Energy Deposition in the Breast During CT Scanning: Quantification and Implications for Dose Reduction

Studies suggest that dose to the breast leads to a higher lifetime attributable cancer incidence risk from a chest CT scan for women compared to men. Numerous methods have been proposed for reducing dose to the breast during CT scanning, including bismuth shielding, tube current modulation, partial-angular scanning, and reduced kVp. These methods differ in how they alter the spectrum and fluence across projection angle. This study used Monte Carlo CT simulations of a voxelized female phantom to investigate the energy (dose) deposition in the breast as a function of both photon energy and projection angle. The resulting dose deposition matrix was then used to investigate several questions regarding dose reduction to the breast: (1) Which photon energies deposit the most dose in the breast, (2) How does increased filtration compare to tube current reduction in reducing breast dose, and (3) Do reduced kVp scans reduce dose to breast, and if so, by what mechanism? The results demonstrate that while high-energy photons deposit more dose per emitted photon, the low-energy photons deposit more dose to the breast for a 120 kVp acquisition. The results also demonstrate that decreasing the tube current for the AP views to match the fluence exiting a shield deposits nearly the same dose to the breast as when using a shield (within ~1%). Finally, results suggest that the dose reduction observed during lower kVp scans is caused by reduced photon fluence rather than the elimination of high-energy photons from the beam. Overall, understanding the mechanisms of dose deposition in the breast as a function of photon energy and projection angle enables comparisons of dose reduction methods and facilitates further development of optimized dose reduction schemes

Development of advanced geometric models and acceleration techniques for Monte Carlo simulation in Medical Physics

Els programes de simulació Monte Carlo de caràcter general s'utilitzen actualment en una gran varietat d'aplicacions.Tot i això, els models geomètrics implementats en la majoria de programes imposen certes limitacions a la forma dels objectes que es poden definir. Aquests models no són adequats per descriure les superfícies arbitràries que es troben en estructures anatòmiques o en certs aparells mèdics i, conseqüentment, algunes aplicacions que requereixen l'ús de models geomètrics molt detallats no poden ser acuradament estudiades amb aquests programes.L'objectiu d'aquesta tesi doctoral és el desenvolupament de models geomètrics i computacionals que facilitin la descripció dels objectes complexes que es troben en aplicacions de física mèdica. Concretament, dos nous programes de simulació Monte Carlo basats en PENELOPE han sigut desenvolupats. El primer programa, penEasy, utilitza un algoritme de caràcter general estructurat i inclou diversos models de fonts de radiació i detectors que permeten simular fàcilment un gran nombre d'aplicacions. Les noves rutines geomètriques utilitzades per aquest programa, penVox, extenen el model geomètric estàndard de PENELOPE, basat en superfícices quàdriques, per permetre la utilització d'objectes voxelitzats. Aquests objectes poden ser creats utilitzant la informació anatòmica obtinguda amb una tomografia computeritzada i, per tant, aquest model geomètric és útil per simular aplicacions que requereixen l'ús de l'anatomia de pacients reals (per exemple, la planificació radioterapèutica). El segon programa, penMesh, utilitza malles de triangles per definir la forma dels objectes simulats. Aquesta tècnica, que s'utilitza freqüentment en el camp del disseny per ordinador, permet representar superfícies arbitràries i és útil per simulacions que requereixen un gran detall en la descripció de la geometria, com per exemple l'obtenció d'imatges de raig x del cos humà.Per reduir els inconvenients causats pels llargs temps d'execució, els algoritmes implementats en els nous programes s'han accelerat utilitzant tècniques sofisticades, com per exemple una estructura octree. També s'ha desenvolupat un paquet de programari per a la paral·lelització de simulacions Monte Carlo, anomentat clonEasy, que redueix el temps real de càlcul de forma proporcional al nombre de processadors que s'utilitzen.Els programes de simulació que es presenten en aquesta tesi són gratuïts i de codi lliures. Aquests programes s'han provat en aplicacions realistes de física mèdica i s'han comparat amb altres programes i amb mesures experimentals.Per tant, actualment ja estan llestos per la seva distribució pública i per la seva aplicació a problemes reals.Monte Carlo simulation of radiation transport is currently applied in a large variety of areas. However, the geometric models implemented in most general-purpose codes impose limitations on the shape of the objects that can be defined. These models are not well suited to represent the free-form (i.e., arbitrary) shapes found in anatomic structures or complex medical devices. As a result, some clinical applications that require the use of highly detailed phantoms can not be properly addressed.This thesis is devoted to the development of advanced geometric models and accelration techniques that facilitate the use of state-of-the-art Monte Carlo simulation in medical physics applications involving detailed anatomical phantoms. To this end, two new codes, based on the PENELOPE package, have been developed. The first code, penEasy, implements a modular, general-purpose main program and provides various source models and tallies that can be readily used to simulate a wide spectrum of problems. Its associated geometry routines, penVox, extend the standard PENELOPE geometry, based on quadric surfaces, to allow the definition of voxelised phantoms. This kind of phantoms can be generated using the information provided by a computed tomography and, therefore, penVox is convenient for simulating problems that require the use of the anatomy of real patients (e.g., radiotherapy treatment planning). The second code, penMesh, utilises closed triangle meshes to define the boundary of each simulated object. This approach, which is frequently used in computer graphics and computer-aided design, makes it possible to represent arbitrary surfaces and it is suitable for simulations requiring a high anatomical detail (e.g., medical imaging).A set of software tools for the parallelisation of Monte Carlo simulations, clonEasy, has also been developed. These tools can reduce the simulation time by a factor that is roughly proportional to the number of processors available and, therefore, facilitate the study of complex settings that may require unaffordable execution times in a sequential simulation.The computer codes presented in this thesis have been tested in realistic medical physics applications and compared with other Monte Carlo codes and experimental data. Therefore, these codes are ready to be publicly distributed as free and open software and applied to real-life problems.Postprint (published version

Automation of the Monte Carlo simulation of medical linear accelerators

La consulta íntegra de la tesi, inclosos els articles no comunicats públicament per drets d'autor, es pot realitzar prèvia petició a l'Arxiu de la UPCThe main result of this thesis is a software system, called PRIMO, which simulates clinical linear accelerators and the subsequent dose distributions using the Monte Carlo method. PRIMO has the following features: (i) it is self- contained, that is, it does not require additional software libraries or coding; (ii) it includes a geometry library with most Varian and Elekta linacs; (iii) it is based on the general-purpose Monte Carlo code PENELOPE; (iv) it provides a suite of variance-reduction techniques and distributed parallel computing to enhance the simulation efficiency; (v) it is graphical user interfaced; and (vi) it is freely distributed through the website http://www.primoproject.net In order to endow PRIMO with these features the following tasks were conducted: - PRIMO was conceived with a layered structure. The topmost layer, named the GLASS, was developed in this thesis. The GLASS implements the GUI, drives all the functions of the system and performs the analysis of results. Lower layers generate geometry files, provide input data and execute the Monte Carlo simulation. - The geometry of Elekta linacs from series SU and MLCi were coded in the PRIMO system. - A geometrical model of the Varian True Beam linear accelerator was developed and validated. This model was created to surmount the limitations of the Varian distributed phase-space files and the absence of released information about the actual geometry of that machine. This geometry model was incorporated into PRIMO. - Two new variance-reduction techniques, named splitting roulette and selective splitting, were developed and validated. In a test made with an Elekta linac it was found that when both techniques are used in conjunction the simulation efficiency improves by a factor of up to 45. - A method to automatically distribute the simulation among the available CPU cores of a computer was implemented. The following investigations were done using PRIMO as a research tool : - The configu ration of the condensed history transport algorithm for charged particles in PENELOPE was optimized for linac simulation. Dose distributions in the patient were found to be particularly sensitive to the values of the transport parameters in the linac target. Use of inadequate values of these parameters may lead to an incorrect determination of the initial beam configuration or to biased dose distributions. - PRIMO was used to simulate phase-space files distributed by Varian for the True Beam linac. The results were compared with experimental data provided by five European radiotherapycenters. It was concluded thatthe latent variance and the accuracy of the phase-space files were adequate for the routine clinical practice. However, for research purposes where low statistical uncertainties are required the phase-space files are not large enough. To the best of our knowledge PRIMO is the only fully Monte Carlo-based linac and dose simulation system , addressed to research and dose verification, that does not require coding tasks from end users and is publicly available.El principal resultado de esta tesis es un sistema informático llamado PRIMO el cual simula aceleradores lineales médicos y las subsecuentes distribuciones de dosis empleando el método de Monte Carlo. PRIMO tiene las siguiente características: (i) es auto contenido, o sea no requiere de librerías de código ni de programación adicional ; (ii) incluye las geometrías de los principales modelos de aceleradores Varían y Elekta; (iii) está basado en el código Monte Carlo de propósitos generales PENELOPE; (iv) contiene un conjunto de técnicas de reducción de varianza y computación paralela distribuida para mejorar la eficiencia de simulación; (v) tiene una interfaz gráfica de usuario; y (vi) se distribuye gratis en el sitio web http://vvww.primoproject.net. Para dotar a PRIMO de esas características, se realizaron las tareas siguientes: - PRIMO se concibió con una estructura de capas. La capa superior, nombrada GLASS, fue desarrollada en esta tesis. GLASS implementa la interfazgráfica de usuario, controla todas las funciones del sistema y realiza el análisis de resultados. Las capas inferiores generan los archivos de geometría y otros datos de entrada y ejecutan la simulación Monte Carlo. - Se codificó en el sistema PRIMO la geometría de los aceleradores Elekta de las series SLi y MLC. - Se desarrolló y validó un modelo geométrico del acelerador TrueBeam de Varian. Este modelo fue creado para superar las limitaciones de los archivos de espacio de fase distribuidos por Varian, así como la ausencia de información sobre la geometría real de esta máquina. Este modelo geométrico fue incorporado en PRIMO. - Fueron desarrolladas y validadas dos nuevas técnicas de reducción de varianza nombradas splitting roulette y selective splitting. En pruebas hechas en un acelerador Elekta se encontró que cuando ambas técnicas se usan en combinación, la eficiencia de simulación mejora 45 veces. - Se implementó un método para distribuir la simulación entre los procesadores disponibles en un ordenador. Las siguientes investigaciones fueron realizadas usando PRIMO como herramienta: - Fue optimizada la configuración del algoritmo de PENELOPE para el transporte de partículas cargadas con historia condensada en la simulación del linac. Se encontró que las distribuciones de dosis en el paciente son particularmente sensibles a los valores de los parámetros de transporte usados para el target del linac. El uso de va lores inadecuados para esos parámetros puede conducir a una incorrecta determinación de la configuración del haz inicial o producir sesgos en las distribuciones de dosis. - Se utilizó PRIMO para simular archivos de espacios de fase distribuidos por Varian para el linac TrueBeam. Los resultados se compararon con datos experimentales aportados por cinco centros de radioterapia europeos. Se concluyó que la varianza latente y la exactitud de estos espacios de fase son adecuadas para la práctica clínica de rutina. Sin embargo estos espacios de fase no son suficientemente grandes para emplearse en investigaciones que requieren alcanzar una baja incertidumbre estadística. Hasta donde conocemos, PRIMO es el único sistema Monte Carlo que simula completamente el acelerador lineal y calcula la dosis absorbida, dirigido a la investigación y la verificación de dosis que no requiere del usuario tareas de codificación y está disponible públicamentePostprint (published version

Monte Carlo’s Core and Tests for Application Developers: Geant4 and XRMC Comparison and Validation

In this chapter, the Monte Carlo (MC) core is presented, particularly its cross-sectional libraries and random generators. The main idea is to introduce validation and reliability of MC applications and to explore its limitations. As an example, a comparison between two MC toolkits, namely XRMC (version 6.5.0–2) and Geant4 (version 10.02.p02), and a validation between each of them and experimental data applied to mammography (external dosimetry) are presented. The simulated quantities compared are exposure, kerma, half-value layer, and backscattering. Limitations, advantages, and disadvantages of using a general and specific MC toolkit are commented too

Monte Carlo and experimental small-field dosimetry applied to spatially fractionated synchrotron radiotherapy techniques

Two innovative radiotherapy (RT) approaches are under development at the ID17 Biomedical Beamline of the European Synchrotron Radiation Facility (ESRF): microbeam radiation therapy (MRT) and minibeam radiation therapy (MBRT). The two main distinct characteristics with respect to conventional RT are the use of submillimetric field sizes and spatial fractionation of the dose. This PhD work deals with different features related to small-field dosimetry involved in these techniques. Monte Carlo (MC) calculations and several experimental methods are used with this aim in mind. The core of this PhD Thesis consisted of the development and benchmarking of an MC-based computation engine for a treatment planning system devoted to MRT within the framework of the preparation of forthcoming MRT clinical trials. Additional achievements were the definition of safe MRT irradiation protocols, the assessment of scatter factors in MRT, the further improvement of the MRT therapeutic index by injecting a contrast agent into the tumour and the definition of a dosimetry protocol for preclinical trials in MBRT

Fast Monte Carlo Simulations for Quality Assurance in Radiation Therapy

Monte Carlo (MC) simulation is generally considered to be the most accurate method for dose calculation in radiation therapy. However, it suffers from the low simulation efficiency (hours to days) and complex configuration, which impede its applications in clinical studies. The recent rise of MRI-guided radiation platform (e.g. ViewRay’s MRIdian system) brings urgent need of fast MC algorithms because the introduced strong magnetic field may cause big errors to other algorithms. My dissertation focuses on resolving the conflict between accuracy and efficiency of MC simulations through 4 different approaches: (1) GPU parallel computation, (2) Transport mechanism simplification, (3) Variance reduction, (4) DVH constraint. Accordingly, we took several steps to thoroughly study the performance and accuracy influence of these methods. As a result, three Monte Carlo simulation packages named gPENELOPE, gDPMvr and gDVH were developed for subtle balance between performance and accuracy in different application scenarios. For example, the most accurate gPENELOPE is usually used as golden standard for radiation meter model, while the fastest gDVH is usually used for quick in-patient dose calculation, which significantly reduces the calculation time from 5 hours to 1.2 minutes (250 times faster) with only 1% error introduced. In addition, a cross-platform GUI integrating simulation kernels and 3D visualization was developed to make the toolkit more user-friendly. After the fast MC infrastructure was established, we successfully applied it to four radiotherapy scenarios: (1) Validate the vender provided Co60 radiation head model by comparing the dose calculated by gPENELOPE to experiment data; (2) Quantitatively study the effect of magnetic field to dose distribution and proposed a strategy to improve treatment planning efficiency; (3) Evaluate the accuracy of the build-in MC algorithm of MRIdian’s treatment planning system. (4) Perform quick quality assurance (QA) for the “online adaptive radiation therapy” that doesn’t permit enough time to perform experiment QA. Many other time-sensitive applications (e.g. motional dose accumulation) will also benefit a lot from our fast MC infrastructure