Realizzazione e convalida del modello Monte Carlo di un calibratore di attività per radiofarmaci

I calibratori di attività sono strumenti molto importanti per la pratica, diagnostica e terapeutica, in medicina nucleare, perché permettono di associare ad un radiofarmaco una misura accurata dell’attività dell’isotopo in esso contenuto; questo è fondamentale in quanto l’attività della sorgente esprime la quantità di farmaco somministrata al paziente. In questo lavoro è stato sviluppato il modello Monte Carlo di un calibratore di attività ampiamente diffuso nei laboratori di radiofarmacia (Capintec CRC-15), utilizzando il codice Monte Carlo FLUKA. Per realizzare il modello si è posta estrema attenzione nel riprodurre al meglio tutti i dettagli delle componenti geometriche della camera e dei campioni delle sorgenti radioattive utilizzati. A tale scopo, la camera di ionizzazione di un calibratore è stata studiata mediante imaging TAC. Un’analisi preliminare è stata eseguita valutando il confronto tra l’andamento sperimentale dell’efficienza della camera in funzione dell’energia dei fotoni incidenti e quello ottenuto in simulazione. In seguito si è proceduto con la validazione: si sono studiati a questo proposito la risposta del calibratore in funzione dell’altezza della sorgente e i confronti tra i fattori relativi (rispetto ad una sorgente certificata di 137Cs) e le misure di confronto sono state eseguite con diverse sorgenti certificate di 133Ba, 68Ge-68Ga, 177Lu ed uno standard tarato internamente di 99mTc. In tale modo, si è ricoperto l'intero campo di interesse dei principali radionuclidi impiegati nelle applicazioni diagnostiche e terapeutiche di Medicina Nucleare. Il modello sviluppato rappresenta un importante risultato per l’eventuale determinazione di nuovi fattori di calibrazione o per un futuro studio relativo all’ottimizzazione della risposta del calibratore.

Ricostruzione di segnali multipli del rivelatore a tempo di volo (ToF) di ALICE: confronto fra dati e Monte Carlo

La fisica delle collisioni ad alte energie è, ad oggi, uno dei campi di ricerca più interessante per la verifica di modelli teorici che spieghino la nascita e la formazione dell'universo in cui viviamo. In quest'ottica lavorano esperimenti presso i più importanti acceleratori di particelle: tra questi anche l'esperimento ALICE, presso il Large Hadron Collider LHC del CERN di Ginevra. Il suo scopo principale è quello di verificare e ampliare l'insieme delle prove sperimentali alla base sull'esistenza di un nuovo stato della materia: il Quark Gluon Plasma. La presenza della transizione di fase della materia adronica ordinaria a QGP era stata teorizzata da diversi studi termodinamici e da calcoli di QCD su reticolo: in particolare si prevedeva l'esistenza di uno stato della materia in cui i quark sono deconfinati. Il QGP è dunque un plasma colorato e densissimo di quark e gluoni, liberi di interagire tra loro. Queste condizioni sarebbero state quelle dell'universo primordiale, circa 1µs dopo il Big Bang: a seguito di una transizione di fase, si sarebbe poi formata tutta la materia adronica ordinaria. Per riprodurre le condizioni necessarie alla formazione del QGP occorrono collisioni ad energie ultrarelativistiche come quelle prodotte, negli ultimi anni, dall'acceleratore LHC. Uno dei principali rivelatori dedicati all'identificazione di particelle in ALICE è il sistema a tempo di volo TOF. Nonostante un attento processo di ottimizzazione della risoluzione delle sue componenti, persistono residui errori strumentali che limitano la qualità (già ottima) del segnale, tutt'oggi oggetto di studio. L'elaborato presentato in questa tesi è suddiviso in tre capitoli: nel primo ripercorriamo gli aspetti teorici del Modello Standard e del Quark Gluon Plasma. Nel secondo descriviamo la struttura di rivelazione di ALICE, analizzando il funzionamento delle singole componenti. Nel terzo, infine, verifichiamo le principali correzioni al TOF ad oggi note, confrontando i dati a nostra disposizione con delle simulazioni Monte Carlo: questo ci permette da un lato di approfondirne la conoscenza, dall'altro di cercarne di migliorare la descrizione del comportamento del rivelatore.

Monte Carlo dose calculation improvements for low energy electron beams using eMC

The electron Monte Carlo (eMC) dose calculation algorithm in Eclipse (Varian Medical Systems) is based on the macro MC method and is able to predict dose distributions for high energy electron beams with high accuracy. However, there are limitations for low energy electron beams. This work aims to improve the accuracy of the dose calculation using eMC for 4 and 6 MeV electron beams of Varian linear accelerators. Improvements implemented into the eMC include (1) improved determination of the initial electron energy spectrum by increased resolution of mono-energetic depth dose curves used during beam configuration; (2) inclusion of all the scrapers of the applicator in the beam model; (3) reduction of the maximum size of the sphere to be selected within the macro MC transport when the energy of the incident electron is below certain thresholds. The impact of these changes in eMC is investigated by comparing calculated dose distributions for 4 and 6 MeV electron beams at source to surface distance (SSD) of 100 and 110 cm with applicators ranging from 6 x 6 to 25 x 25 cm(2) of a Varian Clinac 2300C/D with the corresponding measurements. Dose differences between calculated and measured absolute depth dose curves are reduced from 6% to less than 1.5% for both energies and all applicators considered at SSD of 100 cm. Using the original eMC implementation, absolute dose profiles at depths of 1 cm, d(max) and R50 in water lead to dose differences of up to 8% for applicators larger than 15 x 15 cm(2) at SSD 100 cm. Those differences are now reduced to less than 2% for all dose profiles investigated when the improved version of eMC is used. At SSD of 110 cm the dose difference for the original eMC version is even more pronounced and can be larger than 10%. Those differences are reduced to within 2% or 2 mm with the improved version of eMC. In this work several enhancements were made in the eMC algorithm leading to significant improvements in the accuracy of the dose calculation for 4 and 6 MeV electron beams of Varian linear accelerators.

Comparison of monte carlo collimator transport methods for photon treatment planning in radiotherapy

The aim of this work was a Monte Carlo (MC) based investigation of the impact of different radiation transport methods in collimators of a linear accelerator on photon beam characteristics, dose distributions, and efficiency. Thereby it is investigated if it is possible to use different simplifications in the radiation transport for some clinical situations in order to save calculation time.

Monte Carlo Dose Calculation on Deforming Anatomy

This article presents the implementation and validation of a dose calculation approach for deforming anatomical objects. Deformation is represented by deformation vector fields leading to deformed voxel grids representing the different deformation scenarios. Particle transport in the resulting deformed voxels is handled through the approximation of voxel surfaces by triangles in the geometry implementation of the Swiss Monte Carlo Plan framework. The focus lies on the validation methodology which uses computational phantoms representing the same physical object through regular and irregular voxel grids. These phantoms are chosen such that the new implementation for a deformed voxel grid can be compared directly with an established dose calculation algorithm for regular grids. Furthermore, separate validation of the aspects voxel geometry and the density changes resulting from deformation is achieved through suitable design of the validation phantom. We show that equivalent results are obtained with the proposed method and that no statistically significant errors are introduced through the implementation for irregular voxel geometries. This enables the use of the presented and validated implementation for further investigations of dose calculation on deforming anatomy.

Monte Carlo implementation, validation, and characterization of a 120 leaf MLC

Recently, the new high definition multileaf collimator (HD120 MLC) was commercialized by Varian Medical Systems providing high resolution in the center section of the treatment field. The aim of this work is to investigate the characteristics of the HD120 MLC using Monte Carlo (MC) methods.

Generalized eMC implementation for Monte Carlo dose calculation of electron beams from different machine types

The electron Monte Carlo (eMC) dose calculation algorithm available in the Eclipse treatment planning system (Varian Medical Systems) is based on the macro MC method and uses a beam model applicable to Varian linear accelerators. This leads to limitations in accuracy if eMC is applied to non-Varian machines. In this work eMC is generalized to also allow accurate dose calculations for electron beams from Elekta and Siemens accelerators. First, changes made in the previous study to use eMC for low electron beam energies of Varian accelerators are applied. Then, a generalized beam model is developed using a main electron source and a main photon source representing electrons and photons from the scattering foil, respectively, an edge source of electrons, a transmission source of photons and a line source of electrons and photons representing the particles from the scrapers or inserts and head scatter radiation. Regarding the macro MC dose calculation algorithm, the transport code of the secondary particles is improved. The macro MC dose calculations are validated with corresponding dose calculations using EGSnrc in homogeneous and inhomogeneous phantoms. The validation of the generalized eMC is carried out by comparing calculated and measured dose distributions in water for Varian, Elekta and Siemens machines for a variety of beam energies, applicator sizes and SSDs. The comparisons are performed in units of cGy per MU. Overall, a general agreement between calculated and measured dose distributions for all machine types and all combinations of parameters investigated is found to be within 2% or 2 mm. The results of the dose comparisons suggest that the generalized eMC is now suitable to calculate dose distributions for Varian, Elekta and Siemens linear accelerators with sufficient accuracy in the range of the investigated combinations of beam energies, applicator sizes and SSDs.

Checking Assumptions in Latent Class Regression Models via a Markov Chain Monte Carlo Estimation Approach: An Application to Depression and Socio-Economic Status

Latent class regression models are useful tools for assessing associations between covariates and latent variables. However, evaluation of key model assumptions cannot be performed using methods from standard regression models due to the unobserved nature of latent outcome variables. This paper presents graphical diagnostic tools to evaluate whether or not latent class regression models adhere to standard assumptions of the model: conditional independence and non-differential measurement. An integral part of these methods is the use of a Markov Chain Monte Carlo estimation procedure. Unlike standard maximum likelihood implementations for latent class regression model estimation, the MCMC approach allows us to calculate posterior distributions and point estimates of any functions of parameters. It is this convenience that allows us to provide the diagnostic methods that we introduce. As a motivating example we present an analysis focusing on the association between depression and socioeconomic status, using data from the Epidemiologic Catchment Area study. We consider a latent class regression analysis investigating the association between depression and socioeconomic status measures, where the latent variable depression is regressed on education and income indicators, in addition to age, gender, and marital status variables. While the fitted latent class regression model yields interesting results, the model parameters are found to be invalid due to the violation of model assumptions. The violation of these assumptions is clearly identified by the presented diagnostic plots. These methods can be applied to standard latent class and latent class regression models, and the general principle can be extended to evaluate model assumptions in other types of models.

Fixed-Width Output Analysis for Markov Chain Monte Carlo

Markov chain Monte Carlo is a method of producing a correlated sample in order to estimate features of a complicated target distribution via simple ergodic averages. A fundamental question in MCMC applications is when should the sampling stop? That is, when are the ergodic averages good estimates of the desired quantities? We consider a method that stops the MCMC sampling the first time the width of a confidence interval based on the ergodic averages is less than a user-specified value. Hence calculating Monte Carlo standard errors is a critical step in assessing the output of the simulation. In particular, we consider the regenerative simulation and batch means methods of estimating the variance of the asymptotic normal distribution. We describe sufficient conditions for the strong consistency and asymptotic normality of both methods and investigate their finite sample properties in a variety of examples.

COVARIATE-ADJUSTED NONPARAMETRIC ANALYSIS OF MAGNETIC RESONANCE IMAGES USING MARKOV CHAIN MONTE CARLO

Permutation tests are useful for drawing inferences from imaging data because of their flexibility and ability to capture features of the brain that are difficult to capture parametrically. However, most implementations of permutation tests ignore important confounding covariates. To employ covariate control in a nonparametric setting we have developed a Markov chain Monte Carlo (MCMC) algorithm for conditional permutation testing using propensity scores. We present the first use of this methodology for imaging data. Our MCMC algorithm is an extension of algorithms developed to approximate exact conditional probabilities in contingency tables, logit, and log-linear models. An application of our non-parametric method to remove potential bias due to the observed covariates is presented.

An efficient framework for photon Monte Carlo treatment planning

Currently photon Monte Carlo treatment planning (MCTP) for a patient stored in the patient database of a treatment planning system (TPS) can usually only be performed using a cumbersome multi-step procedure where many user interactions are needed. This means automation is needed for usage in clinical routine. In addition, because of the long computing time in MCTP, optimization of the MC calculations is essential. For these purposes a new graphical user interface (GUI)-based photon MC environment has been developed resulting in a very flexible framework. By this means appropriate MC transport methods are assigned to different geometric regions by still benefiting from the features included in the TPS. In order to provide a flexible MC environment, the MC particle transport has been divided into different parts: the source, beam modifiers and the patient. The source part includes the phase-space source, source models and full MC transport through the treatment head. The beam modifier part consists of one module for each beam modifier. To simulate the radiation transport through each individual beam modifier, one out of three full MC transport codes can be selected independently. Additionally, for each beam modifier a simple or an exact geometry can be chosen. Thereby, different complexity levels of radiation transport are applied during the simulation. For the patient dose calculation, two different MC codes are available. A special plug-in in Eclipse providing all necessary information by means of Dicom streams was used to start the developed MC GUI. The implementation of this framework separates the MC transport from the geometry and the modules pass the particles in memory; hence, no files are used as the interface. The implementation is realized for 6 and 15 MV beams of a Varian Clinac 2300 C/D. Several applications demonstrate the usefulness of the framework. Apart from applications dealing with the beam modifiers, two patient cases are shown. Thereby, comparisons are performed between MC calculated dose distributions and those calculated by a pencil beam or the AAA algorithm. Interfacing this flexible and efficient MC environment with Eclipse allows a widespread use for all kinds of investigations from timing and benchmarking studies to clinical patient studies. Additionally, it is possible to add modules keeping the system highly flexible and efficient.

Conversion of CT numbers into tissue parameters for Monte Carlo dose calculations: a multi-centre study

The conversion of computed tomography (CT) numbers into material composition and mass density data influences the accuracy of patient dose calculations in Monte Carlo treatment planning (MCTP). The aim of our work was to develop a CT conversion scheme by performing a stoichiometric CT calibration. Fourteen dosimetrically equivalent tissue subsets (bins), of which ten bone bins, were created. After validating the proposed CT conversion scheme on phantoms, it was compared to a conventional five bin scheme with only one bone bin. This resulted in dose distributions D(14) and D(5) for nine clinical patient cases in a European multi-centre study. The observed local relative differences in dose to medium were mostly smaller than 5%. The dose-volume histograms of both targets and organs at risk were comparable, although within bony structures D(14) was found to be slightly but systematically higher than D(5). Converting dose to medium to dose to water (D(14) to D(14wat) and D(5) to D(5wat)) resulted in larger local differences as D(5wat) became up to 10% higher than D(14wat). In conclusion, multiple bone bins need to be introduced when Monte Carlo (MC) calculations of patient dose distributions are converted to dose to water.

Monte Carlo calculation of Varian 2300C/D Linac photon beam characteristics: a comparison between MCNP4C, GEANT3 and measurements

Different codes are used for Monte Carlo (MC) calculations in radiation therapy. In this research, MCNP4C and GEANT3 codes have been compared in calculations of dosimetric characteristics of Varian Clinac 2300C/D. The parameters of influence in the differences seen in dosimetric features were discussed. This study emphasizes that both MCNP4C and GEANT3 MC can be used in radiation therapy computations and their differences in photon spectra calculations have a negligible effect on percentage depth dose computations in radiation therapy.