Stochastic local search algorithms for the direct aperture optimisation problem in IMRT

Leslie Pérez Cáceres, Ignacio Araya, Denisse Soto, Guillermo Cabrera-Guerrero

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

2 Citas (Scopus)

Resumen

In this paper, two heuristic algorithms are proposed to solve the direct aperture optimisation problem (DAO) in radiation therapy for cancer treatment. In the DAO problem, the goal is to find a set of deliverable aperture shapes and intensities so we can irradiate the tumor according to a medical prescription without producing any harm to the surrounding healthy tissues. Unlike the traditional two-step approach used in intensity modulated radiation therapy (IMRT) where the intensities are computed and then the apertures shapes are determined by solving a sequencing problem, in the DAO problem, constraints associated to the number of deliverable aperture shapes as well as physical constraints are taken into account during the intensities optimisation process. Thus, we do not longer need any leaves sequencing procedure after solving the DAO problem. We try our heuristic algorithms on a prostate case and compare the obtained treatment plan to the one obtained using the traditional two-step approach. Results show that our algorithms are able to find treatment plans that are very competitive when considering the number of deliverable aperture shapes.

Idioma originalInglés
Título de la publicación alojadaHybrid Metaheuristics - 11th International Workshop, HM 2019, Proceedings
EditoresMaria J. Blesa Aguilera, Christian Blum, Pedro Pinacho-Davidson, Julio Godoy del Campo, Haroldo Gambini Santos
EditorialSpringer Verlag
Páginas108-123
Número de páginas16
ISBN (versión impresa)9783030059828
DOI
EstadoPublicada - 2019
Publicado de forma externa
Evento11th International Workshop on Hybrid Metaheuristics, HM 2019 - Concepción, Chile
Duración: 16 ene 201918 ene 2019

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11299 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia11th International Workshop on Hybrid Metaheuristics, HM 2019
País/TerritorioChile
CiudadConcepción
Período16/01/1918/01/19

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