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3D optimal light distribution in brain tumors for photodynamic therapy

Abstract: Photodynamic therapy is a treatment technique that takes advantage of the effects induced by the body itself, together with a photosensitizer, to destroy unwanted tumor volumes with high accuracy and low invasiveness. This study analyzes treatment volume by 3D optical distributions in a realistic way from MRI images. First of all a volumetric model of a real head is built from MRI images. Optical distributions generated by the source over the tissue are considered at different brain tumor stages, and with multitude of processes that occur within the volume to be treated. By means of Monte Carlo we can estimate the photonic density that is absorbed by the tissues, whose optical properties are previously collected. This application considers that a reasonable time has passed for the photosensitizer to have reached the area under study, and that there is a minimum concentration in adjoining areas during radiation exposure. With this approach it is possible to estimate the level of radiation exposure and the affected volume. This is very relevant due to the fact that, as the radiation increases, different areas with different energy densities appear. This makes it much more complicated to apply a certain known optimal radiation on the treatment volume. A non-optimal high radiation density would damage healthy tissue, while, on the contrary, a non-optimal low radiation would not bring unwanted tissue to necrosis or apoptosis for tumor destruction, generating recurrence. This tool could be of great interest in treatment planning.

Otras comunicaciones del congreso o articulos relacionados con autores/as de la Universidad de Cantabria

 Autoría: de la Nuez-Díaz R., Arce-Diego J.L., Fanjul-Vélez F.,

 Congreso: Optical Interactions with Tissue and Cells (32ª : 2022 : En línea)

Editorial: SPIE Society of Photo-Optical Instrumentation Engineers

 Fecha de publicación: 05/03/2021

Nº de páginas: 4

Tipo de publicación: Comunicación a Congreso

 DOI: 10.1117/12.2578313

ISSN: 0277-786X,1996-756X

Proyecto español: PGC2018-101464-B-I00

Url de la publicación: https://doi.org/10.1117/12.2578313