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Статті в журналах з теми "Dose Computation"

Автор: Grafiati

Опубліковано: 25 січня 2025

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1

Knöös, T. "3D dose computation algorithms." Journal of Physics: Conference Series 847 (May 2017): 012037. http://dx.doi.org/10.1088/1742-6596/847/1/012037.

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2

Battista, J., J. Chen, S. Sawchuk, and G. Hajdok. "Evolution of 3D X-Ray Dose Computation Algorithms." Journal of Physics: Conference Series 2630, no. 1 (November 1, 2023): 012008. http://dx.doi.org/10.1088/1742-6596/2630/1/012008.

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Анотація:

Abstract Radiation treatment planning of individual cancer patients relies on the accurate computation of dose distributions in irradiated tissue. Inaccurate dose maps have the potential to mislead clinical decision-making and compromise the balance between effective tumour control and side effects in surrounding normal tissue. In the context of this conference, 3D dosimetry is important for the experimental validation of computed dose distributions. Dose computation methods for external beams of high energy x rays have evolved over the past decade with computer simulation models more closely aligned with the fundamental physics of x-ray scattering and absorption in heterogeneous tissue. In this article, we first present a historical review from a Canadian perspective, followed by an introductory intuitive description of contemporary algorithms used in clinical treatment planning: (1) Convolution-superposition algorithm fundamentally based on the Green’s function method; (2) Stochastic Monte Carlo simulation of x-ray interactions in tissue, and (3) Deterministic numerical solution of a system of Boltzmann transport equations. In principle, all these methods solve the same problem of predicting x-ray scattering and absorption in heterogeneous tissue. However, the mathematical tools differ in their approach and approximations to achieve sufficient speed for routine clinical application. In the conclusion of this article, the evolution of 3D x-ray dose computation is summarized, in terms of accuracy and computational speed.

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3

Cutanda Henríquez, Francisco, and Silvia Vargas Castrillón. "Confidence intervals in dose volume histogram computation." Medical Physics 37, no. 4 (March 15, 2010): 1545–53. http://dx.doi.org/10.1118/1.3355888.

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4

Laub, W., M. Alber, M. Birkner, and F. Nüsslin. "Monte Carlo dose computation for IMRT optimization*." Physics in Medicine and Biology 45, no. 7 (June 26, 2000): 1741–54. http://dx.doi.org/10.1088/0031-9155/45/7/303.

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5

Tang, Man-Lai, Karim F. Hirji, and Stein E. Vollset. "Exact power computation for dose—response studies." Statistics in Medicine 14, no. 20 (October 30, 1995): 2261–72. http://dx.doi.org/10.1002/sim.4780142009.

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6

Sandison, G. A., and L. S. Papiez. "Dose computation applications of the electron loss model." Physics in Medicine and Biology 35, no. 7 (July 1, 1990): 979–97. http://dx.doi.org/10.1088/0031-9155/35/7/013.

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7

Mohan, R., C. Chui, and L. Lidofsky. "Differential pencil beam dose computation model for photons." Medical Physics 13, no. 1 (January 1986): 64–73. http://dx.doi.org/10.1118/1.595924.

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8

Siebert, Frank-André, Ping Jiang, Rene Baumann, Gunnar Bockelmann, Susann Bohn, Maike Thieben, and Jürgen Dunst. "Dose Computation of Keloids in Brachytherapy: Tg-43 or Model-Based-Dose-Calculation?" Brachytherapy 15 (May 2016): S149. http://dx.doi.org/10.1016/j.brachy.2016.04.262.

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9

Dray, Nicolas, Nicolas Mary, Cédric Dossat, Jefferson Bourgoin, and Nathalie Chatry. "An overview of last decade’s developments in RayXpert^®, a 3D Monte Carlo code." EPJ Nuclear Sciences & Technologies 10 (2024): 10. http://dx.doi.org/10.1051/epjn/2024013.

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This article provides an overview of the developments made over the last 10 years within RayXpert®, a CAD-based geometry 3D Monte Carlo software developed by TRAD – Tests & Radiations. The main features of RayXpert® are its 3D Monte Carlo engine and its CAD-based geometry. It is also possible to import STEP file, automatically detect overlaps, and perform parallel Monte Carlo computations. During the last 10 years, numerous new features were added to the software: TTB approximation, dose and flux mapping, computation resumption, radioactive decay computation, script support, MPI computation, advanced convergence indicators, etc. New features such as advanced biasing or neutron-induced activation simulation are under development. This article presents the past and future functionalities of RayXpert®.

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10

Panitsa, E., J. C. Rosenwald, and C. Kappas. "Developing a dose-volume histogram computation program for brachytherapy." Physics in Medicine and Biology 43, no. 8 (August 1, 1998): 2109–21. http://dx.doi.org/10.1088/0031-9155/43/8/009.

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11

Wu, Xingen, and Yunping Zhu. "A neural network regression model for relative dose computation." Physics in Medicine and Biology 45, no. 4 (March 9, 2000): 913–22. http://dx.doi.org/10.1088/0031-9155/45/4/307.

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12

Mohan, R., I. Y. Ding, J. Toraskar, C. Chui, L. L. Anderson, and D. Nori. "Computation of radiation dose distributions for shielded cervical applicators." International Journal of Radiation Oncology*Biology*Physics 11, no. 4 (April 1985): 823–30. http://dx.doi.org/10.1016/0360-3016(85)90317-7.

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13

Sopasakis, Pantelis, and Haralambos Sarimveis. "An integer programming approach for optimal drug dose computation." Computer Methods and Programs in Biomedicine 108, no. 3 (December 2012): 1022–35. http://dx.doi.org/10.1016/j.cmpb.2012.06.008.

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14

Hounsell, A. R., and J. M. Wilkinson. "Data For Dose Computation In Treatments With Multileaf Collimators." Journal of Medical Physics 16, no. 2 (1991): 32. http://dx.doi.org/10.4103/0971-6203.50166.

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15

Magro, Giuseppe, Stewart Mein, Benedikt Kopp, Edoardo Mastella, Andrea Pella, Mario Ciocca, and Andrea Mairani. "FRoG dose computation meets Monte Carlo accuracy for proton therapy dose calculation in lung." Physica Medica 86 (June 2021): 66–74. http://dx.doi.org/10.1016/j.ejmp.2021.05.021.

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16

Beam, Andrew L., and Alison A. Motsinger-Reif. "Optimization of Nonlinear Dose- and Concentration-Response Models Utilizing Evolutionary Computation." Dose-Response 9, no. 3 (June 25, 2010): dose—response.0. http://dx.doi.org/10.2203/dose-response.09-030.beam.

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17

Daartz, J., T. Madden, E. Cascio, A. Lalonde, and J. P. Schuemann. "Computation of Voxel-by-Voxel Dose Rates in Patients for Proton Pencil Beam Dose Delivery." International Journal of Radiation Oncology*Biology*Physics 114, no. 3 (November 2022): S140—S141. http://dx.doi.org/10.1016/j.ijrobp.2022.07.606.

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18

Wu, X., J. Y. Ting, A. M. Markoe, H. J. Landy, J. A. Fiedler, and J. Russell. "Stereotactic Dose Computation and Plan Optimization Using the Convolution Theorem." Stereotactic and Functional Neurosurgery 66, no. 1 (1996): 302–8. http://dx.doi.org/10.1159/000099822.

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19

Wang, Yangping, Chong Deng, Lian Li, and Jianwu Dang. "Compute Unified Device Architecture-Based Parallel Dose-Volume Histogram Computation." Journal of Medical Imaging and Health Informatics 5, no. 4 (August 1, 2015): 833–40. http://dx.doi.org/10.1166/jmihi.2015.1466.

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20

Möller, T. R., U. Rosenow, R. E. Bentley, J. R. Cunningham, F. Nüsslin, J. C. Rosenwald, and J. Van de Geijn. "5. Computation of the Absorbed Dose Distribution in a Patient." Reports of the International Commission on Radiation Units and Measurements os-22, no. 1 (December 1987): 19–29. http://dx.doi.org/10.1093/jicru_os22.1.19.

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21

Möller, T. R., U. Rosenow, R. E. Bentley, J. R. Cunningham, F. Nüsslin, J. C. Rosenwald, and J. Van de Geijn. "5. Computation of the Absorbed Dose Distribution in a Patient." Journal of the International Commission on Radiation Units and Measurements os22, no. 1 (December 15, 1987): 19–29. http://dx.doi.org/10.1093/jicru/os22.1.19.

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22

Elbern, Alwin W. "Computation of dose distribution for linear radioactive sources in brachytherapy." Computers in Biology and Medicine 22, no. 4 (July 1992): 263–68. http://dx.doi.org/10.1016/0010-4825(92)90065-u.

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23

Jacques, R., R. Taylor, J. Wong, and T. McNutt. "SU-GG-T-604: GPU-Accelerated KV/MV Dose Computation." Medical Physics 37, no. 6Part25 (June 2010): 3326. http://dx.doi.org/10.1118/1.3469005.

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24

Ambrožič, Klemen, Rosaria Vilarri, Paola Batistoni, and Luka Snoj. "APPLICATION OF THE JSIR2S CODE PACKAGE FOR SHUTDOWN DOSE RATE CALCULATIONS ON JET." EPJ Web of Conferences 247 (2021): 06050. http://dx.doi.org/10.1051/epjconf/202124706050.

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Анотація:

In this paper we present a computational exercise for shut-down dose rate calculations for the JET tokamak using the in-house developed JSIR2S code package as part of its validation. The computation is performed in two parts: neutron transport and transport of secondary gamma radiation. In order to calculate neutron activation reaction rates with sufficiently low variance, hybrid variance reduction techniques using the ADVANTG code have been utilized. Probability based sampling of secondary source particles was performed. Calculated gamma dose rates after shut down are compared with dose rate measurements performed on site using ionization chambers. The C/E agreement for 1st octant is between 0.8 to 1 while statistically meaningfull results for the 2nd octant are yet to be obtained.

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25

Mettivier, Giovanni, Antonio Sarno, Youfang Lai, Bruno Golosio, Viviana Fanti, Maria Elena Italiano, Xun Jia, and Paolo Russo. "Virtual Clinical Trials in 2D and 3D X-ray Breast Imaging and Dosimetry: Comparison of CPU-Based and GPU-Based Monte Carlo Codes." Cancers 14, no. 4 (February 17, 2022): 1027. http://dx.doi.org/10.3390/cancers14041027.

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Computational reproductions of medical imaging tests, a form of virtual clinical trials (VCTs), are increasingly being used, particularly in breast imaging research. The accuracy of the computational platform that is used for the imaging and dosimetry simulation processes is a fundamental requirement. Moreover, for practical usage, the imaging simulation computation time should be compatible with the clinical workflow. We compared three different platforms for in-silico X-ray 3D breast imaging: the Agata (University & INFN Napoli) that was based on the Geant4 toolkit and running on a CPU-based server architecture; the XRMC Monte Carlo (University of Cagliari) that was based on the use of variance reduction techniques, running on a CPU hardware; and the Monte Carlo code gCTD (University of Texas Southwestern Medical Center) running on a single GPU platform with CUDA environment. The tests simulated the irradiation of cylindrical objects as well as anthropomorphic breast phantoms and produced 2D and 3D images and 3D maps of absorbed dose. All the codes showed compatible results in terms of simulated dose maps and imaging values within a maximum discrepancy of 3%. The GPU-based code produced a reduction of the computation time up to factor 104, and so permits real-time VCT studies for X-ray breast imaging.

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26

Lin, Ruitao, Yanhong Zhou, Fangrong Yan, Daniel Li, and Ying Yuan. "BOIN12: Bayesian Optimal Interval Phase I/II Trial Design for Utility-Based Dose Finding in Immunotherapy and Targeted Therapies." JCO Precision Oncology, no. 4 (November 2020): 1393–402. http://dx.doi.org/10.1200/po.20.00257.

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PURPOSE For immunotherapy, such as checkpoint inhibitors and chimeric antigen receptor T-cell therapy, where the efficacy does not necessarily increase with the dose, the maximum tolerated dose may not be the optimal dose for treating patients. For these novel therapies, the objective of dose-finding trials is to identify the optimal biologic dose (OBD) that optimizes patients’ risk-benefit trade-off. METHODS We propose a simple and flexible Bayesian optimal interval phase I/II (BOIN12) trial design to find the OBD that optimizes the risk-benefit trade-off. The BOIN12 design makes the decision of dose escalation and de-escalation by simultaneously taking account of efficacy and toxicity and adaptively allocates patients to the dose that optimizes the toxicity-efficacy trade-off. We performed simulation studies to evaluate the performance of the BOIN12 design. RESULTS Compared with existing phase I/II dose-finding designs, the BOIN12 design is simpler to implement, has higher accuracy to identify the OBD, and allocates more patients to the OBD. One of the most appealing features of the BOIN12 design is that its adaptation rule can be pretabulated and included in the protocol. During the trial conduct, clinicians can simply look up the decision table to allocate patients to a dose without complicated computation. CONCLUSION The BOIN12 design is simple to implement and yields desirable operating characteristics. It overcomes the computational and implementation complexity that plagues existing Bayesian phase I/II dose-finding designs and provides a useful design to optimize the dose of immunotherapy and targeted therapy. User-friendly software is freely available to facilitate the application of the BOIN12 design.

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27

Sullivan, A., and M. Brand. "SU-E-T-806: Very Fast GPU-Based IMPT Dose Computation." Medical Physics 42, no. 6Part25 (June 2015): 3523. http://dx.doi.org/10.1118/1.4925170.

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28

Jelen, U., M. Radon, A. Santiago, A. Wittig, and F. Ammazzalorso. "A Monte Carlo tool for raster-scanning particle therapy dose computation." Journal of Physics: Conference Series 489 (March 24, 2014): 012013. http://dx.doi.org/10.1088/1742-6596/489/1/012013.

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29

Rout, Sabyasachi, D. G. Mishra, P. M. Ravi, Vandana Pulhani, and R. M. Tripathi. "RADCOM: Radiation dose computation model- a software for radiological impact assessment." Progress in Nuclear Energy 118 (January 2020): 103141. http://dx.doi.org/10.1016/j.pnucene.2019.103141.

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30

Jacques, Robert, John Wong, Russell Taylor, and Todd McNutt. "Real-time dose computation: GPU-accelerated source modeling and superposition/convolution." Medical Physics 38, no. 1 (December 21, 2010): 294–305. http://dx.doi.org/10.1118/1.3483785.

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31

Tripathi, H. B. "A General Formulation For Depth Dose Computation In Photon Beam Dosimetry." Journal of Medical Physics 11, no. 3 (1986): 12. http://dx.doi.org/10.4103/0971-6203.50285.

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32

Mishev, Alexander, Sasu Tuohino, and Ilya Usoskin. "Neutron monitor count rate increase as a proxy for dose rate assessment at aviation altitudes during GLEs." Journal of Space Weather and Space Climate 8 (2018): A46. http://dx.doi.org/10.1051/swsc/2018032.

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Radiation exposure due to cosmic rays, specifically at cruising aviation altitudes, is an important topic in the field of space weather. While the effect of galactic cosmic rays can be easily assessed on the basis of recent models, estimate of the dose rate during strong solar particle events is rather complicated and time consuming. Here we compute the maximum effective dose rates at a typical commercial flight altitude of 35 kft (≈11 000 m above sea level) during ground level enhancement events, where the necessary information, namely derived energy/rigidity spectra of solar energetic particles, is available. The computations are carried out using different reconstructions of the solar proton spectra, available in bibliographic sources, leading to multiple results for some events. The computations were performed employing a recent model for effective dose and/or ambient dose equivalent due to cosmic ray particles. A conservative approach for the computation was assumed. A highly significant correlation between the maximum effective dose rate and peak NM count rate increase during ground level enhancement events is derived. Hence, we propose to use the peak NM count rate increase as a proxy in order to assess the peak effective dose rate at flight altitude during strong solar particle events using the real time records of the worldwide global neutron monitor network.

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33

Böhlen, T. T., R. Dreindl, J. Osorio, G. Kragl, and M. Stock. "PO-0800: Log file based performance characterization of a PBS dose delivery system with dose re-computation." Radiotherapy and Oncology 123 (May 2017): S426—S427. http://dx.doi.org/10.1016/s0167-8140(17)31237-9.

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34

Liang, J., D. Yan, and Y. Chi. "SU-GG-T-36: Influence of Dose Grid Resolution in Cumulative Dose Computation for 4D Inverse Planning." Medical Physics 37, no. 6Part15 (June 2010): 3192. http://dx.doi.org/10.1118/1.3468422.

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35

Meer, Marjolein C., Peter A. N. Bosman, Bradley R. Pieters, Yury Niatsetski, Niek Wieringen, Tanja Alderliesten, and Arjan Bel. "Sensitivity of dose‐volume indices to computation settings in high‐dose‐rate prostate brachytherapy treatment plan evaluation." Journal of Applied Clinical Medical Physics 20, no. 4 (March 18, 2019): 66–74. http://dx.doi.org/10.1002/acm2.12563.

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36

Satoh, Daiki, Hiromasa Nakayama, Takuya Furuta, Tamotsu Yoshihiro, and Kensaku Sakamoto. "Simulation code for estimating external gamma-ray doses from a radioactive plume and contaminated ground using a local-scale atmospheric dispersion model." PLOS ONE 16, no. 1 (January 25, 2021): e0245932. http://dx.doi.org/10.1371/journal.pone.0245932.

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In this study, we developed a simulation code powered by lattice dose-response functions (hereinafter SIBYL), which helps in the quick and accurate estimation of external gamma-ray doses emitted from a radioactive plume and contaminated ground. SIBYL couples with atmospheric dispersion models and calculates gamma-ray dose distributions inside a target area based on a map of activity concentrations using pre-evaluated dose-response functions. Moreover, SIBYL considers radiation shielding due to obstructions such as buildings. To examine the reliability of SIBYL, we investigated five typical cases for steady-state and unsteady-state plume dispersions by coupling the Gaussian plume model and the local-scale high-resolution atmospheric dispersion model using large eddy simulation. The results of this coupled model were compared with those of full Monte Carlo simulations using the particle and heavy-ion transport code system (PHITS). The dose-distribution maps calculated using SIBYL differed by up to 10% from those calculated using PHITS in most target locations. The exceptions were locations far from the radioactive contamination and those behind the intricate structures of building arrays. In addition, SIBYL’s computation time using 96 parallel processing elements was several tens of minutes even for the most computationally expensive tasks of this study. The computation using SIBYL was approximately 100 times faster than the same calculation using PHITS under the same computation conditions. From the results of the case studies, we concluded that SIBYL can estimate a ground-level dose-distribution map within one hour with accuracy that is comparable to that of the full Monte Carlo simulation.

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37

Zhang, Libo, Benqiang Yang, Zhikun Zhuang, Yining Hu, Yang Chen, Limin Luo, and Huazhong Shu. "Optimized Parallelization for Nonlocal Means Based Low Dose CT Image Processing." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/790313.

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Low dose CT (LDCT) images are often significantly degraded by severely increased mottled noise/artifacts, which can lead to lowered diagnostic accuracy in clinic. The nonlocal means (NLM) filtering can effectively remove mottled noise/artifacts by utilizing large-scale patch similarity information in LDCT images. But the NLM filtering application in LDCT imaging also requires high computation cost because intensive patch similarity calculation within a large searching window is often required to be used to include enough structure-similarity information for noise/artifact suppression. To improve its clinical feasibility, in this study we further optimize the parallelization of NLM filtering by avoiding the repeated computation with the row-wise intensity calculation and the symmetry weight calculation. The shared memory with fastI/Ospeed is also used in row-wise intensity calculation for the proposed method. Quantitative experiment demonstrates that significant acceleration can be achieved with respect to the traditional straight pixel-wise parallelization.

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38

Devine, R. T. "Computation of cross sections and dose conversion factors for criticality accident dosimetry." Radiation Protection Dosimetry 110, no. 1-4 (August 1, 2004): 491–95. http://dx.doi.org/10.1093/rpd/nch381.

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39

Sechopoulos, Ioannis, Sankararaman Suryanarayanan, Srinivasan Vedantham, Carl D'Orsi, and Andrew Karellas. "Computation of the glandular radiation dose in digital tomosynthesis of the breast." Medical Physics 34, no. 1 (December 20, 2006): 221–32. http://dx.doi.org/10.1118/1.2400836.

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40

Alber, M., N. Saito, and M. Söhn. "EP-1786 Towards real-time Monte Carlo dose computation: muscle or brain?" Radiotherapy and Oncology 133 (April 2019): S966—S967. http://dx.doi.org/10.1016/s0167-8140(19)32206-6.

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41

Mejaddem, Y., Dž Belkić, A. Brahme, and S. Hyödynmaa. "Development of the electron transport theory and absorbed dose computation in matter." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms 187, no. 4 (April 2002): 499–524. http://dx.doi.org/10.1016/s0168-583x(01)01156-9.

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42

Beilla, Sara, Tony Younes, Laure Vieillevigne, Manuel Bardies, Xavier Franceries, and Luc Simon. "Monte-Carlo dose computation in radiotherapy for lung at very low density." Physica Medica 32 (September 2016): 245–46. http://dx.doi.org/10.1016/j.ejmp.2016.07.519.

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43

Myronakis, Marios E., Marketa Zvelebil, and Dimitra G. Darambara. "Normalized mean glandular dose computation from mammography using GATE: a validation study." Physics in Medicine and Biology 58, no. 7 (March 11, 2013): 2247–65. http://dx.doi.org/10.1088/0031-9155/58/7/2247.

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44

Chen, Z., J. Deng, D. Carlson, K. Roberts, R. Decker, S. Rockwell, and R. Nath. "A Serial-imaging Based 4D Dose Computation System for Prostate Implant Dosimetry." International Journal of Radiation Oncology*Biology*Physics 75, no. 3 (November 2009): S349. http://dx.doi.org/10.1016/j.ijrobp.2009.07.800.

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45

Guo, Jiahao, Xinlei Li, Yidi Wang, Han Gao, Xianghui Kong, Tao Wu, Xinjie Wang, et al. "Application of phase space file secondary computation method in cell dose distribution." Radiation Physics and Chemistry 226 (January 2025): 112301. http://dx.doi.org/10.1016/j.radphyschem.2024.112301.

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46

Nojiri, Mai, Takushi Takata, Naonori Hu, Yoshinori Sakurai, Minoru Suzuki, and Hiroki Tanaka. "Development and evaluation of dose calculation algorithm with a combination of Monte Carlo and point-kernel methods for boron neutron capture therapy." Biomedical Physics & Engineering Express 9, no. 3 (April 6, 2023): 035025. http://dx.doi.org/10.1088/2057-1976/acc33c.

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Анотація:

Abstract We developed a ‘hybrid algorithm’ that combines the Monte Carlo (MC) and point-kernel methods for fast dose calculation in boron neutron capture therapy. The objectives of this study were to experimentally verify the hybrid algorithm and to verify the calculation accuracy and time of a ‘complementary approach’ adopting both the hybrid algorithm and the full-energy MC method. In the latter verification, the results were compared with those obtained using the full-energy MC method alone. In the hybrid algorithm, the moderation process of neutrons is simulated using only the MC method, and the thermalization process is modeled as a kernel. The thermal neutron fluxes calculated using only this algorithm were compared with those measured in a cubic phantom. In addition, a complementary approach was used for dose calculation in a geometry simulating the head region, and its computation time and accuracy were verified. The experimental verification indicated that the thermal neutron fluxes calculated using only the hybrid algorithm reproduced the measured values at depths exceeding a few centimeters, whereas they overestimated those at shallower depths. Compared with the calculation using only the full-energy MC method, the complementary approach reduced the computation time by approximately half, maintaining nearly same accuracy. When focusing on the calculation only using the hybrid algorithm only for the boron dose attributed to the reaction of thermal neutrons, the computation time was expected to reduce by 95% compared with the calculation using only the full-energy MC method. In conclusion, modeling the thermalization process as a kernel was effective for reducing the computation time.

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47

TAKAHASHI, KENICHI, HIDEYO ISHIGAKI, KIMIO UDAGAWA, MASAMI SAITO, and KYOKO YAMAGUCHI. "COMPUTATION OF DOSE DISTRIBUTION BY A PERSONAL COMPUTER FOR THE EXTERNAL BEAM IRRADIATION." Japanese Journal of Radiological Technology 43, no. 10 (1987): 1529–35. http://dx.doi.org/10.6009/jjrt.kj00001363744.

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48

Panitsa, E., J. C. Rosenwald, and C. Kappas. "Quality control of dose volume histogram computation characteristics of 3D treatment planning systems." Physics in Medicine and Biology 43, no. 10 (October 1, 1998): 2807–16. http://dx.doi.org/10.1088/0031-9155/43/10/010.

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49

Kim, Chankyu, Hyunjun Yoo, Yewon Kim, Myungkook Moon, Jong Yul Kim, Dong Uk Kang, Daehee Lee, et al. "CALCULATION OF GAMMA SPECTRA IN A PLASTIC SCINTILLATOR FOR ENERGY CALIBRATIONAND DOSE COMPUTATION." Radiation Protection Dosimetry 170, no. 1-4 (April 28, 2016): 377–81. http://dx.doi.org/10.1093/rpd/ncw086.

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50

Mishev, Alexander, and Ilya Usoskin. "Numerical model for computation of effective and ambient dose equivalent at flight altitudes." Journal of Space Weather and Space Climate 5 (2015): A10. http://dx.doi.org/10.1051/swsc/2015011.

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