Статті в журналах: "Distributions de dose"

1

Beckett, Craig, and Peter Dickof. "Mapping dose distributions." Medical Physics 25, no. 10 (October 1998): 1944–53. http://dx.doi.org/10.1118/1.598384.

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2

Dutreix, Andree. "3D Dose Distributions." Journal of Medical Physics 11, no. 3 (1986): 166. http://dx.doi.org/10.4103/0971-6203.50340.

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3

Pike, G. Bruce, Ervin B. Podgorsak, Terence M. Peters, Conrado Pla, André Olivier, and Luis Souhami. "Dose distributions in radiosurgery." Medical Physics 17, no. 2 (March 1990): 296–304. http://dx.doi.org/10.1118/1.596508.

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4

Bak, Jino, Jin Hwa Choi, Jae-Sung Kim, and Suk Won Park. "Modified dose difference method for comparing dose distributions." Journal of Applied Clinical Medical Physics 13, no. 2 (March 2012): 73–80. http://dx.doi.org/10.1120/jacmp.v13i2.3616.

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5

Low, Daniel A., Delphine Morele, Philip Chow, Tai H. Dou та Tao Ju. "Does the γ dose distribution comparison technique default to the distance to agreement test in clinical dose distributions?" Medical Physics 40, № 7 (18 червня 2013): 071722. http://dx.doi.org/10.1118/1.4811141.

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6

Vaas, L. H., R. O. Blaauboer, and H. P. Leenhouts. "Radiation Sources, Doses and Dose Distributions in the Netherlands." Radiation Protection Dosimetry 36, no. 2-4 (June 1, 1991): 89–92. http://dx.doi.org/10.1093/oxfordjournals.rpd.a080974.

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7

Vaas, L. H., R. O. Blaauboer, and H. P. Leenhouts. "Radiation Sources, Doses and Dose Distributions in the Netherlands." Radiation Protection Dosimetry 36, no. 2-4 (June 1, 1991): 89–92. http://dx.doi.org/10.1093/rpd/36.2-4.89.

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8

Keall, P., S. Zavgorodni, L. Schmidt, and D. Haskard. "Improving wedged field dose distributions." Physics in Medicine and Biology 42, no. 11 (November 1, 1997): 2183–92. http://dx.doi.org/10.1088/0031-9155/42/11/013.

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9

Cross, W. G., J. Böhm, M. Charles, E. Piesch, and S. M. Seltzer. "6. Calculation of Dose Distributions." Journal of the International Commission on Radiation Units and Measurements os29, no. 1 (January 5, 1997): 18–32. http://dx.doi.org/10.1093/jicru/os29.1.18.

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10

Cross, W. G., J. Böhm, M. Charles, E. Piesch, and S. M. Seltzer. "6. Calculation of Dose Distributions." Reports of the International Commission on Radiation Units and Measurements os-29, no. 1 (January 1997): 18–32. http://dx.doi.org/10.1093/jicru_os29.1.18.

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11

Pla, Conrado, Michael D. C. Evans, and Ervin B. Podgorsak. "Dose distributions around selectron applicators." International Journal of Radiation Oncology*Biology*Physics 13, no. 11 (November 1987): 1761–66. http://dx.doi.org/10.1016/0360-3016(87)90175-1.

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12

Pla, Conrado, Michael D. C. Evans, and Ervin B. Podgorsak. "Dose Distributions Around Selectron Applicators." Medical Dosimetry 13, no. 2 (1988): 102. http://dx.doi.org/10.1016/0958-3947(88)90045-3.

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13

Placidi, Lorenzo, Eliana Gioscio, Cristina Garibaldi, Tiziana Rancati, Annarita Fanizzi, Davide Maestri, Raffaella Massafra, et al. "A Multicentre Evaluation of Dosiomics Features Reproducibility, Stability and Sensitivity." Cancers 13, no. 15 (July 30, 2021): 3835. http://dx.doi.org/10.3390/cancers13153835.

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Dosiomics is a texture analysis method to produce dose features that encode the spatial 3D distribution of radiotherapy dose. Dosiomic studies, in a multicentre setting, require assessing the features’ stability to dose calculation settings and the features’ capability in distinguishing different dose distributions. Dose distributions were generated by eight Italian centres on a shared image dataset acquired on a dedicated phantom. Treatment planning protocols, in terms of planning target volume coverage and dose–volume constraints to the organs at risk, were shared among the centres to produce comparable dose distributions for measuring reproducibility/stability and sensitivity of dosiomic features. In addition, coefficient of variation (CV) was employed to evaluate the dosiomic features’ variation. We extracted 38,160 features from 30 different dose distributions from six regions of interest, grouped by four features’ families. A selected group of features (CV < 3 for the reproducibility/stability studies, CV > 1 for the sensitivity studies) were identified to support future multicentre studies, assuring both stable features when dose distributions variation is minimal and sensitive features when dose distribution variations need to be clearly identified. Dosiomic is a promising tool that could support multicentre studies, especially for predictive models, and encode the spatial and statistical characteristics of the 3D dose distribution.

14

Al Hakim, Reza Azizul Nasa, Sigit Arrohman, Eko Saputra, Iwan Budi Anwar, J. Jamari, Rifky Ismail, Athanasius P. Bayuseno, and Mohammad Tauviqirrahman. "The Contact Simulation Comparison of UHMWPE to the Crosslink Intensity Effect." E3S Web of Conferences 73 (2018): 12014. http://dx.doi.org/10.1051/e3sconf/20187312014.

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Ultra High Molecular Weight Polyethylene called UHMWPE is a unique polymer material that has excellent physical and mechanical properties. UHMWPE material is frequently used in prosthesis. One example of UHMWPE uses in prosthesis is acetabular liner which is one component for Total Hip Joint Replacement (THR) and can also be found for bearing surfaces on the knee, ankle, shoulder, and connective tissue of the joint. UHMWPE material is made by compression molding process. However, UHMWPE wear often causes the failure of artificial hip joints. Therefore, a treatment to reduce the crosslink method is performed. The purpose of this study was to determine the crosslink effect in UHMWPE material. The method used for this analysis is ABAQUS 6-13 software. On bipolar model, the 3000 N load is applied in the FEM model. The crosslink dose used in this analysis was 50kgy, 75kgy, and 100kgy. The results obtained are that UHMWPE that has received by gamma irradiation treatment can receive a smaller stress distribution. The results of the simulation for UHMWPE without irradiation produced 0.759 stress distributions; 50kgy crosslink dose has 0.666 stress distributions; 75kgy crosslink dose has 0.662 stress distributions; and 100kgy crosslink dose has 0.660 stress distributions. This result proved that UHMWPE which has received crosslink can receive a better stress distribution. For the result crosslink with 100kgy dose received the best number of stress distributionss.

15

Yamada, Takahiro, Seishin Takao, Hidenori Koyano, Hideaki Nihongi, Yusuke Fujii, Shusuke Hirayama, Naoki Miyamoto, et al. "Validation of dose distribution for liver tumors treated with real-time-image gated spot-scanning proton therapy by log data based dose reconstruction." Journal of Radiation Research 62, no. 4 (May 5, 2021): 626–33. http://dx.doi.org/10.1093/jrr/rrab024.

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Abstract In spot scanning proton therapy (SSPT), the spot position relative to the target may fluctuate through tumor motion even when gating the radiation by utilizing a fiducial marker. We have established a procedure that evaluates the delivered dose distribution by utilizing log data on tumor motion and spot information. The purpose of this study is to show the reliability of the dose distributions for liver tumors treated with real-time-image gated SSPT (RGPT). In the evaluation procedure, the delivered spot information and the marker position are synchronized on the basis of log data on the timing of the spot irradiation and fluoroscopic X-ray irradiation. Then a treatment planning system reconstructs the delivered dose distribution. Dose distributions accumulated for all fractions were reconstructed for eight liver cases. The log data were acquired in all 168 fractions for all eight cases. The evaluation was performed for the values of maximum dose, minimum dose, D99, and D5–D95 for the clinical target volumes (CTVs) and mean liver dose (MLD) scaled by the prescribed dose. These dosimetric parameters were statistically compared between the planned dose distribution and the reconstructed dose distribution. The mean difference of the maximum dose was 1.3% (95% confidence interval [CI]: 0.6%—2.1%). Regarding the minimum dose, the mean difference was 0.1% (95% CI: −0.5%—0.7%). The mean differences of D99, D5–D95 and MLD were below 1%. The reliability of dose distributions for liver tumors treated with RGPT-SSPT was shown by the evaluation of the accumulated dose distributions.

16

Komemushi, Atsushi, Noboru Tanigawa, Shuji Kariya, Rie Yagi, Miyuki Nakatani, Satoshi Suzuki, Akira Sano, et al. "Does Vertebroplasty Affect Radiation Dose Distribution?: Comparison of Spatial Dose Distributions in a Cement-Injected Vertebra as Calculated by Treatment Planning System and Actual Spatial Dose Distribution." Radiology Research and Practice 2012 (2012): 1–6. http://dx.doi.org/10.1155/2012/571571.

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Purpose. To assess differences in dose distribution of a vertebral body injected with bone cement as calculated by radiation treatment planning system (RTPS) and actual dose distribution.Methods. We prepared two water-equivalent phantoms with cement, and the other two phantoms without cement. The bulk density of the bone cement was imported into RTPS to reduce error from high CT values. A dose distribution map for the phantoms with and without cement was calculated using RTPS with clinical setting and with the bulk density importing. Actual dose distribution was measured by the film density. Dose distribution as calculated by RTPS was compared to the dose distribution measured by the film dosimetry.Results. For the phantom with cement, dose distribution was distorted for the areas corresponding to inside the cement and on the ventral side of the cement. However, dose distribution based on film dosimetry was undistorted behind the cement and dose increases were seen inside cement and around the cement. With the equivalent phantom with bone cement, differences were seen between dose distribution calculated by RTPS and that measured by the film dosimetry.Conclusion. The dose distribution of an area containing bone cement calculated using RTPS differs from actual dose distribution.

17

Fenwick, John D., Wolfgang A. Tomé, Michael W. Kissick, and T. Rock Mackie. "Modelling simple helically delivered dose distributions." Physics in Medicine and Biology 50, no. 7 (March 23, 2005): 1505–17. http://dx.doi.org/10.1088/0031-9155/50/7/013.

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18

Pike, Bruce, Ervin B. Podgorsak, Terence M. Peters, and Conrado Pla. "Dose distributions in dynamic stereotactic radiosurgery." Medical Physics 14, no. 5 (September 1987): 780–89. http://dx.doi.org/10.1118/1.596003.

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19

Ayadi, M., D. Sarrut, and C. Ginestet. "SU-FF-T-154: Cumulating Static Dose Distributions to Simulate Dynamic Dose Distributions: An Experimental Study." Medical Physics 33, no. 6Part9 (June 2006): 2084. http://dx.doi.org/10.1118/1.2241078.

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20

Van de Kamer, Jeroen B., Astrid A. C. De Leeuw, Marinus A. Moerland, Arjan Bel, and Ina M. Jurgenliemk-Schulz. "Adding MRI-based 3D brachytherapy dose distributions to 3D IMRT dose distributions in cervical cancer patients." Brachytherapy 7, no. 2 (April 2008): 123–24. http://dx.doi.org/10.1016/j.brachy.2008.02.097.

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21

Zapp, E. Neal, Chester R. Ramsey, Lawrence W. Townsend, and Gautam D. Badhwar. "Solar particle event dose distributions: Parameterization of dose-time profiles." Acta Astronautica 43, no. 3-6 (August 1998): 249–59. http://dx.doi.org/10.1016/s0094-5765(98)00158-1.

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22

Radojcic, Đeni Smilovic, David Rajlic, Bozidar Casar, Manda Svabic Kolacio, Nevena Obajdin, Dario Faj, and Slaven Jurkovic. "Evaluation of two-dimensional dose distributions for pre-treatment patient-specific IMRT dosimetry." Radiology and Oncology 52, no. 3 (April 30, 2018): 346–52. http://dx.doi.org/10.2478/raon-2018-0019.

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Abstract Background The accuracy of dose calculation is crucial for success of the radiotherapy treatment. One of the methods that represent the current standard for patient-specific dosimetry is the evaluation of dose distributions measured with an ionization chamber array inside a homogeneous phantom using gamma method. Nevertheless, this method does not replicate the realistic conditions present when a patient is undergoing therapy. Therefore, to more accurately evaluate the treatment planning system (TPS) capabilities, gamma passing rates were examined for beams of different complexity passing through inhomogeneous phantoms. Materials and methods The research was performed using Siemens Oncor Expression linear accelerator, Siemens Somatom Open CT simulator and Elekta Monaco TPS. A 2D detector array was used to evaluate dose distribution accuracy in homogeneous, semi-anthropomorphic and anthropomorphic phantoms. Validation was based on gamma analysis with 3%/3mm and 2%/2mm criteria, respectively. Results Passing rates of the complex dose distributions degrade depending on the thickness of non-water equivalent material. They also depend on dose reporting mode used. It is observed that the passing rate decreases with plan complexity. Comparison of the data for all set-ups of semi-anthropomorphic and anthropomorphic phantoms shows that passing rates are higher in the anthropomorphic phantom. Conclusions Presented results raise a question of possible limits of dose distribution verification in assessment of plan delivery quality. Consequently, good results obtained using standard patient specific dosimetry methodology do not guarantee the accuracy of delivered dose distribution in real clinical cases.

23

van Dijk, Robert H. W., Nick Staut, Cecile J. A. Wolfs, and Frank Verhaegen. "A novel multichannel deep learning model for fast denoising of Monte Carlo dose calculations: preclinical applications." Physics in Medicine & Biology 67, no. 16 (August 8, 2022): 164001. http://dx.doi.org/10.1088/1361-6560/ac8390.

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Abstract Objective. In preclinical radiotherapy with kilovolt (kV) x-ray beams, accurate treatment planning is needed to improve the translation potential to clinical trials. Monte Carlo based radiation transport simulations are the gold standard to calculate the absorbed dose distribution in external beam radiotherapy. However, these simulations are notorious for their long computation time, causing a bottleneck in the workflow. Previous studies have used deep learning models to speed up these simulations for clinical megavolt (MV) beams. For kV beams, dose distributions are more affected by tissue type than for MV beams, leading to steep dose gradients. This study aims to speed up preclinical kV dose simulations by proposing a novel deep learning pipeline. Approach. A deep learning model is proposed that denoises low precision (∼106 simulated particles) dose distributions to produce high precision (109 simulated particles) dose distributions. To effectively denoise the steep dose gradients in preclinical kV dose distributions, the model uses the novel approach to use the low precision Monte Carlo dose calculation as well as the Monte Carlo uncertainty (MCU) map and the mass density map as additional input channels. The model was trained on a large synthetic dataset and tested on a real dataset with a different data distribution. To keep model inference time to a minimum, a novel method for inference optimization was developed as well. Main results. The proposed model provides dose distributions which achieve a median gamma pass rate (3%/0.3 mm) of 98% with a lower bound of 95% when compared to the high precision Monte Carlo dose distributions from the test set, which represents a different dataset distribution than the training set. Using the proposed model together with the novel inference optimization method, the total computation time was reduced from approximately 45 min to less than six seconds on average. Significance. This study presents the first model that can denoise preclinical kV instead of clinical MV Monte Carlo dose distributions. This was achieved by using the MCU and mass density maps as additional model inputs. Additionally, this study shows that training such a model on a synthetic dataset is not only a viable option, but even increases the generalization of the model compared to training on real data due to the sheer size and variety of the synthetic dataset. The application of this model will enable speeding up treatment plan optimization in the preclinical workflow.

24

Berns, Ch, P. Fritz, F. W. Hensley, and M. Wannenmacher. "54 PDR optimized dose distributions versus non-optimized CLDR dose distributions on the basis of clinical examples." Radiotherapy and Oncology 31 (April 1994): S35. http://dx.doi.org/10.1016/0167-8140(94)91152-5.

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25

Knisely, Jonathan P., James E. Bond, Ning J. Yue, Colin Studholme, and Alain C. J. de Lotbinière. "Image registration and calculation of a biologically effective dose for multisession radiosurgical treatments." Journal of Neurosurgery 93, supplement_3 (December 2000): 208–18. http://dx.doi.org/10.3171/jns.2000.93.supplement_3.0208.

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✓ The purpose of this study was to develop techniques for registering image sets associated with staged or multifraction radiosurgical treatments of large targets with the Leksell gamma knife to transform shot coordinates between treatment sessions and produce cumulative dose distributions and to investigate the theoretical biological effects of such protracted treatments by means of such concepts as the linear—quadratic model and biologically effective dose. An image registration technique based on normalized mutual information was adapted to produce one fused-image study from an imaging series acquired during distinct treatment sessions. A spreadsheet computer program was developed to determine coordinate transformations between the associated stereotactic coordinate systems based on digitized coordinates of fiducial markers appearing on the fused images. Coordinates of shots used during one treatment session could then be transformed to the stereotactic space of another session, and cumulative dose distributions could be computed. The procedure was applied to the two-stage treatment of a giant arteriovenous malformation (AVM). Overall uncertainty in each transformed shot position is approximately 0.7 mm. An effective single-fraction dose (Deff) was defined and computed for the two-stage AVM treatment. The simple summed dose distribution was compared with the Deff distribution. Because dose values differ significantly in overlap regions between the individual distributions, the clinical usefulness of the simple cumulative distribution is dubious. It may be useful for a future update of the GammaPlan treatment planning software to generate effective single-session dose distributions for such cases.

26

Gore, Elizabeth, Michael T. Gillin, Katherine Albano, and Beth Erickson. "Comparison of high dose-rate and low dose-rate dose distributions for vaginal cylinders." International Journal of Radiation Oncology*Biology*Physics 31, no. 1 (January 1995): 165–70. http://dx.doi.org/10.1016/0360-3016(94)00326-g.

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27

Thieke, Christian, Thomas Bortfeld, and Karl-Heinz Küfer. "Characterization of Dose Distributions Through the Max and Mean Dose Concept." Acta Oncologica 41, no. 2 (January 2002): 158–61. http://dx.doi.org/10.1080/028418602753669535.

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28

Niemierko, Andrzej. "Reporting and analyzing dose distributions: A concept of equivalent uniform dose." Medical Physics 24, no. 1 (January 1997): 103–10. http://dx.doi.org/10.1118/1.598063.

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29

Shortt, K. R., C. K. Ross, A. F. Bielajew, and D. W. O. Rogers. "Electron beam dose distributions near standard inhomogeneities." Physics in Medicine and Biology 31, no. 3 (March 1, 1986): 235–49. http://dx.doi.org/10.1088/0031-9155/31/3/003.

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30

Sont, W. N. "Statistical Modelling of Annual Occupational Dose Distributions." Radiation Protection Dosimetry 36, no. 2-4 (June 1, 1991): 279–83. http://dx.doi.org/10.1093/oxfordjournals.rpd.a081013.

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31

Sont, W. N. "Statistical Modelling of Annual Occupational Dose Distributions." Radiation Protection Dosimetry 36, no. 2-4 (June 1, 1991): 279–83. http://dx.doi.org/10.1093/rpd/36.2-4.279.

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32

Cross, W. G., J. Böhm, M. Charles, E. Piesch, and S. M. Seltzer. "Appendix C: Absorbed Dose Distributions; Conversion Factors." Journal of the International Commission on Radiation Units and Measurements os29, no. 1 (January 5, 1997): 92–106. http://dx.doi.org/10.1093/jicru/os29.1.92.

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33

Cross, W. G., J. Böhm, M. Charles, E. Piesch, and S. M. Seltzer. "Appendix C: Absorbed Dose Distributions; Conversion Factors." Reports of the International Commission on Radiation Units and Measurements os-29, no. 1 (January 1997): 92–106. http://dx.doi.org/10.1093/jicru_os29.1.92.

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34

Mohan, R. "139: Robust Optimization of IMPT Dose Distributions." Radiotherapy and Oncology 110 (February 2014): S68—S69. http://dx.doi.org/10.1016/s0167-8140(15)34160-8.

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35

Goitein, Michael. "Comparison of proton and photon dose distributions." Radiotherapy and Oncology 37 (October 1995): S43. http://dx.doi.org/10.1016/0167-8140(96)80598-6.

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36

Scarlat, F., Nicoleta Baboi, and V. Manu. "An analytical approximation of depth-dose distributions." Radiotherapy and Oncology 37 (October 1995): S54. http://dx.doi.org/10.1016/0167-8140(96)80641-4.

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37

Peppa, Vasiliki, Eleftherios P. Pappas, Pantelis Karaiskos, and Panagiotis Papagiannis. "Time resolved dose rate distributions in brachytherapy." Physica Medica 41 (September 2017): 13–19. http://dx.doi.org/10.1016/j.ejmp.2017.04.013.

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38

Mackeprang, P.-H., W. Volken, D. Terribilini, D. Frauchiger, K. Zaugg, D. M. Aebersold, M. K. Fix, and P. Manser. "Assessing dose rate distributions in VMAT plans." Physics in Medicine and Biology 61, no. 8 (March 30, 2016): 3208–21. http://dx.doi.org/10.1088/0031-9155/61/8/3208.

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39

Faddegon, B. A., and I. Blevis. "Electron spectra derived from depth dose distributions." Medical Physics 27, no. 3 (March 2000): 514–26. http://dx.doi.org/10.1118/1.598919.

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40

Yaparpalvi, R., K. J. Mehta, S. C. Desai, H. C. Kuo, W. A. Tome, and S. Kalnicki. "Are Inversely Planned Dose Distributions Superior to Manually Optimized Dose Distributions in Cervix T&O HDR Brachytherapy?" International Journal of Radiation Oncology*Biology*Physics 96, no. 2 (October 2016): E307. http://dx.doi.org/10.1016/j.ijrobp.2016.06.1397.

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41

Kajikawa, Tomohiro, Noriyuki Kadoya, Kengo Ito, Yoshiki Takayama, Takahito Chiba, Seiji Tomori, Hikaru Nemoto, Suguru Dobashi, Ken Takeda, and Keiichi Jingu. "A convolutional neural network approach for IMRT dose distribution prediction in prostate cancer patients." Journal of Radiation Research 60, no. 5 (July 19, 2019): 685–93. http://dx.doi.org/10.1093/jrr/rrz051.

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Abstract The purpose of the study was to compare a 3D convolutional neural network (CNN) with the conventional machine learning method for predicting intensity-modulated radiation therapy (IMRT) dose distribution using only contours in prostate cancer. In this study, which included 95 IMRT-treated prostate cancer patients with available dose distributions and contours for planning target volume (PTVs) and organs at risk (OARs), a supervised-learning approach was used for training, where the dose for a voxel set in the dataset was defined as the label. The adaptive moment estimation algorithm was employed for optimizing a 3D U-net similar network. Eighty cases were used for the training and validation set in 5-fold cross-validation, and the remaining 15 cases were used as the test set. The predicted dose distributions were compared with the clinical dose distributions, and the model performance was evaluated by comparison with RapidPlan™. Dose–volume histogram (DVH) parameters were calculated for each contour as evaluation indexes. The mean absolute errors (MAE) with one standard deviation (1SD) between the clinical and CNN-predicted doses were 1.10% ± 0.64%, 2.50% ± 1.17%, 2.04% ± 1.40%, and 2.08% ± 1.99% for D2, D98 in PTV-1 and V65 in rectum and V65 in bladder, respectively, whereas the MAEs with 1SD between the clinical and the RapidPlan™-generated doses were 1.01% ± 0.66%, 2.15% ± 1.25%, 5.34% ± 2.13% and 3.04% ± 1.79%, respectively. Our CNN model could predict dose distributions that were superior or comparable with that generated by RapidPlan™, suggesting the potential of CNN in dose distribution prediction.

42

Arsalan, M. Z., M. B. Kakakhel, M. Shamshad, and T. A. Afridi. "Patient-Specific Pre-Treatment VMAT Plan Verification Using Gamma Passing Rates." Atom Indonesia 1, no. 1 (April 14, 2023): 53–59. http://dx.doi.org/10.55981/aij.2023.1261.

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Continuous gantry motion, continuous beam modulation, and variable dose rate are used in volumetric modulated arc therapy (VMAT) to obtain highly conformal radiation therapy dose distributions. Several errors during daily radiation therapy treatment can be sources of uncertainties in dose delivery. These errors include monitor unit calculation errors and other human mistakes. Due to the uncertainties in the excessively modulated VMAT plan, the intended dose distribution is not delivered perfectly, leading to a mismatch between the measured and planned dose distributions. This necessitates an extensive and effective quality assurance (QA) program for both machine and patient. In this study, VMAT QA plan verification of 62 head and neck (HN) and 19 prostate cases was done using Octavius 4D setup with its associating VeriSoft gamma analysis software. The plans showed a maximum 3D gamma passing rate with 4 mm/3 % gamma acceptance criteria, i.e., 99.7 % for the HN cancer cases and 99.5 % for the prostate cancer cases. Local gamma analysis was also performed for both regions. Furthermore, 2D and volumetric gamma analyses were also carried out. Gamma analysis with respect to different axis was also carried out. It was known that the transversal axis showed the highest gamma passing rate in both HN and prostate cases, i.e., 99.17 % and 98.3 %, respectively. The transverse axis came to be a better fit for the planned dose distribution.

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Reda, Sonia, Eman Massoud, Ibrahem Bashter, and Esmat Amin. "Comparison between the calculated and measured dose distributions for four beams of 6 MeV linac in a human-equivalent phantom." Nuclear Technology and Radiation Protection 21, no. 2 (2006): 67–72. http://dx.doi.org/10.2298/ntrp0602067r.

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Radiation dose distributions in various parts of the body are of importance in radiotherapy. Also, the percent depth dose at different body depths is an important parameter in radiation therapy applications. Monte Carlo simulation techniques are the most accurate methods for such purposes. Monte Carlo computer calculations of photon spectra and the dose ratios at surfaces and in some internal organs of a human equivalent phantom were performed. In the present paper, dose distributions in different organs during bladder radiotherapy by 6 MeV X-rays were measured using thermoluminescence dosimetry placed at different points in the human-phantom. The phantom was irradiated in exactly the same manner as in actual bladder radiotherapy. Four treatment fields were considered to maximize the dose at the center of the target and minimize it at non-target healthy organs. All experimental setup information was fed to the MCNP-4b code to calculate dose distributions at selected points inside the proposed phantom. Percent depth dose distribution was performed. Also, the absorbed dose as ratios relative to the original beam in the surrounding organs was calculated by MCNP-4b and measured by thermoluminescence dosimetry. Both measured and calculated data were compared. Results indicate good agreement between calculated and measured data inside the phantom. Comparison between MCNP-4b calculations and measurements of depth dose distribution indicated good agreement between both.

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Piatkevich, M. N., H. I. Brynkevich, and E. V. Titovich. "Establishment of criteria for gamma-analysis of individual dose distributions during verification of radiotherapy high-tech treatment plans for cancer patients." Proceedings of the National Academy of Sciences of Belarus, Physical-Technical Series 67, no. 1 (April 7, 2022): 119–28. http://dx.doi.org/10.29235/1561-8358-2022-67-1-119-128.

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A typical process for verification of treatment plans in intensity-modulated radiation therapy is described. The main errors and uncertainties that arise in the course of planning dose distribution and in the process of dose delivery are listed. Methods for comparing dose distributions are considered: the distance to agreement (DTA) and the test for the algebraic dose difference. Formulas for calculating the shift of points of dose distributions, as well as the minimum value of the shift of points, are provided. The influences of global and local normalization and spatial resolution on the interpretation of the results obtained are defined. A methodology for determining reasonable criteria for gamma-analysis of individual dose distributions when verifying plans for irradiation of cancer patients using high-tech radiation therapy methods has been developed. Using the procedure proposed by the authors to establish action limits and tolerances will make it possible to assess the quality of medical care provided in healthcare institutions when using high-tech radiotherapy methods

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Климанов, В., V. Klimanov, Ж. Галяутдинова, Zh Galyautdinova, Н. Могиленец, N. Mogilenec, В. Смирнов, and V. Smirnov. "Reconstruction of Bremsstrahlung Spectrum of Medical Electron Linear Accelerators from Deep Dose Distributions in Water Phantom." Medical Radiology and radiation safety 62, no. 5 (October 27, 2017): 47–51. http://dx.doi.org/10.12737/article_59f300494670a7.65219672.

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Purpose: Development of the bremsstrahlung spectrum reconstruction method of medical electron linear accelerators (ELA) with different field sizes on the base of the deep dose distributions in a water phantom and determination of photon spectra for Varian Trilogy accelerator 6 MV. Material and methods: The proposed methodology is based on the use of dose kernels algorithm of point monoenergetic monodirectional source (pencil beam (PB)) for the deep dose distribution calculation, created different cross-section beams of in a water phantom, and experimental measurements of these distributions. For solving the inverse problem is applied Toolbox routines 'ptimtool knowing mathematical package MATLAB to solve. Results: Bremsstrahlung energy spectrum generated medical accelerator Varian Triology with different sizes of square fields from 3×3 up to 40×40 cm and average energy photons, depending on the size of the fields were received. Dose kernels for a set of defined energies PB were calculated. Depth dose distribution in a water phantom, calculated using the obtained spectra and dose kernels agree well with measurement dose distributions. Conclusion: The proposed technique reconstruction of bremsstrahlung spectrum of electron linear accelerator is adequate. Average energy spectra of bremsstrahlung photons for Varian Trilogy Accelerator in regime 6 MV varies from 1.71 to 1.43 MeV depending on the field size.

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Zapp, E. Neal, Chester R. Ramsey, Lawrence W. Townsend, and Gautum D. Badhwar. "Solar particle event dose and dose-rate distributions: parameterization of dose–time profiles, with subsequent dose-rate analysis." Radiation Measurements 30, no. 3 (June 1999): 393–400. http://dx.doi.org/10.1016/s1350-4487(99)00064-5.

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Zhu, Jingeng. "Generation of wedge-shaped dose distributions through dynamic multileaf collimator dose delivery." Journal of Applied Clinical Medical Physics 6, no. 3 (August 12, 2005): 37–45. http://dx.doi.org/10.1120/jacmp.2025.25351.

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Zhu, Jingeng. "Generation of wedge-shaped dose distributions through dynamic multileaf collimator dose delivery." Journal of Applied Clinical Medical Physics 6, no. 3 (June 2005): 37–45. http://dx.doi.org/10.1120/jacmp.v6i3.2060.

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Leydon, Patrick. "Monte Carlo in assessment of low dose rate prostate brachytherapy dose distributions." Physica Medica 32, no. 7 (July 2016): 950. http://dx.doi.org/10.1016/j.ejmp.2016.05.015.

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MacPherson, M. S., and J. J. Battista. "Dose distributions and dose rate constants for new ytterbium-169 brachytherapy seeds." Medical Physics 22, no. 1 (January 1995): 89–96. http://dx.doi.org/10.1118/1.597597.

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