Relevant bibliographies by topics / Distributions de dose

Academic literature on the topic 'Distributions de dose'

Author: Grafiati
Published: 7 July 2024

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Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

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

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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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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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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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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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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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Journal articles Dissertations / Theses Books

Dissertations / Theses on the topic "Distributions de dose":

1

Öström, Linn. "Post-processingof Monte Carlo calculated dose distributions." Thesis, KTH, Matematisk statistik, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-244314.

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This Master Thesis focuses on denoising of Monte Carlo calculated dose distributions of radiosurgery treatment plans. The objective of this project is to implement a Denoising Autoencoder (DAE) and investigate its denoising performance when it has been trained on Monte Carlo calculated dose distributions generated with lower number of photon showers. The DAE is trained in a supervised setting to learn the mapping between corrupted observations and clean ones. The questions this thesis aims to answer are: (i) Can a DAE be used to denoise Monte Carlo calculated dose distributions, and thus predict the dose prior to a full simulation? Additionally, (ii) does incorporating prior knowledge of shot position increase the denoising performance? The results in this investigation have shown that the network successfully predicts the dose for low number of photon showers. In very heavy noise inputs the network denoising was in general successful, and the network could fill in missing data. The results indicated that the DAE could reduce the level of noise with an amount comparable with simulations that were done with 102 times more samples.
Denna masteruppsats fokus är på att brusreducera Monte Carlo-beräknade dosdis-tributioner för behandlingsplaner i hjärnstereotaktisk radiokirurgi. Projektets avsikt är att implementera en brusreducerande Autoencoder (DAE) samt undersöka dess brusreducerande egenskaper, när nätverket har tränats på Monte Carlo-beräknade dosdistributioner genererade med få fotonsimulationer. Den brusreducerande Au-toencodern har genomgått övervakad träning, där den lär sig en avbildning mellan brusiga till brusfria distributioner. Frågorna som denna uppsats ämnar besvara är;(i) Kan en brusreducerande Autoencoder användas för att brusreducera Monte Carlo-beräknade dosdistributioner, och därmed förutspå dosen på förhand? Dessutom, (ii) förbättras nätverkets brusreducerande prestanda när ytterligare information angående skottpositionerna tillförs till nätverket? Resultaten i denna undersökning pekade på att nätverket framgångsrikt förutspår dosdistributionerna, baserat på dosdistribu-tioner som simulerats med få fotonsimulationer. I de fall då bruset i indata är väldigt kraftigt lyckas fortfarande nätverket att brusreducera, samt lyckat fylla i data som saknas. Resultaten indikerade att den brusreducerande Autoencodern kunde reduc-era brus i en mängd som kan jämföras med en simulation som gjorts med en faktor 102 fler fotonsimulationer.
2

Milette, Marie-Pierre. "Direct optimization of 3D dose distributions using collimator rotation." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/274.

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The primary goal of this thesis is to improve the precision and efficiency of radiation therapy treatment. This goal is achieved by developing and implementing a direct aperture optimization (DAO) platform where the multileaf collimator (MLC) is rotated between each aperture. The approach is referred to as rotating aperture optimization (RAO). A series of tests is performed to evaluate how a final optimized plan depends on MLC parameters. Imposing constraints on the leaf sequence results in increased efficiency and a simplification of the treatment plan without compromising the quality of the dose distribution. It is also shown that an arrangement of equispaced collimator angles takes full advantage of the flexibility associated with collimator rotation. A study including ten recurring nasopharynx cancer patients is used to evaluate the capabilities of RAO compared to other optimization techniques. It is shown that RAO plans require significantly less linac radiation output (monitor units or MU) while maintaining equivalent dose distribution quality compared to plans generated with the conventional fluence based approach. Furthermore with an improved collimator rotation speed, the RAO plans should be executable in the same or less time than plans generated with the fluence-based approach. For the second part of the study it is shown that plans generated with RAO are as good as or better than plans generated with standard fixed collimator DAO. Film and ion chamber measurements indicate that RAO plans can be delivered more accurately than DAO plans. Additional applications of DAO were investigated through collaboration with two PhD students. First, Monte Carlo was used to generate pencil beam dose distributions for DAO inverse treatment planning (MC-DAO). The MC-DAO technique correctly models traditionally difficult treatment geometries such as small fields and tissue inhomogeneities. The MC-DAO also takes advantage of the improved MU efficiency associated with the DAO technique. Secondly DAO is proposed for adaptive radiation therapy. The results show that plan re-adaptation can be performed more quickly than complete plan regeneration thereby minimizing the time the patient has to spend in the treatment room and reducing the potential for geometric errors in treatment delivery.
3

Nilsson, Viktor. "Prediction of Dose Probability Distributions Using Mixture Density Networks." Thesis, KTH, Matematisk statistik, 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-273610.

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In recent years, machine learning has become utilized in external radiation therapy treatment planning. This involves automatic generation of treatment plans based on CT-scans and other spatial information such as the location of tumors and organs. The utility lies in relieving clinical staff from the labor of manually or semi-manually creating such plans. Rather than predicting a deterministic plan, there is great value in modeling it stochastically, i.e. predicting a probability distribution of dose from CT-scans and delineated biological structures. The stochasticity inherent in the RT treatment problem stems from the fact that a range of different plans can be adequate for a patient. The particular distribution can be thought of as the prevalence in preferences among clinicians. Having more information about the range of possible plans represented in one model entails that there is more flexibility in forming a final plan. Additionally, the model will be able to reflect the potentially conflicting clinical trade-offs; these will occur as multimodal distributions of dose in areas where there is a high variance. At RaySearch, the current method for doing this uses probabilistic random forests, an augmentation of the classical random forest algorithm. A current direction of research is learning the probability distribution using deep learning. A novel parametric approach to this is letting a suitable deep neural network approximate the parameters of a Gaussian mixture model in each volume element. Such a neural network is known as a mixture density network. This thesis establishes theoretical results of artificial neural networks, mainly the universal approximation theorem, applied to the activation functions used in the thesis. It will then proceed to investigate the power of deep learning in predicting dose distributions, both deterministically and stochastically. The primary objective is to investigate the feasibility of mixture density networks for stochastic prediction. The research question is the following. U-nets and Mixture Density Networks will be combined to predict stochastic doses. Does there exist such a network, powerful enough to detect and model bimodality? The experiments and investigations performed in this thesis demonstrate that there is indeed such a network.
Under de senaste åren har maskininlärning börjat nyttjas i extern strålbehandlingsplanering. Detta involverar automatisk generering av behandlingsplaner baserade på datortomografibilder och annan rumslig information, såsom placering av tumörer och organ. Nyttan ligger i att avlasta klinisk personal från arbetet med manuellt eller halvmanuellt skapa sådana planer. I stället för att predicera en deterministisk plan finns det stort värde att modellera den stokastiskt, det vill säga predicera en sannolikhetsfördelning av dos utifrån datortomografibilder och konturerade biologiska strukturer. Stokasticiteten som förekommer i strålterapibehandlingsproblemet beror på att en rad olika planer kan vara adekvata för en patient. Den särskilda fördelningen kan betraktas som förekomsten av preferenser bland klinisk personal. Att ha mer information om utbudet av möjliga planer representerat i en modell innebär att det finns mer flexibilitet i utformningen av en slutlig plan. Dessutom kommer modellen att kunna återspegla de potentiellt motstridiga kliniska avvägningarna; dessa kommer påträffas som multimodala fördelningar av dosen i områden där det finns en hög varians. På RaySearch används en probabilistisk random forest för att skapa dessa fördelningar, denna metod är en utökning av den klassiska random forest-algoritmen. En aktuell forskningsriktning är att generera in sannolikhetsfördelningen med hjälp av djupinlärning. Ett oprövat parametriskt tillvägagångssätt för detta är att låta ett lämpligt djupt neuralt nätverk approximera parametrarna för en Gaussisk mixturmodell i varje volymelement. Ett sådant neuralt nätverk är känt som ett mixturdensitetsnätverk. Den här uppsatsen fastställer teoretiska resultat för artificiella neurala nätverk, främst det universella approximationsteoremet, tillämpat på de aktiveringsfunktioner som används i uppsatsen. Den fortsätter sedan att utforska styrkan av djupinlärning i att predicera dosfördelningar, både deterministiskt och stokastiskt. Det primära målet är att undersöka lämpligheten av mixturdensitetsnätverk för stokastisk prediktion. Forskningsfrågan är följande. U-nets och mixturdensitetsnätverk kommer att kombineras för att predicera stokastiska doser. Finns det ett sådant nätverk som är tillräckligt kraftfullt för att upptäcka och modellera bimodalitet? Experimenten och undersökningarna som utförts i denna uppsats visar att det faktiskt finns ett sådant nätverk.
4

Tozer-Loft, Stephen M. "Dose volume analysis in brachytherapy and stereotactic radiosurgery." Thesis, University of Sheffield, 2000. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.366100.

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Fox, Timothy Harold. "Computation and optimization of dose distributions for rotational stereotactic radiosurgery." Diss., Georgia Institute of Technology, 1994. http://hdl.handle.net/1853/32843.

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South, Christopher Peter. "The use of functional imaging to design optimal radiotherapy dose distributions." Thesis, Institute of Cancer Research (University Of London), 2011. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.538528.

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CECILIO, PAULO J. "Implementacao e aceite de sistema de radioterapia de feixe modulado dinamico com o uso de colimador secundario de multiplas folhas." reponame:Repositório Institucional do IPEN, 2008. http://repositorio.ipen.br:8080/xmlui/handle/123456789/11757.

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Tese (Doutoramento)
IPEN/T
Instituto de Pesquisas Energeticas e Nucleares - IPEN-CNEN/SP
8

Rowbottom, Carl Graham. "Optimisation of beam-orientations in conformal radiotherapy treatment planning." Thesis, Institute of Cancer Research (University Of London), 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.314088.

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9

Doucet, Robert. "Experimental verification of Monte Carlo calculated dose distributions for clinical electron beams." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=33750.

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Current electron beam treatment planning algorithms are inadequate to calculate dose distributions in heterogeneous phantoms. Fast Monte Carlo algorithms are accurate in general but their clinical implementation needs validation. Calculations of electron beam dose distributions performed using the fast Monte Carlo system XVMC and the well-benchmarked general-purpose Monte Carlo code EGSnrc were compared with measurements. Irradiations were performed using the 9 MeV and 15 MeV beams from the Clinac 18 accelerator with standard conditions. Percent depth doses and lateral profiles were measured with thermoluminescent dosimeter and electron diode respectively. The accelerator was modelled using EGS4/BEAM, and using an experiment-based beam model. All measurements were corrected by EGSnrc calculated stopping power ratios. Overall, the agreement between measurement and calculation is excellent. Small remaining discrepancies can be attributed to the non-equivalence between physical and simulated lung material, precision in energy tuning, beam model parameters optimisation and detector fluence perturbation effects.
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Hellström, Terese. "Deep-learning based prediction model for dose distributions in lung cancer patients." Thesis, Stockholms universitet, Fysikum, 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-196891.

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Background To combat one of the leading causes of death worldwide, lung cancer treatment techniques and modalities are advancing, and the treatment options are becoming increasingly individualized. Modern cancer treatment includes the option for the patient to be treated with proton therapy, which can in some cases spare healthy tissue from excessive dose better than conventional photon radiotherapy. However, to assess the benefit of proton therapy compared to photon therapy, it is necessary to make both treatment plans to get information about the Tumour Control Probability (TCP) and the Normal Tissue Complication Probability (NTCP). This requires excessive treatment planning time and increases the workload for planners.  Aim This project aims to investigate the possibility for automated prediction of the treatment dose distribution using a deep learning network for lung cancer patients treated with photon radiotherapy. This is an initial step towards decreasing the overall planning time and would allow for efficient estimation of the NTCP for each treatment plan and lower the workload of treatment planning technicians. The purpose of the current work was also to understand which features of the input data and training specifics were essential for producing accurate predictions.  Methods Three different deep learning networks were developed to assess the difference in performance based on the complexity of the input for the network. The deep learning models were applied for predictions of the dose distribution of lung cancer treatment and used data from 95 patient treatments. The networks were trained with a U-net architecture using input data from the planning Computed Tomography (CT) and volume contours to produce an output of the dose distribution of the same image size. The network performance was evaluated based on the error of the predicted mean dose to Organs At Risk (OAR) as well as the shape of the predicted Dose-Volume Histogram (DVH) and individual dose distributions.  Results  The optimal input combination was the CT scan and lung, mediastinum envelope and Planning Target Volume (PTV) contours. The model predictions showed a homogenous dose distribution over the PTV with a steep fall-off seen in the DVH. However, the dose distributions had a blurred appearance and the predictions of the doses to the OARs were therefore not as accurate as of the doses to the PTV compared to the manual treatment plans. The performance of the network trained with the Houndsfield Unit input of the CT scan had similar performance as the network trained without it.  Conclusions As one of the novel attempts to assess the potential for a deep learning-based prediction model for the dose distribution based on minimal input, this study shows promising results. To develop this kind of model further a larger data set would be needed and the training method could be expanded as a generative adversarial network or as a more developed U-net network.
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Books on the topic "Distributions de dose":

1

National Council on Radiation Protection and Measurements. Conceptual basis for calculations of absorbed-dose distributions: Recommendations. Bethesda, MD: National Council on Radiation Protection and Measurements, 1991.

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Aydarous, Abdulkadir Sheikh. Development of imaging techniques for determining dose distributions around discrete radioactive particles found in the environment. Birmingham: University of Birmingham, 2003.

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A, Kosunen, and Säteilyturvakeskus (Finland), eds. Intercomparison of radiotherapy treatment planning systems using calculated and measured dose distributions for external photon and electron beams. Helsinki, Finland: Finnish Centre for Radiation and Nuclear Safety, 1991.

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United States. Food and Drug Administration, ed. Pharmacokinetics: Guidance for repeated dose tissue distribution studies. [Rockville, Md.?]: U.S. Dept. of Health and Human Services, Public Health Service, Food and Drug Administration, 1995.

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Mulé, Rosa. Does democracy promote equality?. Luxembourg: LIS, 1998.

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Canadian Society of Hospital Pharmacists., ed. Working paper on unit dose-IV additive drug distribution system. Toronto, Ont: Canadian Society of Hospital Pharmacists, 1989.

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Shea, John. Does parents' money matter? Cambridge, MA: National Bureau of Economic Research, 1997.

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Ramachandran, T. V., of Environmental Assessment Division, Bhabha Atomic Research Centre. and Bhabha Atomic Research Centre, eds. Radon-thoron levels and inhalation dose distribution patterns in Indian dwellings. Mumbai: Bhabha Atomic Research Centre, 2003.

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Chmielewski, Andrzej G. Dose distribution effect on optimal geometry for industrial flue gas treatment system. Warszawa: Institute of Nuclear Chemistry and Technology, 1998.

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Reece, W. D. SADDE (Scaled Absorbed Dose Distribution Evaluator): A code to generate input for VARSKIN. Washington, DC: Division of Accident Evaluation, Office of Nuclear Regulatory Research, U.S. Nuclear Regulatory Commission, 1989.

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Book chapters on the topic "Distributions de dose":

1

Westerly, David, and Moyed Miften. "Quantifying Differences in Dose Distributions." In Clinical 3D Dosimetry in Modern Radiation Therapy, 357–76. Boca Raton : Taylor & Francis, 2017. | Series: Imaging in medical diagnosis and therapy ; 28: CRC Press, 2017. http://dx.doi.org/10.1201/9781315118826-14.

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Perucha, M., F. Sánchez-Doblado, A. Leal, M. Rincón, L. Núnez, R. Arráns, E. Carrasco, B. Sánchez-Nieto, J. A. Sánchez-Calzado, and L. Errazquin. "Monte Carlo Dose Distributions for Radiosurgery." In Advanced Monte Carlo for Radiation Physics, Particle Transport Simulation and Applications, 561–64. Berlin, Heidelberg: Springer Berlin Heidelberg, 2001. http://dx.doi.org/10.1007/978-3-642-18211-2_89.

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Nishio, Teiji, and Aya Nishio-Miyatake. "Visualization of Dose Distributions for Proton." In Image-Based Computer-Assisted Radiation Therapy, 319–54. Singapore: Springer Singapore, 2017. http://dx.doi.org/10.1007/978-981-10-2945-5_13.

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Pfaender, Mathias, Gerhard Grebe, Julia Ahlswede, Martin Roll, Lutz Lüdemann, Volker Budach, and Reinhard Wurm. "Concave Dose Distributions in Dynamic Arc Radiosurgery." In The Use of Computers in Radiation Therapy, 401–2. Berlin, Heidelberg: Springer Berlin Heidelberg, 2000. http://dx.doi.org/10.1007/978-3-642-59758-9_152.

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Mavroidis, P., B. Costa Ferreira, N. Papanikolaou, R. Svensson, B. K. Lind, and A. Brahme. "Radiobiological analysis of planned and delivered IMRT dose distributions." In IFMBE Proceedings, 381–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03474-9_107.

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Reft, Chester. "Point Detectors for Determining and Verifying 3D Dose Distributions." In Clinical 3D Dosimetry in Modern Radiation Therapy, 79–98. Boca Raton : Taylor & Francis, 2017. | Series: Imaging in medical diagnosis and therapy ; 28: CRC Press, 2017. http://dx.doi.org/10.1201/9781315118826-4.

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Kortesniemi, Mika, Antti Kosunen, Carita Aschan, Tom Serén, Petri Kotiluoto, Matti Toivonen, Petteri Välimäki, Tiina Seppälä, Iiro Auterinen, and Sauli Savolainen. "Measurements of Phantom Dose Distributions at the Finnish BNCT Facility." In Frontiers in Neutron Capture Therapy, 659–64. Boston, MA: Springer US, 2001. http://dx.doi.org/10.1007/978-1-4615-1285-1_95.

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Marks, Lawrence B. "Radiosurgery Dose Distributions: Theoretical Impact of Inhomogeneities on Lesion Control." In Advances in Radiosurgery, 13–17. Vienna: Springer Vienna, 1994. http://dx.doi.org/10.1007/978-3-7091-9371-6_4.

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Becker, G., R. Lohrum, T. Werner, J. Bürkelbach, G. Nemeth, R. Boesecke, W. Schlegel, and W. J. Lorenz. "Presentation and Evaluation of 3D Dose Distributions in Radiotherapy Planning." In CAR’89 Computer Assisted Radiology / Computergestützte Radiologie, 254–61. Berlin, Heidelberg: Springer Berlin Heidelberg, 1989. http://dx.doi.org/10.1007/978-3-642-52311-3_45.

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Nguyen, Dan, Azar Sadeghnejad Barkousaraie, Chenyang Shen, Xun Jia, and Steve Jiang. "Generating Pareto Optimal Dose Distributions for Radiation Therapy Treatment Planning." In Lecture Notes in Computer Science, 59–67. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-32226-7_7.

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Conference papers on the topic "Distributions de dose":

1

Oldham, Mark, Leonard Kim, and Geoffrey Hugo. "Optical-CT imaging of complex 3D dose distributions." In Medical Imaging, edited by Michael J. Flynn. SPIE, 2005. http://dx.doi.org/10.1117/12.595525.

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Ávila-Rodrı́guez, M. A. "Experimental measurements of spatial dose distributions in radiosurgery treatments." In MEDICAL PHYSICS: Fifth Mexican Symposium. AIP, 2001. http://dx.doi.org/10.1063/1.1420475.

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Zhou, Chuanyu. "Computational determination of absorbed dose distributions from gamma ray sources." In The 27th annual review of progress in quantitative nondestructive evaluation. AIP, 2001. http://dx.doi.org/10.1063/1.1373803.

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Cucinotta, Francis A., Robert Katz, John W. Wilson, and Rajendra R. Dubey. "Radial dose distributions in the delta-ray theory of track structure." In Two−center effects in ion−atom collisions: A symposium in honor of M. Eugene Rudd. AIP, 1996. http://dx.doi.org/10.1063/1.50083.

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Diederich, Chris J., William H. Nau, Frank Kleinstueck, Jeff Lotz, and David Bradford. "IDTT therapy in cadaveric lumbar spine: temperature and thermal dose distributions." In BiOS 2001 The International Symposium on Biomedical Optics, edited by Thomas P. Ryan. SPIE, 2001. http://dx.doi.org/10.1117/12.427849.

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Brynkevich, A. I., M. N. Piatkevich, and E. V. Titovich. "CRITERIA FOR EVALUATION OF DOSIMETRIC VERIFICATION OF RADIOTHERAPY HIGH-TECH TREATMENT PLANS FOR CANCER PATIENTS." In SAKHAROV READINGS 2021: ENVIRONMENTAL PROBLEMS OF THE XXI CENTURY. International Sakharov Environmental Institute of Belarusian State University, 2021. http://dx.doi.org/10.46646/sakh-2021-2-252-255.

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To test the effectiveness of the dose delivery system of the treatment plan in intensity modulated radiation therapy, quality assurance systems are used. The main tool for verifying the correspondence between the reference and the estimated dose distribution is Y—indexing. In the process of Y—analysis of individual dose distributions, both point dose values and geometrical offset between the reference and delivered distributions are evaluated. To check the compliance of dose distributions, the concepts of action limits and tolerances are used. The action limits are defined as the total percentage of the estimated value by which deviation of the indicators checked by the quality assurance system is allowed, with a minimal risk of harm to the patient. Tolerances are defined as the boundaries of the magnitude change within which the treatment process is considered to be performed according to the prescribed conditions.
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Almaraz, S. "Dose distributions in prostate brachytherapy: comparison between Sievert and Monte Carlo methods." In MEDICAL PHYSICS: Seventh Mexican Symposium on Medical Physics. AIP, 2003. http://dx.doi.org/10.1063/1.1615110.

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Lee, Tzu-Cheng, Ruoqiao Zhang, Adam M. Alessio, Lin Fu, Bruno De Man, and Paul E. Kinahan. "Statistical distributions of ultra-low dose CT sinograms and their fundamental limits." In SPIE Medical Imaging, edited by Thomas G. Flohr, Joseph Y. Lo, and Taly Gilat Schmidt. SPIE, 2017. http://dx.doi.org/10.1117/12.2254375.

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Zhou, Chuanyu. "Computational determination of absorbed dose distributions from multiple volumetric gamma ray sources." In QUANTITATIVE NONDESTRUCTIVE EVALUATION. AIP, 2002. http://dx.doi.org/10.1063/1.1472852.

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Góes, Daniel A., and Nelson D. A. Mascarenhas. "Low-Dose Computed Tomography Filtering Using Geodesic Distances." In Conference on Graphics, Patterns and Images. Sociedade Brasileira de Computação, 2020. http://dx.doi.org/10.5753/sibgrapi.est.2020.12983.

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Due to the concerns related to patient exposure to X-ray, the dosage used in computed tomography must be reduced (Low-dose Computed Tomography - LDCT). One of the effects of LDCT is the degradation in the quality of the final reconstructed image. In this work, we propose a method of filtering LDCT sinograms that are subject to signal-dependent Poisson noise. To filter this type of noise, we use a Bayesian approach, changing the Non-local Means (NLM) algorithm to use geodesic stochastic distances for Gamma distribution, the conjugate prior to Poisson, as a similarity metric between each projection point. Among the geodesic distances evaluated, we found a closed solution for the Shannon entropy for Gamma distributions. We compare our method with the following methods based on NLM: PoissonNLM, Stochastic Poisson NLM, Stochastic Gamma NLM and the original NLM after Anscombe transform. We also compare with BM3D after Anscombe transform. Comparisons are made on the final images reconstructed by the Filtered-Back Projection (FBP) and Projection onto Convex Sets (POCS) methods using the metrics PSNR and SSIM.

Reports on the topic "Distributions de dose":

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Napier, B. A., W. T. Farris, and J. C. Simpson. Determination of dose distributions and parameter sensitivity. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/7062146.

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Napier, B. A., W. T. Farris, and J. C. Simpson. Determination of dose distributions and parameter sensitivity. Hanford Environmental Dose Reconstruction Project; dose code recovery activities; Calculation 005. Office of Scientific and Technical Information (OSTI), December 1992. http://dx.doi.org/10.2172/10116712.

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Rakhno, Igor, and Igor Tropin. Accident Scenarios for IOTA Ring and Dose Distributions Calculated with MARS15 Code. Office of Scientific and Technical Information (OSTI), June 2018. http://dx.doi.org/10.2172/1469001.

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Kamboj, S., C. Yu, and J. Rabovsky. Potential dose distributions at proposed surface radioactvity clearance levels resulting from occupational scenarios. Office of Scientific and Technical Information (OSTI), August 2011. http://dx.doi.org/10.2172/1021328.

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Wilson, W. E., and W. D. Jr Reece. Calculation of percentile-distance ratios and scaled absorbed-dose distributions for 0. 05- to 30-keV primary electrons. Office of Scientific and Technical Information (OSTI), October 1991. http://dx.doi.org/10.2172/6040610.

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Awschalom, M., and R. K. T. Haken. Dependence of charge collection distributions and dose on the gas type filling the ionization chamber for a p(66)Be(49) clinical neutron beam. Office of Scientific and Technical Information (OSTI), January 1985. http://dx.doi.org/10.2172/5345986.

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Awschalom, Miguel, and R. Ten Haken. Dependence of Charge Collection Distributions and Dose of the Gas Type Filling the Ionization Chamber for a p(66)-Be(49) Clinical Neutron Beam. Office of Scientific and Technical Information (OSTI), January 1985. http://dx.doi.org/10.2172/1156255.

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Schimmel, J. G., and D. M. Beck. Milk production and distribution in low-dose counties for the Hanford Thyroid Disease Study. Hanford Environmental Dose Reconstruction Project. Office of Scientific and Technical Information (OSTI), June 1992. http://dx.doi.org/10.2172/10159360.

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Deonigi, D. E., D. M. Anderson, and G. L. Wilfert. Commercial milk distribution profiles and production locations. Hanford Environmental Dose Reconstruction Project. Office of Scientific and Technical Information (OSTI), April 1994. http://dx.doi.org/10.2172/10153894.

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Verma, Monika, Thomas Hertel, and Paul Preckel. Predicting Within Country Household Food Expenditure Variation Using International Cross-Section Estimates. GTAP Working Paper, September 2009. http://dx.doi.org/10.21642/gtap.wp57.

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There is a long and distinguished literature involving demand analysis using international cross-section data. Such models are widely used for predicting national per capita consumption. However, there is nothing in this literature testing the performance of estimated models in predicting demands across the income spectrum within a single country. This paper fills the gap. We estimate an AIDADS model using cross-section international per capita data, and find that it does well in predicting food demand across the income distribution within Bangladesh. This suggests that there may be considerable value in using international cross-section analysis to study poverty and distributional impacts of policies.

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