Planificación radioterápica de intensidad modulada basada en un modelo de simulación explícita del transporte de partículas mediante optimización por imágen médica

Falta resumen y palabras claveIntroducción: La planificación radioterápica más precisa es la basada en un cálculo del transporte explícito del haz de partículas, desde su generación en la cabeza del acelerador lineal, y en su interacción con los modificadores y colimadores, hasta la deposición de su energía en los tejidos del paciente con densidades heterogéneas. No obstante, este cálculo exige un tiempo de computación inviable para la práctica clínica diaria. En este trabajo se presenta un modelo de optimización de abertura directa que está exclusivamente basado en los datos de la imagen del paciente, y que se implementa en un sistema propio de planificación de tratamientos Monte Carlo (MCTPS), con objeto de resolver tratamientos de radioterapia complejos con resultados óptimos y en tiempos eficientes para ser adaptado a la práctica clínica. Método: El sistema de planificación es un sistema full Monte Carlo (fMC), controlado mediante una interfaz de Matlab®, que está basado en la generación de matrices, que conforman un mapa denominado biofísico, el cual es generado a partir de los datos de la imagen del paciente para conseguir un juego de segmentos realizable óptimo. En orden a reducir los tiempos de computación necesarios, el mapa de fluencia convencional ha sido sustituido por el conjunto de mapas biofísicos, el cual es secuenciado para proporcionar las aberturas que posteriormente serán pesadas mediante un algoritmo de optimización basado en un modelo de programación lineal, que permite optimizar la distribución de dosis al nivel del vóxel. Un algoritmo de ray-casting extrae del CT del paciente la información de las estructuras de interés, el espesor atravesado, así como los valores PET, si los hay. Los datos son guardados para generar los mapas biofísicos en cada incidencia. Estos mapas son los ficheros inputs de un secuenciador propio desarrollado para este fin. Se simularon espacios de fase para distintos aceleradores (Primus de Siemens y Axesse de Elekta) para varias energías (6, 9, 12, 15 MeVy 6 MV). Los espacios de fase fueron simulados con el código EGSnrc/BEAMnrc. El cálculo de dosis en el paciente fue simulado con el código BEAMDOSE. Este código es una versión modificada de EGSnrc/DOSXYZnrc capaz de calcular la dosis sobre cada vóxel debido a cada segmento, y así estar en disposición de combinarlos con diferentes pesos durante el proceso de optimización. Resultados: Se han estudiado casos complejos con distintas características, para chequear el algoritmo de planificación en situaciones en las que el cálculo MC ofrece un valor añadido: Un caso de cabeza y cuello (Caso I) con tres blancos delineados a partir de la imagen PET/CT y con un escalado de dosis exigente; un caso de mama parcial (Caso II) para ser resuelto con haces de electrones modulados (IMRT+MERT); y un caso de lecho prostático (Caso III) con una geometría cóncava acusada. En estos tres casos, las dosis de prescripción y límites en los órganos de riesgo fueron satisfactorias en un tiempo lo suficientemente corto como para permitir implementarlo en la rutina clínica. Estas soluciones teóricas fueron verificadas experimentalmente con éxito. Conclusiones: Se ha desarrollado un modelo de planificación de tratamiento Monte Carlo basado exclusivamente en mapas diseñados a partir de la imagen del paciente. La secuenciación de estos mapas permite obtener aberturas realizables las cuales son moduladas mediante una formulación de programación lineal, permitiendo una optimización de la dosis al nivel del vóxel. El modelo es capaz de resolver casos complejos de radioterapia con una gran precisión y empleando tiempos de computación asumibles para la aplicación clínica

Accurate,robust and harmonized implementation of morpho-functional imaging in treatment planning for personalized radiotherapy

In this work we present a methodology able to use harmonized PET/CT imaging in dose painting by number (DPBN) approach by means of a robust and accurate treatment planning system. Image processing and treatment planning were performed by using a Matlab-based platform, called CARMEN, in which a full Monte Carlo simulation is included. Linear programming formulation was developed for a voxel-by-voxel robust optimization and a specific direct aperture optimization was designed for an efficient adaptive radiotherapy implementation. DPBN approach with our methodology was tested to reduce the uncertainties associated with both, the absolute value and the relative value of the information in the functional image. For the same H&N case, a single robust treatment was planned for dose prescription maps corresponding to standardized uptake value distributions from two different image reconstruction protocols: One to fulfill EARL accreditation for harmonization of [18F]FDG PET/CT image, and the other one to use the highest available spatial resolution. Also, a robust treatment was planned to fulfill dose prescription maps corresponding to both approaches, the dose painting by contour based on volumes and our voxel-by-voxel DPBN. Adaptive planning was also carried out to check the suitability of our proposal. Different plans showed robustness to cover a range of scenarios for implementation of harmonizing strategies by using the highest available resolution. Also, robustness associated to discretization level of dose prescription according to the use of contours or numbers was achieved. All plans showed excellent quality index histogram and quality factors below 2%. Efficient solution for adaptive radiotherapy based directly on changes in functional image was obtained. We proved that by using voxel-by-voxel DPBN approach it is possible to overcome typical drawbacks linked to PET/CT images, providing to the clinical specialist confidence enough for routinely implementation of functional imaging for personalized radiotherapy.Junta de Andalucía (FISEVI, reference project CTS 2482)European Regional Development Fund (FEDER

3D VMAT Verification Based on Monte Carlo Log File Simulation with Experimental Feedback from Film Dosimetry.

A model based on a specific phantom, called QuAArC, has been designed for the evaluation of planning and verification systems of complex radiotherapy treatments, such as volumetric modulated arc therapy (VMAT). This model uses the high accuracy provided by the Monte Carlo (MC) simulation of log files and allows the experimental feedback from the high spatial resolution of films hosted in QuAArC. This cylindrical phantom was specifically designed to host films rolled at different radial distances able to take into account the entrance fluence and the 3D dose distribution. Ionization chamber measurements are also included in the feedback process for absolute dose considerations. In this way, automated MC simulation of treatment log files is implemented to calculate the actual delivery geometries, while the monitor units are experimentally adjusted to reconstruct the dose-volume histogram (DVH) on the patient CT. Prostate and head and neck clinical cases, previously planned with Monaco and Pinnacle treatment planning systems and verified with two different commercial systems (Delta4 and COMPASS), were selected in order to test operational feasibility of the proposed model. The proper operation of the feedback procedure was proved through the achieved high agreement between reconstructed dose distributions and the film measure- ments (global gamma passing rates > 90% for the 2%/2 mm criteria). The necessary discre- tization level of the log file for dose calculation and the potential mismatching between calculated control points and detection grid in the verification process were discussed. Besides the effect of dose calculation accuracy of the analytic algorithm implemented in treatment planning systems for a dynamic technique, it was discussed the importance of the detection density level and its location in VMAT specific phantom to obtain a more reliable DVH in the patient CT. The proposed model also showed enough robustness and efficiency to be considered as a pre-treatment VMAT verification system.Ministerio de Ciencia y Tecnología SAF2011- 27116; IPT-2011-1480-900000

MCTP system model based on linear programming optimization of apertures obtained from sequencing patient image data maps

Purpose: We present a hybrid direct MLC aperture optimization model exclusively based on sequencing of patient imaging data to be implemented on a Monte Carlo 30 treatment planning system (MC-TPS) to allow the explicit radiation transport simulation of advanced radiotherapy treatments with optimal results in efficient times for clinical practice. Methods: The planning system (called CARMEN) is a full MC-TPS, controlled 35 through a MatLab interface, which is based on the sequencing of a novel map, called 'biophysical' map, which is generated from enhanced image data of patients to achieve a set of segments actually deliverable. In order to reduce the required computation time, the conventional fluence map has been replaced by the biophysical map which is sequenced to provide direct apertures that will later be weighted by means of an 40 optimization algorithm based on linear programming. A ray-casting algorithm throughout the patient CT assembles information about the found structures, the mass thickness crossed, as well as PET values. Data are recorded to generate a biophysical map for each gantry angle. These maps are the input files for a home-made sequencer developed to take into account the interactions of photons and electrons with the 45 multileaf collimator (MLC). For each linac (Axesse of Elekta and Primus of Siemens) and energy beam studied (6, 9, 12, 15 MeV and 6MV), phase space files were simulated with the EGSnrc/BEAMnrc code. The dose calculation in patient was carried out with the BEAMDOSE code. This code is a modified version of EGSnrc/DOSXYZnrc able to 50 calculate the beamlet dose in order to combine them with different weights during the optimization process. Results: Three complex radiotherapy treatments were selected to check the reliability of CARMEN in situations where the MC calculation can offer an added value: A head-and-neck case (Case I) with three targets delineated on PET/CT images and a 55 demanding dose-escalation; a partial breast irradiation case (Case II) solved with photon and electron modulated beams (IMRT+MERT); and a prostatic bed case (Case III) with a pronounced concave-shaped PTV by using VMAT. In all cases, the required target prescription doses and constraints on organs at risk were fulfilled using in a short enough time to allow routine clinical implementation of such a MC-TPS for similar specialized cases. The quality assurance protocol followed to check CARMEN system showed a high agreement with the experimental measurements. Conclusions: A Monte Carlo treatment planning model exclusively based on maps performed from patient imaging data has been presented. The sequencing of these maps allows obtaining deliverable apertures which are weighted for modulation under 65 a linear programming formulation. The model is able to solve complex radiotherapy treatments with high accuracy in an efficient computation tim

Dose painting by means of Monte Carlo treatment planning at the voxel level

Purpose To develop a new optimization algorithm to carry out true dose painting by numbers (DPBN) planning based on full Monte Carlo (MC) calculation. Methods Four configurations with different clustering of the voxel values from PET data were proposed. An optimization method at the voxel level under Lineal Programming (LP) formulation was used for an inverse planning and implemented in CARMEN, an in-house Monte Carlo treatment planning system. Results Beamlet solutions fulfilled the objectives and did not show significant differences between the different configurations. More differences were observed between the segment solutions. The plan for the dose prescription map without clustering was the better solution. Conclusions LP optimization at voxel level without dose-volume restrictions can carry out true DPBN planning with the MC accuracy