Ancari Iñiguez, Luis A. (2022) Simulación de radiocirugía craneal estereotáctica guiada por resonancia magnética funcional / Simulation of stereotactic cranial radiosurgery guide by functional magnetic resonance. Maestría en Física Médica, Universidad Nacional de Cuyo, Instituto Balseiro.
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Resumen en español
El objetivo de este trabajo fue reconocer e incorporar redes neuronales en estado de reposo (RSN) a los sistemas de planificación a partir de estudios rs-fMRI y simular planes de tratamientos de radiocirugía craneal estereotáctica (SRS) con la información anatómica tradicional y la información funcional de las RSN. La SRS es una técnica no invasiva utilizada para tratar lesiones intracraneales a través de múltiples haces que entregan altas dosis al objetivo en pocas fracciones. Las dosis recibidas por las regiones funcionales pueden ser reducidas a través su incorporación como órganos en riesgo (OAR) en el proceso de planificación de radioterapia. Se utilizaron tres pacientes con tumores cerebrales para el desarrollo de los planes de tratamiento, contaban con estudios 3DGRE-T1, rs-fMRI y CT. Para encontrar las redes neuronales en estado de reposo RSN se realizó un análisis bidimensional de componentes independientes (ICA) con la función MELODIC, seguido de un proceso semiautomático de clasificación combinando FIX y la clasificación manual. Posteriormente se seleccionaron las redes visual, motora y DMN para ser incorporadas al TPS, para ello se desarrolló un programa en Python que permite la conversión de archivos NIfTI a DICOM, el programa consideró la integridad de los datos, adaptación del marco de referencia NIfTI al sistema DICOM y la asignación de UID. El procedimiento semiautomático para clasificar y reconocer las RSN demostró una eficiencia comparable a la clasificación manual, con una reducción considerable del tiempo requerido en esta tarea. Se convirtió y adecuó satisfactoriamente las RSN Nifti al formato DICOM, siendo reconocidos y asignados correctamente a los pacientes en el TPS. Al incorporar las redes visual, motor y DMN como fOAR en los planes de tratamiento utilizando técnicas de VMAT coplanar y no-coplanar, se pudo reducir con éxito las dosis de la red visual en 15 % y 18 % aproximadamente, sin comprometer la dosis del objetivo ni superar los límites de los OAR convencionales. Este estudio demostró la factibilidad de incorporar RSNs en los planes de tratamiento en RT con una posible aplicación clínica.
Resumen en inglés
The aim of this work was to recognize and incorporate resting-state neural networks (RSNs) into planning systems from rs-fMRI studies and simulate stereotactic cranial radiosurgery (SRS) treatment plans with traditional anatomical and rs-fMRI information. SRS is a non-invasive technique used to treat intracranial lesions through multiple beams that deliver high doses to the target in few fractions. The doses received by the functional regions can be reduced through their incorporation as organs at risk (OAR) in the radiotherapy planning process. Three patients with brain tumors were used for the development of treatment plans, they had 3DGRE-T1, rs-fMRI and CT studies. To find the RSN resting-state neural networks, a two-dimensional independent component analysis (ICA) was performed with the MELODIC function, followed by a semi-automatic classification process combining FIX and manual classification. Subsequently, the visual, motor and DMN networks were selected to be incorporated into the TPS, for this a program was developed in Python that allows the conversion of NIfTI files to DICOM, the program considered the integrity of the data, adaptation of the NIfTI reference framework to the DICOM system and UID assignment. The semi-automated procedure to classify and recognize RSNs demonstrated an efficiency comparable to manual classification, with a considerable reduction in the time required for this task. The Nifti RSNs were successfully converted and adapted to the DICOM format, being recognized and correctly assigned to patients in the TPS. By incorporating the visual, motor and DMN networks as fOAR in the treatment plans using coplanar and non-coplanar VMAT techniques, it was possible to successfully reduce the visual network doses by approximately 15% and 18%, without compromising the target dose or exceed the limits of conventional OARs. This study demonstrated the feasibility of incorporating RSNs into RT treatment plans with possible clinical application.
| Tipo de objeto: | Tesis (Maestría en Física Médica) |
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| Palabras Clave: | Magnetic resonance, Resonancia magnética; Neural networks; Redes neuronales; [ Stereotactic radiosurgery; Radiocirugía estereotáctica; Resting state functional; Funcional en estado de reposo; Independent component analysis; Análisis de competentes independientes; Multilevel classifier; Clasificador multinivel; Volumetric modulated arc therapy; Arco terapia volumétrica modulada] |
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Notas de clase: Resonancia magnética, February 2021. |
| Materias: | Medicina > Radiocirugía estereotáctica Medicina > Imágenes funcionales |
| Divisiones: | FUESMEN |
| Código ID: | 1136 |
| Depositado Por: | Tamara Cárcamo |
| Depositado En: | 10 Aug 2023 12:01 |
| Última Modificación: | 10 Aug 2023 12:01 |
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