimate doses for specific exposure periods. This is a significant step forward for alternative dosimetry techniques which have previously been limited to annual dose estimation. We compare our dose estimates with dosimeter measurements from skiers and builders in Switzerland. The dosimetry measurements are expected to be slightly below the true doses due to a variety of dosimeter-related measurement errors, mostly explaining why our estimates are greater than or equal to the corresponding dosimetry measurements. Our approach holds much promise as a low-cost way to either complement or substitute traditional dosimetry. It can be applied in a research context, but is also fundamentally well-suited to be used as the basis for a dose-estimating mobile app that does not require an external device.


2013 ◽  
Vol 16 (10) ◽  
pp. 1749-1761 ◽  
Author(s):  
Xiaodun Wang ◽  
Kai Weng ◽  
Hongbo Liu ◽  
Yulan Zhang ◽  
Zhihua Chen

Author(s):  
David Endesfelder ◽  
Ursula Oestreicher ◽  
Ulrike Kulka ◽  
Elizabeth A. Ainsbury ◽  
Jayne Moquet ◽  
...  

2013 ◽  
Vol 6 (2) ◽  
pp. 349-355 ◽  
Author(s):  
Kohei Kawasaki ◽  
Masaharu Imazeki ◽  
Ryota Hasegawa ◽  
Shinichi Shiba ◽  
Hiroyuki Takahashi ◽  
...  

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