Monte Carlo validation experiments for the gas Cherenkov detectors at the National Ignition Facility and Omega

2013 ◽  
Vol 84 (7) ◽  
pp. 073504 ◽  
Author(s):  
M. S. Rubery ◽  
C. J. Horsfield ◽  
H. Herrmann ◽  
Y. Kim ◽  
J. M. Mack ◽  
...  
Keyword(s):  
Monte Carlo ◽  
National Ignition Facility ◽  
Cherenkov Detectors ◽  
Validation Experiments

scholarly journals Towards computer-aided severity assessment via deep neural networks for geographic and opacity extent scoring of SARS-CoV-2 chest X-rays

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
A. Wong ◽  
Z. Q. Lin ◽  
L. Wang ◽  
A. G. Chung ◽  
B. Shen ◽  
...  
Keyword(s):  
Neural Networks ◽  
Monte Carlo ◽  
Lung Disease ◽  
Disease Severity ◽  
Deep Neural Networks ◽  
Cross Validation ◽  
X Rays ◽  
Computer Aided ◽  
Monte Carlo Cross Validation ◽  
Validation Experiments

AbstractA critical step in effective care and treatment planning for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause for the coronavirus disease 2019 (COVID-19) pandemic, is the assessment of the severity of disease progression. Chest x-rays (CXRs) are often used to assess SARS-CoV-2 severity, with two important assessment metrics being extent of lung involvement and degree of opacity. In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system. Data consisted of 396 CXRs from SARS-CoV-2 positive patient cases. Geographic extent and opacity extent were scored by two board-certified expert chest radiologists (with 20+ years of experience) and a 2nd-year radiology resident. The deep neural networks used in this study, which we name COVID-Net S, are based on a COVID-Net network architecture. 100 versions of the network were independently learned (50 to perform geographic extent scoring and 50 to perform opacity extent scoring) using random subsets of CXRs from the study, and we evaluated the networks using stratified Monte Carlo cross-validation experiments. The COVID-Net S deep neural networks yielded R$$^2$$ 2 of $$0.664 \pm 0.032$$ 0.664 ± 0.032 and $$0.635 \pm 0.044$$ 0.635 ± 0.044 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively, in stratified Monte Carlo cross-validation experiments. The best performing COVID-Net S networks achieved R$$^2$$ 2 of 0.739 and 0.741 between predicted scores and radiologist scores for geographic extent and opacity extent, respectively. The results are promising and suggest that the use of deep neural networks on CXRs could be an effective tool for computer-aided assessment of SARS-CoV-2 lung disease severity, although additional studies are needed before adoption for routine clinical use.

scholarly journals Progress on next generation gamma-ray Cherenkov detectors for the National Ignition Facility

2018 ◽  
Vol 89 (10) ◽  
pp. 10I148 ◽  
Author(s):  
H. W. Herrmann ◽  
Y. H. Kim ◽  
A. B. Zylstra ◽  
H. Geppert-Kleinrath ◽  
K. D. Meaney ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Next Generation ◽  
National Ignition Facility ◽  
Cherenkov Detectors

Monte Carlo Uncertainty Analyses of Pulsed Activation in the National Ignition Facility Gunite Shielding

2003 ◽  
Vol 43 (3) ◽  
pp. 473-477 ◽  
Author(s):  
Javier Sanz ◽  
Rafael Falquina ◽  
Arturo Rodriguez ◽  
Oscar Cabellos ◽  
Susana Reyes ◽  
...  
Keyword(s):  
Monte Carlo ◽  
National Ignition Facility ◽  
Uncertainty Analyses

Development of an ultra-fast photomultiplier tube for gamma-ray Cherenkov detectors at the National Ignition Facility (PD-PMT)

2018 ◽  
Vol 89 (10) ◽  
pp. 10I137 ◽  
Author(s):  
A. K. L. Dymoke-Bradshaw ◽  
J. D. Hares ◽  
J. Milnes ◽  
H. W. Herrmann ◽  
C. J. Horsfield ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Photomultiplier Tube ◽  
National Ignition Facility ◽  
Cherenkov Detectors

scholarly journals Direct Annihilation Position Classification Based on Deep Learning Using Paired Cherenkov Detectors: A Monte Carlo Study

2020 ◽  
Vol 10 (22) ◽  
pp. 7957
Author(s):  
Kibo Ote ◽  
Ryosuke Ota ◽  
Fumio Hashimoto ◽  
Tomoyuki Hasegawa
Keyword(s):  
Monte Carlo ◽  
Deep Learning ◽  
Single Photon ◽  
Three Dimensional ◽  
Monte Carlo Study ◽  
Point Sources ◽  
Cherenkov Detector ◽  
Cherenkov Detectors ◽  
Dimensional Interaction ◽  
Interaction Position

To apply deep learning to estimate the three-dimensional interaction position of a Cherenkov detector, an experimental measurement of the true depth of interaction is needed. This requires significant time and effort. Therefore, in this study, we propose a direct annihilation position classification method based on deep learning using paired Cherenkov detectors. The proposed method does not explicitly estimate the interaction position or time-of-flight information and instead directly estimates the annihilation position from the raw data of photon information measured by paired Cherenkov detectors. We validated the feasibility of the proposed method using Monte Carlo simulation data of point sources. A total of 125 point sources were arranged three-dimensionally with 5 mm intervals, and two Cherenkov detectors were placed face-to-face, 50 mm apart. The Cherenkov detector consisted of a monolithic PbF2 crystal with a size of 40 × 40 × 10 mm3 and a photodetector with a single photon time resolution (SPTR) of 0 to 100 picosecond (ps) and readout pitch of 0 to 10 mm. The proposed method obtained a classification accuracy of 80% and spatial resolution with a root mean square error of less than 1.5 mm when the SPTR was 10 ps and the readout pitch was 3 mm.

scholarly journals Next generation gamma-ray Cherenkov detectors for the National Ignition Facility

2016 ◽  
Vol 87 (11) ◽  
pp. 11E732 ◽  
Author(s):  
H. W. Herrmann ◽  
Y. H. Kim ◽  
A. M. McEvoy ◽  
A. B. Zylstra ◽  
C. S. Young ◽  
...  
Keyword(s):  
Gamma Ray ◽  
Next Generation ◽  
National Ignition Facility ◽  
Cherenkov Detectors
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