Relevant bibliographies by topics / Treatment planning

Academic literature on the topic 'Treatment planning'

Author: Grafiati
Published: 4 June 2021
Last updated: 1 June 2024

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Journal articles on the topic "Treatment planning"

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Oldham, John M. "Treatment Planning." Journal of Personality Disorders 1, no. 2 (June 1987): 207–10. http://dx.doi.org/10.1521/pedi.1987.1.2.207.

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Oldham, John M. "Treatment Planning." Journal of Personality Disorders 2, no. 1 (March 1988): 88–90. http://dx.doi.org/10.1521/pedi.1988.2.1.88.

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Klopman, Theodore. "Treatment planning." Journal of the American Dental Association 117, no. 7 (December 1988): 812. http://dx.doi.org/10.14219/jada.archive.1988.0138.

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Ibbetson, R. "Treatment planning." British Dental Journal 186, no. 11 (June 1999): 552–58. http://dx.doi.org/10.1038/sj.bdj.4800167.

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Ibbetson, R. "Treatment planning." British Dental Journal 186, no. 11 (June 12, 1999): 552–58. http://dx.doi.org/10.1038/sj.bdj.4800167a.

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Knöös, T. "TREATMENT PLANNING." Radiotherapy and Oncology 92 (August 2009): S44. http://dx.doi.org/10.1016/s0167-8140(12)72703-2.

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Kao, Richard T., and Donald A. Curtis. "Treatment Planning." Journal of the California Dental Association 30, no. 7 (July 1, 2002): 501–2. http://dx.doi.org/10.1080/19424396.2002.12223297.

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Sarkar, Vikren, Long Huang, Prema Rassiah-Szegedi, Hui Zhao, Jessica Huang, Martin Szegedi, and Bill J. Salter. "Planning for mARC treatments with the Eclipse treatment planning system." Journal of Applied Clinical Medical Physics 16, no. 2 (March 2015): 458–64. http://dx.doi.org/10.1120/jacmp.v16i2.5351.

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Pavliha, Denis, Bor Kos, Marija Marčan, Anže Županič, Gregor Serša, and Damijan Miklavčič. "Planning of Electroporation-Based Treatments Using Web-Based Treatment-Planning Software." Journal of Membrane Biology 246, no. 11 (June 19, 2013): 833–42. http://dx.doi.org/10.1007/s00232-013-9567-2.

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TASHIRO, Mutsumi, Hirofumi SHIMADA, and Motohiro KAWASHIMA. "6 Treatment Planning." RADIOISOTOPES 64, no. 6 (2015): 394–99. http://dx.doi.org/10.3769/radioisotopes.64.394.

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Dissertations / Theses on the topic "Treatment planning"

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Brown, Richard. "Microbrachytherapy treatment planning." Thesis, Toulouse 3, 2017. http://www.theses.fr/2017TOU30180/document.

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Une nouvelle modalité de radiothérapie, la micro-curiethérapie, est en cours de développement. Cette thérapie cible des tumeurs solides inopérables en effectuant des injections de liquide contenant des microsphères radioactives en suspension. Plusieurs injections sont nécessaires pour suffisamment irradier la zone tumorale et donc, afin d'optimiser le positionnement de ces injections, une méthode de planification de traitement nécessaire a été développée et validée au cours de cette thèse. Tout au long de ce travail, trois thèmes principaux seront discutés : • Comment réaliser la dosimétrie particulière de cette micro-curiethérapie ? • Comment effectuer la planification de traitement pour cette modalité ? • Comment optimiser le plan de traitement afin qu'il soit le plus efficace possible ? La dosimétrie en micro-curiethérapie a été réalisée en calculant la distribution de dose absorbée pour une injection. Cette distribution a été convoluée à la position des autres injections dans la tumeur pour calculer la distribution de dose absorbée dans le patient. Pour effectuer la dosimétrie spécifique dans la tumeur et les organes à risque, les histogrammes dose-volume (HDV) ont été extraits et analysés. Une fois la méthode de dosimétrie établie, nous avons développé une méthodologie de planification de traitement pour développer et optimiser le plan pour chaque patient. Pour cela, nous avons testé et comparé trois algorithmes : la méthode de Nelder-Mead, l'algorithme des abeilles et l'algorithme "Non-Dominated Sorting Genetic Algorithm II" (NSGA II). Nous avons montré que, grâce à l'optimisation multiobjectif, le NSGA II donne plus de liberté à l'utilisateur ; c'est pourquoi il a été utilisé par la suite. Enfin, nous avons effectué une comparaison entre les paramètres d'injection. Nous avons mis en évidence qu'entre les radio-isotopes 90Y, 166Ho, 131I and 177Lu, les injections de 90Y sont optimales. Nous avons testé des injections de 5, 10 et 20 µL et des activités initiales de 5, 10 et 20 MBq. Nous avons trouvé que des injections de 20 µL avec 20 MBq sont optimales car celles-ci permettent de minimiser le nombre d'injections requis. Cette nouvelle technologie associée aux développements réalisés dans ses travaux démontre la faisabilité, qui a pu être validée sur animal, de pouvoir injecter un liquide contenant des microsphères radioactives en suspension afin de pouvoir traiter efficacement, tout en préservant les tissus sains environnants, des tumeurs inopérables encore de mauvais pronostic aujourd'hui, mais surement mieux prises en charge dans un proche avenir
An innovative form of radiotherapy, microbrachytherapy, is under development. This therapy targets solid, inoperable tumours by performing injections of liquid containing radioactive microspheres in suspension. Many injections are required to sufficiently cover the tumoural volume, and so to be able to deliver the position of these injections, a method of treatment planning has been developed and validated throughout this research. Throughout this work, three main questions are addressed: • How to perform the dosimetry for microbrachytherapy? • How to perform treatment planning for this modality? • What are the optimal injection properties to deliver the most efficient treatment? Microbrachytherapy dosimetry was performed by calculating the absorbed dose distribution for an injection. This distribution was then convolved at each injection position within the tumour to calculate the patient's absorbed dose distribution. Dosimetry of the tumour and the organs at risk was performed by extracting and analysing dose-volume histograms (DVHs). Once a method of dosimetry was put in place, optimisation algorithms were developed to generate patient-specific treatment plans. For this, three algorithms were tested and compared: Nelder-Mead Simplex, the Bees algorithm and the non-dominated sorting genetic algorithm II. It was found that, thanks to its MO optimisation, the non-dominated sorting algorithm II was the most flexible, and was used preferentially. Lastly, a comparison of injection parameters was performed. It was found that between 90Y, 166Ho, 131I and 177Lu, optimal injections consisted of microspheres of 90Y. Injection volumes of 5, 10 and 20 µL and initial activities of 5, 10 and 20 MBq were tested. It was found that 20 µL injections with 20 MBq were optimal because they minimise the number of injections required. This new technology combined with developments shown in this work demonstrate the feasibility - that was validated on animals - the ability to inject liquid containing radioactive microspheres in suspension to efficiently treat inoperable tumours whilst protecting surrounding healthy tissue. Such tumours, despite still having a poor prognosis, will surely have better support in the near future
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Neufeld, Esra. "High resolution hyperthermia treatment planning." Konstanz Hartung-Gorre, 2008. http://d-nb.info/992327873/04.

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Qasrawi, Radwan. "Treatment planning methods for clinical electroporation." Doctoral thesis, Universitat Pompeu Fabra, 2017. http://hdl.handle.net/10803/441753.

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Two treatment modalities based on the electroporation phenomenon, electro-chemotherapy and irreversible electroporation, have been developed in the last decades to destroy solid tumors. These treatments are based on the delivery of short high voltage pulses across electrodes and their success depends on covering the whole tumor with an adequate electric field magnitude. This leads to a need for software tools capable of allowing patient-specific treatment planning. In particular, there is a need for treatment planning tools similar to those used in radiotherapy in order to plan the location of the electrodes and the voltage magnitudes to be applied across these electrodes. Here it is described a treatment planning platform prototype which allows users to perform the complete treatment planning sequence in a single environment. The planned treatment volume is represented on the patient medical images after computing, by the finite element method, the electric field magnitude generated by needle-shaped electrodes. Here it is also reported a study in which the above prototype was employed for analyzing the potential impact of liver blood vessels on tumor ablation by irreversible electroporation. From this study it is concluded that those vessels must not be neglected in treatment planning and that undertreatment around those vessels may be occurring frequently in current irreversible electroporation treatments of liver tumors. Finally, it is described the implementation and characterization of a fast semi-analytical algorithm for computing the electric field distribution generated by needle-shaped electrodes. This algorithm is intended to rapidly pre-visualize the expected treatment region before proceeding with an accurate, but laborious and slow, computation based numerical methods.
Dos modalidades de tratamiento basadas en el fenómeno de la electroporación, la electroquimioterapia y la electroporación irreversible, han sido desarrolladas en las últimas décadas para destruir tumores sólidos. Estos tratamientos se basan en la aplicación de pulsos cortos de alta tensión a través de electrodos y para su éxito se requiere abarcar todo el tumor con una magnitud de campo eléctrico adecuada. Esto lleva a la necesidad de herramientas software que permitan la planificación de tratamiento específica del paciente. En particular, existe la necesidad de herramientas de planificación de tratamiento similares a las utilizadas en radioterapia para planificar la ubicación de los electrodos y las magnitudes de voltaje a aplicar a través de estos electrodos. Aquí se describe un prototipo de plataforma de planificación de tratamiento que permite a los usuarios realizar la secuencia completa de planificación de tratamiento en un solo entorno. El volumen planificado de tratamiento se representa sobre las imágenes médicas del paciente después de calcular, mediante el método de elementos finitos, la magnitud del campo eléctrico generada por electrodos en forma de aguja. Aquí también se detalla un estudio en el que el prototipo anterior se empleó para analizar el impacto potencial de los vasos sanguíneos hepáticos sobre la ablación de tumores por electroporación irreversible. De este estudio se concluye que estos vasos no deben ser descuidados en la planificación del tratamiento y que alrededor de esos vasos se puede estar produciendo sub-tratamiento frecuentemente en los tratamientos de electroporación irreversible que actualmente se aplican para tumores hepáticos. Finalmente, se describe la implementación y caracterización de un algoritmo semi-analítico rápido para calcular la distribución de campo eléctrico generada por electrodos en forma de aguja. Este algoritmo está destinado a pre-visualizar rápidamente la región de tratamiento esperada antes de proceder con un preciso, pero laborioso y lento, proceso de cálculo basado en métodos numéricos.
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Perera, Bel Enric. "Treatment planning in electroporation-based therapies." Doctoral thesis, Universitat Pompeu Fabra, 2021. http://hdl.handle.net/10803/673102.

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Diverses teràpies mèdiques es basen en l’electroporació. Entre d’altres, l’electroporació s'usa per al tractament de tumors sòlids. Com que només afecta les cèl·lules i la matriu extracel·lular queda intacta, amb l’electroporació es poden tractar tumors propers a teixits vitals. Tot i aquest clar avantatge respecte d’altres teràpies físiques, els tractaments s’han de planificar amb precisió perquè el resultat és altament dependent dels paràmetres del procediment i de les propietats dels teixits. Aquesta tesi se centra en el desenvolupament d’eines i models per a la planificació de tractaments amb teràpies basades en electroporació, concretament, per al tractament de tumors interns. A continuació, es relacionen les aportacions d’aquesta tesi. Primer, es descriu el desenvolupament d’una plataforma web que il·lustra la forta dependència de l’electroporació dels paràmetres del tractament i de les propietats elèctriques del teixit, en particular, la dependència del nombre i posició dels elèctrodes, el voltatge aplicat entre parells d’elèctrodes i la conductivitat elèctrica del teixit. Segon, s’estudien models que descriuen la mort cel·lular per predir el volum electroporat en casos de superposició de tractaments amb múltiples parells d’elèctrodes, cosa freqüent en les teràpies basades en electroporació. Per a aquest estudi, es van superposar tractaments per caracteritzar els models de mort cel·lular i, després, aquests models es van usar per analitzar fins a quint punt el volum del tractament es veu afectat en teràpies basades en electroporació. Tercer, es presenta una plataforma per a la planificació de tractaments amb teràpies basades en electroporació. La inserció òptima dels elèctrodes pot ser planificada preoperativament simulant el volum del tractament en models precisos específics per a cada pacient de forma senzilla i ràpida.
Tissue electroporation is the basis of several therapies. Among others, it is used in treatments of solid tumors. Because electroporation exclusively targets cells and leaves the extracellular matrix unaffected, tumor treatment near vital structures is feasible, which is a clear advantage over other therapies based on physical methods. However, careful treatment planning is required because electroporation is highly dependent on procedure parameters and tissue properties. This thesis focuses on the development of tools and models for treatment planning in electroporation-based therapies, specifically, for the treatment of internal tumors. The contributions of this thesis are as follows. First, the development of a web platform which illustrates the strong dependence of electroporation on treatment parameters and tissue electrical properties is described. Namely, the dependence on electrode number and positioning, voltage applied between electrode pairs, and tissue electrical conductivity. Second, models which describe cell are presented to predict treatment outcome in cases of treatment overlap with multiple electrode pairs, which are frequent in electroporation-based therapies. This study was performed by first characterizing the cell death models with overlapping treatments, and then, using these models to analyze how the treatment volume was affected in electroporation-based therapies. Third, a platform for treatment planning in electroporation-based therapies is presented. The optimal electrode insertion path can be planned preoperatively by simulating the predicted treatment volume on accurate patient-specific models in an easy-to-use and fast way.
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Huang, Jian. "Visibility problems occurring in radiation treatment planning." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2001. http://www.collectionscanada.ca/obj/s4/f2/dsk3/ftp04/MQ61012.pdf.

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Moerland, Marinus Adriaan. "Magnetic resonance imaging in radiotherapy treatment planning." [S.l.] : Utrecht : [s.n.] ; Universiteitsbibliotheek Utrecht [Host], 1996. http://www.library.uu.nl/digiarchief/dip/diss/01760825/inhoud.htm.

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Kok, Henny Petra. "Treatment planning for locoregional and intraluminal hyperthermia." [S.l. : Amsterdam : s.n.] ; Universiteit van Amsterdam [Host], 2007. http://dare.uva.nl/document/46767.

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Goorley, John Timothy 1974. "Boron neutron capture therapy treatment planning improvements." Thesis, Massachusetts Institute of Technology, 1998. http://hdl.handle.net/1721.1/49670.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Nuclear Engineering, 1998.
Includes bibliographical references.
The Boron Neutron Capture Therapy (BNCT) treatment planning process of the Harvard/MIT team used for their clinical Phase I trials is very time consuming. If BNCT proves to be a successful treatment, this process must be made more efficient. Since the Monte Carlo treatment planning calculations were the most time consuming aspect of the treatment planning process, requiring more than thirty six hours for scoping calculations of three to five beams and final calculations for two beams, it was targeted for improvement. Three approaches were used to reduce the calculation times. A statistical uncertainty analysis was performed on doses rates and showed that a fewer number of particles could not be used and still meet uncertainty requirements in the region of interest. Unused features were removed and assumptions specific to the Harvard/MIT BNCT treatment planning calculations were hard wired into MCNP by Los Alamos personnel, resulting in a thirty percent decrease in runtimes. MCNP was also installed in parallel on the treatment planning computers, allowing a factor of improvement by roughly the number of computers linked together in parallel. After theses enhancements were made, the final executable, MCNPBNCT, was tested by comparing its calculated dose rates against the previously used executable, MCNPNEHD. Since the dose rates in close agreement, MCNPBNCT was adopted. The final runtime improvement to a single beam scoping run by linking the two 200MHz Pentium Pro computers was to reduce the wall clock runtime from 2 hours thirty minutes to fifty nine minutes. It is anticipated that the addition of ten 900 MHz CPUs will further reduce this calculation to three minutes, giving the medical physicist or radiation oncologist the freedom to use an iterative approach to try different radiation beam orientations to optimize treatment. Additional aspects of the treatment planning process were improved. The previously unrecognized phenomenon of peak dose movement during irradiation and its potential for overdosing the subject was identified. A method of predicting its occurrence was developed to prevent this from occurring. The calculated dose rate was also used to create dose volume histograms and volume averaged doses. These data suggest an alternative method for categorizing the subjects, rather than by peak tissue dose.
by John Timothy Goorley.
S.M.
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McGowan, Stacey Elizabeth. "Incorporating range uncertainty into proton therapy treatment planning." Thesis, University of Cambridge, 2015. https://www.repository.cam.ac.uk/handle/1810/248787.

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This dissertation addresses the issue of robustness in proton therapy treatment planning for cancer treatment. Proton therapy is considered to be advantageous in treating most childhood cancers and certain adult cancers, including those of the skull base, spine and head and neck. Protons, unlike X-rays, have a finite range highly dependent on the electron density of the material they are traversing, resulting in a steep dose gradient at the distal edge of the Bragg peak. These characteristics, together with advancements in computation and technology have led to the ability to plan and deliver treatments with greater conformality, sparing normal tissue and organs at risk. Radiotherapy treatment plans aim to meet set dosimetric constraints, and meet them at every fraction. Plan robustness is a measure of deviation between the delivered dose distribution and the planned dose distribution. Due to the same characteristics that make protons advantageous, conventional means of using margins to create a Planning Target Volume (PTV) to ensure plan robustness are inadequate. Additional to this, without a PTV, a new method of analysing plan quality is required in proton therapy. My original contribution to the knowledge in this area is the demonstration of how site- and centre- specific robustness constraints can be established. Robustness constraints can be used both for proton plan analysis and to identify patients that require plans of greater individualisation. I have also used the daily volumetric imaging from patients previously treated with conventional radiotherapy to quantify range uncertainty from inter- and intra-fraction motion. These new methods of both quantifying and analysing the change in proton range in the patient can aid in the choice of beam directions, provide input into a multi- criteria optimisation algorithm or can be used as criteria to determine when adaptive planning may be required. This greater understanding in range uncertainty better informs the planner on how best to balance the trade-off between plan conformality and robustness in proton therapy. This research is directly relevant to furthering the knowledge base in light of HM Government pledging £250 million to build two proton centres in England, to treat NHS patients from 2018. Use of methods described in this dissertation will aid in the establishment of clear and pre-defined protocols for treating patients in the future.
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Neath, Cathy. "Dosimetric evaluation and verification of treatment planning systems." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 2000. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ53113.pdf.

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Books on the topic "Treatment planning"

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Haas, Olivier Cyrille Louis. Radiotherapy Treatment Planning. London: Springer London, 1999. http://dx.doi.org/10.1007/978-1-4471-0821-4.

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Mould, R. F. Radiotherapy treatment planning. 2nd ed. Bristol: Hilger in collaboration with the Hospital Physicists' Association, 1985.

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J, Stefanac Stephen, and Nesbit Samuel Paul, eds. Treatment planning in dentistry. 2nd ed. St. Louis, Mo: Mosby/Elsevier, 2007.

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J, Stefanac Stephen, and Nesbit Samuel Paul, eds. Treatment planning in dentistry. St. Louis, Mo: Mosby, 2001.

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Treatment planning for psychotherapists. Washington, DC: American Psychiatric Press, 1996.

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Fundamentals of psychiatric treatment planning. Washington, DC: American Psychiatric Press, 1992.

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Chato, J. C. Thermal dosimetry and treatment planning. Berlin: Springer-Verlag, 1990.

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Gautherie, Michel, ed. Thermal Dosimetry and Treatment Planning. Berlin, Heidelberg: Springer Berlin Heidelberg, 1990. http://dx.doi.org/10.1007/978-3-642-48712-5.

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Patlak, Margie, Erin Balogh, and Sharyl Nass, eds. Patient-Centered Cancer Treatment Planning. Washington, D.C.: National Academies Press, 2011. http://dx.doi.org/10.17226/13155.

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Treatment planning in radiation oncology. 3rd ed. Philadelphia: Wolters Kluwer/Lippincott Williams & Wilkins Health, 2011.

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Book chapters on the topic "Treatment planning"

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Schweikard, Achim, and Floris Ernst. "Treatment Planning." In Medical Robotics, 207–38. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-22891-4_6.

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de Maio, Mauricio. "Treatment Planning." In Injectable Fillers in Aesthetic Medicine, 41–59. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-45125-6_4.

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Scortecci, Gérard M., and Guillaume Odin. "Treatment Planning." In Basal Implantology, 143–61. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-44873-2_7.

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Nakamura, Mitsuhiro. "Treatment Planning." In Stereotactic Body Radiation Therapy, 117–29. Tokyo: Springer Japan, 2015. http://dx.doi.org/10.1007/978-4-431-54883-6_9.

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Kiger, W. S., and Hiroaki Kumada. "Treatment Planning." In Neutron Capture Therapy, 287–326. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-31334-9_16.

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Schlüter, Matthias, Daniela Schmitt, Christoph Fürweger, Achim Schweikard, and Alexander Schlaefer. "Treatment Planning." In CyberKnife NeuroRadiosurgery, 59–73. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50668-1_5.

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Ihde, S., and T. Maier. "Treatment Planning." In Principles of BOI, 77–93. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/3-540-26987-8_7.

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Yildiz, Esra, Taner Yucel, Ugur Erdemir, and Korkud Demirel. "Treatment Planning." In Esthetic and Functional Management of Diastema, 131–40. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24361-0_10.

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Paredes, Daniel M., and Maria A. Brunelli Paredes. "Treatment Planning." In Clinical Mental Health Counseling: Elements of Effective Practice, 165–87. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2017. http://dx.doi.org/10.4135/9781071801253.n9.

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Yu, Yan, Kamila Nowak Choi, and Virginia Lockamy. "Treatment planning." In Principles and Practice of Image-Guided Radiation Therapy of Lung Cancer, 43–74. Boca Raton : Taylor & Francis, 2017. | Series: Imaging in medical diagnosis and therapy: CRC Press, 2017. http://dx.doi.org/10.1201/9781315143873-4.

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Conference papers on the topic "Treatment planning"

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Kulbida, U. N., O. N. Kaneva, and A. V. Zykina. "Media planning optimization treatment." In 2014 Dynamics of Systems, Mechanisms and Machines (Dynamics). IEEE, 2014. http://dx.doi.org/10.1109/dynamics.2014.7005673.

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Abou-Elela, S. I., M. E. Fawzy, M. El-Khateeb, and W. Abdel-Halim. "Integrated sustainable anaerobic treatment for low strength wastewater." In SUSTAINABLE DEVELOPMENT AND PLANNING 2011. Southampton, UK: WIT Press, 2011. http://dx.doi.org/10.2495/sdp110401.

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Levin, Mark Sh. "Digraph based medical treatment planning." In 2015 International Conference on Biomedical Engineering and Computational Technologies (SIBIRCON). IEEE, 2015. http://dx.doi.org/10.1109/sibircon.2015.7361876.

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Marzi, Hosein, and Yi Jia Lian. "Optimization in radiosurgery treatment planning." In 2011 IEEE International Systems Conference (SysCon). IEEE, 2011. http://dx.doi.org/10.1109/syscon.2011.5929035.

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Suárez, Martín. "Conformal Radiation Therapy, Treatment Planning." In MEDICAL PHYSICS: Sixth Mexican Symposium on Medical Physics. AIP, 2002. http://dx.doi.org/10.1063/1.1512036.

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Paulides, MM, Z. Rijnen, JF Bakker, P. Togni, PC Levendag, and GC Van Rhoon. "Treatment planning guided RF hyperthermia." In 2012 42nd European Microwave Conference (EuMC 2012). IEEE, 2012. http://dx.doi.org/10.23919/eumc.2012.6459105.

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Suárez, Martín, Luis Manuel Montaño Zentina, and Gerardo Herrera Corral. "Conformai Radiation Therapy, Treatment Planning." In MEDICAL PHYSICS: Sixth Mexican Symposium on Medical Physics. AIP, 2011. http://dx.doi.org/10.1063/1.3682844.

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De Wagter, C. "Computer Simulation for Hyperthermia Treatment Planning." In 15th European Microwave Conference, 1985. IEEE, 1985. http://dx.doi.org/10.1109/euma.1985.333554.

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Kumi, Paulette, Myrna Scott, and Deborah Dawson. "26 Present absentees: treatment escalation planning." In Marie Curie Palliative Care Research Conference. British Medical Journal Publishing Group, 2019. http://dx.doi.org/10.1136/spcare-2019-mariecuriepalliativecare.26.

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Greilich, Steffen, Oliver Jäkel, Carlos Granja, and Claude Leroy. "Treatment Planning for Ion Beam Therapy." In NUCLEAR PHYSICS METHODS AND ACCELERATORS IN BIOLOGY AND MEDICINE: Fifth International Summer School on Nuclear Physics Methods and Accelerators in Biology and Medicine. AIP, 2010. http://dx.doi.org/10.1063/1.3295620.

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Reports on the topic "Treatment planning"

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Dunscombe, P. B., Thomas C. Cetas, William G. Connor, Evan B. Douple, Fred W. Hetzel, W. Kaith Lee, David Loshek, et al. Hyperthermia Treatment Planning. AAPM, 1989. http://dx.doi.org/10.37206/26.

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Lyman, J. T. Tolerance doses for treatment planning. Office of Scientific and Technical Information (OSTI), October 1985. http://dx.doi.org/10.2172/6934260.

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Miller, Daniel W., Peter H. Bloch, John R. Cunningham, Bruce H. Curran, Geoffrey S. Ibbott, Douglas Jones, Shirley Z. Jucius, Dennis D. Leavitt, Radhe Mohan, and Jan van de Geijin. Radiation Treatment Planning Dosimetry Verification. AAPM, 1995. http://dx.doi.org/10.37206/54.

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Wessol, D. E., F. J. Wheeler, and R. S. Babcock. BNCT-RTPE: BNCT radiation treatment planning environment. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/421333.

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Yang, David Y. Incorporating Model Parameter Uncertainty into Prostate IMRT Treatment Planning. Fort Belvoir, VA: Defense Technical Information Center, April 2005. http://dx.doi.org/10.21236/ada439169.

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Sterling, J., J. McLaren, M. Taylor, and K. Cory. Treatment of Solar Generation in Electric Utility Resource Planning. Office of Scientific and Technical Information (OSTI), October 2013. http://dx.doi.org/10.2172/1107472.

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Wheeler, F., D. Wessol, C. Atkinson, and D. Nigg. Human applications of the INEL patient treatment planning system. Office of Scientific and Technical Information (OSTI), November 1995. http://dx.doi.org/10.2172/421331.

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Reynaert, N., S. Van der Marck, D. Schaart, W. Van der Zee, M. Tomsej, C. Van Vliet- Vroegindeweij, J. Jansen, M. Coghe, C. De Wagter, and B. Heijmen. NCS Report 16: Monte Carlo treatment planning: An introduction. Delft: NCS, June 2006. http://dx.doi.org/10.25030/ncs-016.

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Schuring, D., H. Westendorp, E. Van der Bijl, G. Bol, W. Crijns, A. Delor, Y. Jourani, et al. NCS Report 35: Quality assurance of Treatment Planning Systems. Delft: NCS, July 2022. http://dx.doi.org/10.25030/ncs-035.

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Chen, Lili. MR Imaging Based Treatment Planning for Radiotherapy of Prostate Cancer. Fort Belvoir, VA: Defense Technical Information Center, February 2005. http://dx.doi.org/10.21236/ada435143.

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