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<p>Generation of narrow segments is a matter of concern in step-and-shoot intensity modulated radiotherapy for several reasons. The measurement, calculation and delivery of dose from narrow segments may be complicated due to: the dosimetric properties of the detector; the effect of beam penumbra and heterogeneities within the patient; and the requirement for high geometric delivery precision respectively. The main purpose of this thesis was to investigate the parameters affecting the generation of narrow beam segments in IMRT optimization. Parameters such as effective source size, Gaussian height and width, density of the target volume, and gap between the tumor and normal tissue were varied to determine their influence on the number of narrow leaf pair separations. The gradient and penumbra were also examined. Two simple geometric models (thick model and thin model) with different dimensions were used. In the thick model, two 6-MV photon beams were incident on the target at right angles. A rectangular target was centered in a phantom with dimensions 20.25 cm×5.25 cm×20.25 cm. In the thin model, one 6-MV photon beam was normally incident on a 20.25 cm×1.25 cm×20.25 cm slab phantom. The relationship between the penumbra and number of narrow separated leaf pairs were examined for the thick model. The results did not show a consistent pattern. For the thin model, creating a gap between the target and the OAR decreased the total number of narrowly separated leaf pairs along the interface but increased the average dose delivered to the OAR. By varying the OAR max dose or the gap between the target and OAR, a peak was created in the dose profiles to compensate the penumbra. As gradient increased the peak height increased to compensate the dose fall-off. The width of the peak at half maximum changed with gradient but not in a predictable fashion.</p> / Master of Science (MSc)

This study aimed to investigate the impact of uncertainties in treatment planning related to the setup and range uncertainties. Three locations of targets were chosen, one in the head, one in the abdomen, and one in the pelvis. The treatment planning was assembled by utilising a CT image of a full body anthropomorphic phantom that was implemented into the treatment planning system RayStation (version 10, RaySearch Laboratories AB). A total of five regions of interest for the target were delineated as well as the associated organs at risks. The initial treatment plan considered the objective functions that aimed to achieve coverage of the CTV according to the clinical goal whilst minimising the dose reviewed by the organs at risk. In order to take the uncertainties into account for the treatment plan robustness optimization was applied with a predetermined set of setup and range uncertainties for each target. Dose histograms for the different cases of setup and range uncertainties were used to evaluate the significance of setup and range uncertainties onto the targets and the organs at risk. In addition, dose volume histograms and the difference in dose distribution aided to visualise the greatest deviation between the different cases. For the targets located in the head the small structures and the tissue heterogeneity resulted in being more affected by the range uncertainty rather than the setup uncertainty. The targets located in the abdomen and pelvic were on the other hand more affected by the setup uncertainties. This was a result related to structures adjacent to the target with different dose constraints and tissue heterogeneities. A comparison of the dose distribution showed the effect of setup uncertainties in the two aforementioned regions. For all cases the target coverage with the robust optimisation was as expected achieved. / Syftet med denna studie var att undersöka påverkan av osäkerheterna för protonernas utbredning och patienternas positionering med avseende på planeringen av en behandlingsplan. Totalt valdes tre områden som valdes att fokusera på var av två tumörer som var placerade i huvudet, en tumör som var placerad i abdomen och en tumör som var placerad i pelvis. Behandlingsplanen konstruerades med hjälp av en CT bild för en antropomorf helkropps fantom som överfördes till behandlingsteamet RayStation (version 10, RaySearch Laboratories AB). Samtliga tumörer och relevanta organ som anses vara i riskzonen för strålning fick definierade konturer. Därefter infördes funktioner för att optimera strålning till tumörer och funktioner för att begränsa strålningen till organen som var placerade i riskzonen enligt de kliniska önskemålen. För att beakta de ingående osäkerheterna i behandlingsplanen användes optimeringsalgoritmer för att säkerhetsställa att behandlingsplanen var robust gentemot ett antal förbestämda värde på de angivna osäkerheterna. Resultaten för osäkerheterna påverkas redovisades med histograms som påvisade den erhållna dosen för tumörer respektive organ i riskzonen. Utöver histogrammen redovisades differens mellan olika tillstånd av osäkerheterna med avseende på fördelning av dos. Tumörer som var placerade i huvudet hade större påverkan av protonernas utbredning på grund av de strukturer som är små till storlek relativt tumören, men även på grund av den heterogena omgivningen i det området. Däremot var tumörer som var placerade i abdomen och i pelvis mer påverkade av patientens position. Detta på grund av att närliggande organ och strukturer hade en diskrepans i de begränsade dosnivåerna samt på grund av heterogena omgivningar. Dessa fenomen påvisades även i de jämförelser som gjordes med avseende på fördelning av dos mellan olika tillstånd av osäkerheter. Slutligen visade denna studie att robust optimering gav samtliga tumörer en dosfördelning, vilket stämmer överens med vad som var förväntat.

A fundamental challenge in the treatment planning process of multi-fractional external-beam radiation therapy (EBRT) is the tradeoff between tumor control and normal tissue sparing in the presence of geometric uncertainties (GUs). To accommodate GUs, the conventional way is to use an empirical planning treatment volume (PTV) margin on the treatment target. However, it is difficult to determine a near-optimal PTV margin to ensure specified target coverage with as much normal tissue protection as achievable. Coverage optimized planning (COP) avoids this problem by optimizing dose in possible virtual treatment courses with GU models directly incorporated. A near-optimal dosimetric margin generated by COP was reported to savvily accommodate setup errors of target and normal tissues for prostate cancer treatment. This work further develops COP to account for (1) deformable organ motion and (2) delineation uncertainties for high-risk prostate cancer patients. The clinical value of COP is investigated by comparing with two margin-based planning techniques: (i) optimized margin (OM) technique that iteratively modifies PTV margins according to the evaluated target coverage probability and (ii) fixed margin (FM) technique that uses empirically selected constant PTV margins. Without patient-specific coverage probability estimation, FM plans are always less immune to the degraded effect of the modeled GUs than the COP plans or the OM plans. Empirical PTV margins face more risks of undesirable target coverage probability and/or excessive dose to surrounding OAR. The value of COP relative to OM varies with different GUs. As implemented for deformable organ motions, COP has limited clinical benefit. Due to optimization tradeoffs, COP often results in target coverage probability below the prescribed value while OM achieves better target coverage with comparable normal tissue dose. For delineation uncertainties, the clinical value of COP is potentially significant. Compared to OM, COP successfully maintains acceptable target coverage probability by exploiting the slack of normal tissue dose in low dose regions and maximally limiting high dose to normal tissue within tolerance.

External beam radiation therapy is currently one of the most commonly used modalities for treating cancer. With the rise of new technologies and increasing computational power, machine learning, deep learning and artificial intelligence applications used for classification and regression problems have begun to find their way into the field of radiation oncology. One such application is the automated generation of radiotherapy treatment plans, which must be optimized for every single patient. The department of radiation physics in Lund, Sweden, has developed an autoplanning software, which in combination with a commercially available treatment planning system (TPS), can be used for automatic creation of clinical treatment plans. The parameters of a multivariable cost function are changed iteratively, making it possible to generate a great amount of different treatment plans for a single patient. The output leads to optimal, near-optimal, clinically acceptable or even non-acceptable treatment plans. In this thesis, the possibility of using machine and deep learning to minimize the amount of treatment plans generated by the autoplanning software as well as the possibility of finding cost function parameters that lead to clinically acceptable optimal or near-optimal plans is evaluated. Data augmentation is used to create matrices of optimal treatment plan parameters, which are stored in a training database.  Patient specific training features are extracted from the TPS, as well as from the bottleneck layer of a trained deep neural network autoencoder. The training features are then matched against the same features extracted for test patients, using a k-nearest neighbor algorithm. Finally, treatment plans for a new patient are generated using the output plan parameter matrices of its nearest neighbors. This allows for a reduction in computation time as well as for finding suitable cost function parameters for a new patient.

<p>New Virtual Reality technologies provide the possibility of widening access to information in data. Haptics, the technology of touch, could be an interesting future aid and have large impact on medical applications. The use of haptic devices allows computer users to use their sense of touch, in order to feel virtual objects with a high degree of realism. </p><p>The aim of the thesis is to investigate the potential deployment and the benefits of using haptic force feedback instruments in maxillo-facial surgery. Based on a produced test application, the thesis includes suggested recommendations for future haptic implementations. </p><p>At the Department of Maxillo-Facial Surgery, at the Karolinska Hospital in Stockholm, Virtual Reality technologies are used as an aid to a limited extent during the production of physical medical models. The physical medical models are produced with Rapid Prototyping techniques. This process is examined and described in the thesis. Moreover, the future of the physical medical models is outlined, and a future alternative visualizing patient data in 3D and use haptics as an interaction tool, is described. Furthermore, we have examined the present use of haptic technology in medicine, and the benefits of using the technology as an aid for diagnostic and treatment planning. </p><p>Based on a presented literature study and an international outlook, we found that haptics could improve the management of medical models. The technology could be an aid, both for physical models as well as for virtual models. We found three different ways of implementing haptics in maxillo-facial surgery. A haptic system could be developed in order to only manage virtual medical models and be an alternative solution to the complete Rapid Prototyping process. A haptic system could serve as a software, handling the image processing and interfacing from a medical scanner to an Rapid Prototyping system. A haptic system could be developed as an alternative interaction tool, which could be implemented as an additional function in currently used image processing software, in order to improve the management of virtual medical models before the Rapid Prototyping process. </p><p>An implementation for planning and examination in maxillo-facial surgery, using haptic force feedback interaction, is developed and evaluated. The test implementation is underlying our aim of investigating the potential deployment and the benefits of using haptic force feedback instruments in maxillo-facial surgery. </p><p>After discussing the possible future of our implementation and the future of haptic force feedback in maxillo-facial surgery, a recommendation is given as a conclusion of our total work.</p>

Orientador: Marco Antônio Rodrigues Fernandes / Resumo: Este trabalho apresenta um estudo de planos radioterápicos realizados com o Sistema de Planejamento de Tratamento (TPS) XiO 5.10, utilizados em centros de radioterapia. Foram analisados os protocolos clínicos de radioterapia através de pesquisa bibliográfica em artigos científicos e textos produzidos pelas sociedades especializadas da área da radioncologia. Foram realizadas visitas técnicas junto a serviços de radioterapia para melhor entendimento das diferenças entre os distintos sistemas de planejamentos radioterápicos aplicados. A pesquisa verificou a resposta de três diferentes algoritmos computacionais de cálculo de dose de radiação: Superposition, Convolution e Fast Superposition, disponíveis no TPS XiO® da Elekta Medical Systems. Para isto foram analisados os parâmetros de distribuição de dose para 22 planejamentos radioterápicos realizados em uma clínica de radioterapia privada, sendo: 1 paciente com metástase cerebral, 3 pacientes com tumores de cabeça e pescoço, 9 com câncer de mama e 9 com tumores da próstata. Também foi quantificada a influência da heterogeneidade do tecido irradiado, através dos métodos de correção de heterogeneidade do XiO. Independentemente da correção de heterogeneidade do tecido, de um modo geral, o algoritmo de Convolution tendeu a subestimar a dose no PTV quando comparado com os outros dois algoritmos de cálculo de dose. Não foram percebidas diferenças significativas nos valores de dose de radiação calculados para o PTV, determinadas com os... (Resumo completo, clicar acesso eletrônico abaixo) / Abstract: This paper bring forward a study of radiotherapeutic planning accomplished by XiO 5.10 treatment planning systems utilized in radiotherapic centers. The clinical protocols of radiotherapy were analyzed through bibliographic research in scientific articles and texts produced by societies related to the area of radioncology. . Technical reconnaissance visits were performed in radiotherapy services for a better understanding of the differences between the systems of radiotherapy planning applied. The research verified the response of three distinct computational algorithms of calculation of dose radiation: Superposition, Convolution and Fast Superposition, available in the Tps XiO® of Elekta Medical Systems. For this purpose, were analyzed the dose distribution parameters for 22 radiotherapy plans performed in a private radiotherapy clinic: 1 patient with cerebral metastasis, 3 patients with head and neck tumors, 9 with breast cancer and 9 with prostate tumors . The influence of the heterogeneity of the irradiated tissue was also quantified through the XiO heterogeneity correction methods. Regardless of the correction of tissue heterogeneity, the Convolution algorithm tended to, in general, underestimate the dose in the PTV when compared to the other two dose calculation algorithms. No significant differences were observed in the radiation dose values calculated for the PTV, determined with the other two algorithms, however, the Superposition method provides a slight reduction o... (Complete abstract click electronic access below) / Mestre

Submitted by Henrique Barcellos Campanelli null (henrique.barcellos@aluno.ibb.unesp.br) on 2018-03-21T19:10:05Z No. of bitstreams: 1 Dissertação Henrique B Campanelli.pdf: 1741646 bytes, checksum: 36fc92e7787c2620fa984fb8bcfeedf4 (MD5) / Approved for entry into archive by Luciana Pizzani null (luciana@btu.unesp.br) on 2018-03-22T15:09:17Z (GMT) No. of bitstreams: 1 campanelli_hb_me_bot.pdf: 1741646 bytes, checksum: 36fc92e7787c2620fa984fb8bcfeedf4 (MD5) / Made available in DSpace on 2018-03-22T15:09:17Z (GMT). No. of bitstreams: 1 campanelli_hb_me_bot.pdf: 1741646 bytes, checksum: 36fc92e7787c2620fa984fb8bcfeedf4 (MD5) Previous issue date: 2018-02-23 / Outra / Este trabalho apresenta um estudo de planos radioterápicos realizados com o Sistema de Planejamento de Tratamento (TPS) XiO 5.10, utilizados em centros de radioterapia. Foram analisados os protocolos clínicos de radioterapia através de pesquisa bibliográfica em artigos científicos e textos produzidos pelas sociedades especializadas da área da radioncologia. Foram realizadas visitas técnicas junto a serviços de radioterapia para melhor entendimento das diferenças entre os distintos sistemas de planejamentos radioterápicos aplicados. A pesquisa verificou a resposta de três diferentes algoritmos computacionais de cálculo de dose de radiação: Superposition, Convolution e Fast Superposition, disponíveis no TPS XiO® da Elekta Medical Systems. Para isto foram analisados os parâmetros de distribuição de dose para 22 planejamentos radioterápicos realizados em uma clínica de radioterapia privada, sendo: 1 paciente com metástase cerebral, 3 pacientes com tumores de cabeça e pescoço, 9 com câncer de mama e 9 com tumores da próstata. Também foi quantificada a influência da heterogeneidade do tecido irradiado, através dos métodos de correção de heterogeneidade do XiO. Independentemente da correção de heterogeneidade do tecido, de um modo geral, o algoritmo de Convolution tendeu a subestimar a dose no PTV quando comparado com os outros dois algoritmos de cálculo de dose. Não foram percebidas diferenças significativas nos valores de dose de radiação calculados para o PTV, determinadas com os outros dois algoritmos, no entanto, o método Superposition proporciona leve redução do tempo de processamento computacional. O trabalho pretende contribuir para uma melhor compreensão da sistemática de cálculo do TPS XiO e assim, beneficiar os usuários dos planejamentos computadorizados durante a análise dos indicadores da qualidade do plano de tratamento. / This paper bring forward a study of radiotherapeutic planning accomplished by XiO 5.10 treatment planning systems utilized in radiotherapic centers. The clinical protocols of radiotherapy were analyzed through bibliographic research in scientific articles and texts produced by societies related to the area of radioncology. . Technical reconnaissance visits were performed in radiotherapy services for a better understanding of the differences between the systems of radiotherapy planning applied. The research verified the response of three distinct computational algorithms of calculation of dose radiation: Superposition, Convolution and Fast Superposition, available in the Tps XiO® of Elekta Medical Systems. For this purpose, were analyzed the dose distribution parameters for 22 radiotherapy plans performed in a private radiotherapy clinic: 1 patient with cerebral metastasis, 3 patients with head and neck tumors, 9 with breast cancer and 9 with prostate tumors . The influence of the heterogeneity of the irradiated tissue was also quantified through the XiO heterogeneity correction methods. Regardless of the correction of tissue heterogeneity, the Convolution algorithm tended to, in general, underestimate the dose in the PTV when compared to the other two dose calculation algorithms. No significant differences were observed in the radiation dose values calculated for the PTV, determined with the other two algorithms, however, the Superposition method provides a slight reduction of computational processing time. This paper aims to contribute to a better understanding of the systematics of TPS XiO calculation and thus, to benefit the users of the computerized planning during the analysis of the quality indicators of the treatment plan. / empresa privada

New Virtual Reality technologies provide the possibility of widening access to information in data. Haptics, the technology of touch, could be an interesting future aid and have large impact on medical applications. The use of haptic devices allows computer users to use their sense of touch, in order to feel virtual objects with a high degree of realism. The aim of the thesis is to investigate the potential deployment and the benefits of using haptic force feedback instruments in maxillo-facial surgery. Based on a produced test application, the thesis includes suggested recommendations for future haptic implementations. At the Department of Maxillo-Facial Surgery, at the Karolinska Hospital in Stockholm, Virtual Reality technologies are used as an aid to a limited extent during the production of physical medical models. The physical medical models are produced with Rapid Prototyping techniques. This process is examined and described in the thesis. Moreover, the future of the physical medical models is outlined, and a future alternative visualizing patient data in 3D and use haptics as an interaction tool, is described. Furthermore, we have examined the present use of haptic technology in medicine, and the benefits of using the technology as an aid for diagnostic and treatment planning. Based on a presented literature study and an international outlook, we found that haptics could improve the management of medical models. The technology could be an aid, both for physical models as well as for virtual models. We found three different ways of implementing haptics in maxillo-facial surgery. A haptic system could be developed in order to only manage virtual medical models and be an alternative solution to the complete Rapid Prototyping process. A haptic system could serve as a software, handling the image processing and interfacing from a medical scanner to an Rapid Prototyping system. A haptic system could be developed as an alternative interaction tool, which could be implemented as an additional function in currently used image processing software, in order to improve the management of virtual medical models before the Rapid Prototyping process. An implementation for planning and examination in maxillo-facial surgery, using haptic force feedback interaction, is developed and evaluated. The test implementation is underlying our aim of investigating the potential deployment and the benefits of using haptic force feedback instruments in maxillo-facial surgery. After discussing the possible future of our implementation and the future of haptic force feedback in maxillo-facial surgery, a recommendation is given as a conclusion of our total work.