Matheurística para os problemas da geometria e da intensidade em IMRT
Autor(a) principal: | |
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Data de Publicação: | 2018 |
Tipo de documento: | Trabalho de conclusão de curso |
Idioma: | por |
Título da fonte: | Repositório Institucional da UFRN |
Texto Completo: | https://repositorio.ufrn.br/handle/123456789/34212 |
Resumo: | Radiotherapy is a form of treatment of cancerous tissues by means of ionizing radiation. The fundamental idea of radiotherapy treatment is to administer a dose of radiation to the tumor region sufficient to destroy it, sparing the anatomical healthy structures. The complete planning of a radiotherapy treatment consists of the following steps: (I) select the beam angles, (II) calculate the intensity of the beams, and (III) to define a radiation delivery sequence (ACOSTA et al., 2008). Such steps can be approached as NP-hard optimization problems, having different mathematical models and a variety of algorithms and techniques applicable for such. In general, these models propose objective functions that somehow penalize excess of radiation in healthy and noble tissues and insufficient dose in the tumor. After literature review, the model presented in (OBAL, 2016) was adopted. Such model refers to problems (I) and (II), commonly called geometry problem and intensity problem, respectively. Its solution methods consists of hybridization of metaheuristics with Simplex, an approach known in the literature as a matheuristic. The metaheuristics perform the search for beam sets, whereas the Simplex calculates the intensity of the beams, using weighting factors for the objective functions. Based on this work, this monograph proposes a matheuristic that hybridizes Tabu Search accompanied by the ejection chain technique with the Simplex method. The two methods are employed in the same way as in (OBAL, 2016), but stands out the differentiation between searches. In the Tabu Search proposed here, besides the neighborhood exploration through the ejection chain, it was also decided by the exploration of different sizes of beam sets, as well as the use of random-restart of the solution. For the evaluation of the proposed algorithm, adapted test cases from (BREEDVELD; HEIJMEN, 2017) are used. Rather than considering cases in their entirety, only a subset of regions is handled by the algorithm.The analysis of the results suggests that the approach proposed in this work is able to obtain radiotherapeutic treatments of better quality compared to the algorithm of (OBAL, 2016). |
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Cunha Neto, Luis Tertulino daGoldbarg, Elizabeth Ferreira GouvêaGoldbarg, Marco CésarMaia, Sílvia Maria Diniz Monteiro2018-07-03T11:03:21Z2021-09-20T11:47:16Z2018-07-03T11:03:21Z2021-09-20T11:47:16Z201820170008288CUNHA NETO, Luis Tertulino da. Matheurística para os problemas da geometria e da intensidade em IMRT. 2018. 53 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal do Rio Grande do Norte, Natal, 2018.https://repositorio.ufrn.br/handle/123456789/34212Radiotherapy is a form of treatment of cancerous tissues by means of ionizing radiation. The fundamental idea of radiotherapy treatment is to administer a dose of radiation to the tumor region sufficient to destroy it, sparing the anatomical healthy structures. The complete planning of a radiotherapy treatment consists of the following steps: (I) select the beam angles, (II) calculate the intensity of the beams, and (III) to define a radiation delivery sequence (ACOSTA et al., 2008). Such steps can be approached as NP-hard optimization problems, having different mathematical models and a variety of algorithms and techniques applicable for such. In general, these models propose objective functions that somehow penalize excess of radiation in healthy and noble tissues and insufficient dose in the tumor. After literature review, the model presented in (OBAL, 2016) was adopted. Such model refers to problems (I) and (II), commonly called geometry problem and intensity problem, respectively. Its solution methods consists of hybridization of metaheuristics with Simplex, an approach known in the literature as a matheuristic. The metaheuristics perform the search for beam sets, whereas the Simplex calculates the intensity of the beams, using weighting factors for the objective functions. Based on this work, this monograph proposes a matheuristic that hybridizes Tabu Search accompanied by the ejection chain technique with the Simplex method. The two methods are employed in the same way as in (OBAL, 2016), but stands out the differentiation between searches. In the Tabu Search proposed here, besides the neighborhood exploration through the ejection chain, it was also decided by the exploration of different sizes of beam sets, as well as the use of random-restart of the solution. For the evaluation of the proposed algorithm, adapted test cases from (BREEDVELD; HEIJMEN, 2017) are used. Rather than considering cases in their entirety, only a subset of regions is handled by the algorithm.The analysis of the results suggests that the approach proposed in this work is able to obtain radiotherapeutic treatments of better quality compared to the algorithm of (OBAL, 2016).A radioterapia é uma forma de tratamento de tecidos cancerígenos por meio de radiação ionizante. A ideia fundamental do tratamento radioterápico é administrar uma dose de radiação direcionada à região tumoral suficiente para destruí-lo, poupando as estruturas anatômicas saudáveis. O completo planejamento de um tratamento radioterápico consiste nas seguintes etapas: (I) selecionar os ângulos dos feixes, (II) calcular a intensidade dos feixes, e (III) definir uma sequência de entrega da radiação (ACOSTA et al., 2008). Tais etapas podem ser abordadas como problemas de otimização NP-difíceis, tendo diferentes modelos matemáticos e uma variedade de algoritmos e técnicas aplicáveis para tais. De modo geral, esses modelos propõem funções objetivo que de alguma forma penalizam excesso de radiação em tecidos saudáveis e nobres e insuficiência de dose no tumor. Após revisão da literatura, optou-se pela adoção do modelo presente em (OBAL, 2016), referente aos problemas (I) e (II), comumente chamados também de problema da geometria e da intensidade, respectivamente. Seus métodos de solução consistem na hibridização de meta-heurísticas com o Simplex, abordagem conhecida na literatura como matheurística. As meta-heurísticas realizam a busca por conjuntos de feixes, enquanto que o Simplex é utilizado para calcular a intensidade dos feixes, utilizando ponderação para as funções objetivo. Fundamentado em tal trabalho, esta monografia propõe uma matheurística que hibridiza a Busca Tabu acompanhada da técnica ejection chain com o método Simplex. Os dois métodos são empregados da mesma maneira que em (OBAL, 2016), mas destacase a diferenciação entre as buscas. Na Busca Tabu proposta neste trabalho, além da exploração da vizinhança através da ejection chain, também foi decidido pela exploração de conjuntos de feixes de diferentes tamanhos, bem como o emprego de reinício aleatório da solução. Para a avaliação do algoritmo proposto, são utilizados casos de teste adaptadosde (BREEDVELD; HEIJMEN, 2017). Ao invés de considerar os casos em sua integridade, apenas um subconjunto de regiões é tratado pelo método. A análise dos resultados sugere que a abordagem proposta neste trabalho consegue obter tratamentos radioterápicos de melhor qualidade em comparação ao algoritmo de (OBAL, 2016).Universidade Federal do Rio Grande do NorteUFRNBrasilCiência da ComputaçãoRadioterapiaSeleção de feixesCálculo de intensidadeMatheurísticaMatheurística para os problemas da geometria e da intensidade em IMRTMatheuristic for geometry and intensity problems in IMRTinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/bachelorThesisinfo:eu-repo/semantics/openAccessporreponame:Repositório Institucional da UFRNinstname:Universidade Federal do Rio Grande do Norte (UFRN)instacron:UFRNTEXTMatheuristicaGeometria_CunhaNeto_2018.pdf.txtExtracted texttext/plain78966https://repositorio.ufrn.br/bitstream/123456789/34212/1/MatheuristicaGeometria_CunhaNeto_2018.pdf.txtdd740335ac4b135e3bde9d5159263784MD51CC-LICENSElicense_urlapplication/octet-stream49https://repositorio.ufrn.br/bitstream/123456789/34212/2/license_urlc88a6ae6c9d9f516a5e32ed747104d8fMD52license_textapplication/octet-stream0https://repositorio.ufrn.br/bitstream/123456789/34212/3/license_textd41d8cd98f00b204e9800998ecf8427eMD53license_rdfapplication/octet-stream0https://repositorio.ufrn.br/bitstream/123456789/34212/4/license_rdfd41d8cd98f00b204e9800998ecf8427eMD54ORIGINALMatheuristicaGeometria_CunhaNeto_2018.pdfMonografiaapplication/pdf2369689https://repositorio.ufrn.br/bitstream/123456789/34212/5/MatheuristicaGeometria_CunhaNeto_2018.pdfd0f467f03271c31a11c220c4a737f032MD55LICENSElicense.txttext/plain756https://repositorio.ufrn.br/bitstream/123456789/34212/6/license.txta80a9cda2756d355b388cc443c3d8a43MD56123456789/342122021-09-20 08:47:16.762oai:https://repositorio.ufrn.br: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ório de PublicaçõesPUBhttp://repositorio.ufrn.br/oai/opendoar:2021-09-20T11:47:16Repositório Institucional da UFRN - Universidade Federal do Rio Grande do Norte (UFRN)false |
dc.title.pr_BR.fl_str_mv |
Matheurística para os problemas da geometria e da intensidade em IMRT |
dc.title.alternative.pr_BR.fl_str_mv |
Matheuristic for geometry and intensity problems in IMRT |
title |
Matheurística para os problemas da geometria e da intensidade em IMRT |
spellingShingle |
Matheurística para os problemas da geometria e da intensidade em IMRT Cunha Neto, Luis Tertulino da Radioterapia Seleção de feixes Cálculo de intensidade Matheurística |
title_short |
Matheurística para os problemas da geometria e da intensidade em IMRT |
title_full |
Matheurística para os problemas da geometria e da intensidade em IMRT |
title_fullStr |
Matheurística para os problemas da geometria e da intensidade em IMRT |
title_full_unstemmed |
Matheurística para os problemas da geometria e da intensidade em IMRT |
title_sort |
Matheurística para os problemas da geometria e da intensidade em IMRT |
author |
Cunha Neto, Luis Tertulino da |
author_facet |
Cunha Neto, Luis Tertulino da |
author_role |
author |
dc.contributor.referees1.none.fl_str_mv |
Goldbarg, Elizabeth Ferreira Gouvêa |
dc.contributor.referees2.none.fl_str_mv |
Goldbarg, Marco César |
dc.contributor.author.fl_str_mv |
Cunha Neto, Luis Tertulino da |
dc.contributor.advisor1.fl_str_mv |
Maia, Sílvia Maria Diniz Monteiro |
contributor_str_mv |
Maia, Sílvia Maria Diniz Monteiro |
dc.subject.pr_BR.fl_str_mv |
Radioterapia Seleção de feixes Cálculo de intensidade Matheurística |
topic |
Radioterapia Seleção de feixes Cálculo de intensidade Matheurística |
description |
Radiotherapy is a form of treatment of cancerous tissues by means of ionizing radiation. The fundamental idea of radiotherapy treatment is to administer a dose of radiation to the tumor region sufficient to destroy it, sparing the anatomical healthy structures. The complete planning of a radiotherapy treatment consists of the following steps: (I) select the beam angles, (II) calculate the intensity of the beams, and (III) to define a radiation delivery sequence (ACOSTA et al., 2008). Such steps can be approached as NP-hard optimization problems, having different mathematical models and a variety of algorithms and techniques applicable for such. In general, these models propose objective functions that somehow penalize excess of radiation in healthy and noble tissues and insufficient dose in the tumor. After literature review, the model presented in (OBAL, 2016) was adopted. Such model refers to problems (I) and (II), commonly called geometry problem and intensity problem, respectively. Its solution methods consists of hybridization of metaheuristics with Simplex, an approach known in the literature as a matheuristic. The metaheuristics perform the search for beam sets, whereas the Simplex calculates the intensity of the beams, using weighting factors for the objective functions. Based on this work, this monograph proposes a matheuristic that hybridizes Tabu Search accompanied by the ejection chain technique with the Simplex method. The two methods are employed in the same way as in (OBAL, 2016), but stands out the differentiation between searches. In the Tabu Search proposed here, besides the neighborhood exploration through the ejection chain, it was also decided by the exploration of different sizes of beam sets, as well as the use of random-restart of the solution. For the evaluation of the proposed algorithm, adapted test cases from (BREEDVELD; HEIJMEN, 2017) are used. Rather than considering cases in their entirety, only a subset of regions is handled by the algorithm.The analysis of the results suggests that the approach proposed in this work is able to obtain radiotherapeutic treatments of better quality compared to the algorithm of (OBAL, 2016). |
publishDate |
2018 |
dc.date.accessioned.fl_str_mv |
2018-07-03T11:03:21Z 2021-09-20T11:47:16Z |
dc.date.available.fl_str_mv |
2018-07-03T11:03:21Z 2021-09-20T11:47:16Z |
dc.date.issued.fl_str_mv |
2018 |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
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info:eu-repo/semantics/bachelorThesis |
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bachelorThesis |
status_str |
publishedVersion |
dc.identifier.pr_BR.fl_str_mv |
20170008288 |
dc.identifier.citation.fl_str_mv |
CUNHA NETO, Luis Tertulino da. Matheurística para os problemas da geometria e da intensidade em IMRT. 2018. 53 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal do Rio Grande do Norte, Natal, 2018. |
dc.identifier.uri.fl_str_mv |
https://repositorio.ufrn.br/handle/123456789/34212 |
identifier_str_mv |
20170008288 CUNHA NETO, Luis Tertulino da. Matheurística para os problemas da geometria e da intensidade em IMRT. 2018. 53 f. TCC (Graduação) - Curso de Ciência da Computação, Universidade Federal do Rio Grande do Norte, Natal, 2018. |
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https://repositorio.ufrn.br/handle/123456789/34212 |
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Universidade Federal do Rio Grande do Norte |
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Brasil |
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Ciência da Computação |
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Universidade Federal do Rio Grande do Norte |
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UFRN |
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