Prompt-gamma imaging (PGI) was proposed in the 2000s as a promising in vivo range-verification method to maintain the physical advantage of proton beams by reducing unwanted range-uncertainties. Recently, PGI with a slit camera has been successfully implemented in clinical application. Despite its high accuracy and sensitivity to range deviation being shown in several studies, the clinical benefits of PGI have not yet been investigated. Hence, to fully exploit the advantages of PGI, this thesis aims to investigate the feasibility of PGI-based range verification for the automatic classification of treatment deviations and differentiation of relevant from non-relevant changes in the treatment of head-and-neck (H&N) tumors. In the first part of this thesis, the four most common types of treatment deviations in proton therapy (PT) were investigated regarding their PGI signature and by considering clinically relevant and non-relevant scenarios. A heuristic decision tree (DT) model was iteratively developed. To gain understanding of the specific signature of the error sources, different levels of geometrical complexities were explored, from simple to complex. At the simplest level, a phantom with homogeneous density was used to distinguish range-prediction and setup errors. Next, in the intermediate complexity level, a phantom with heterogeneous density was used to inspect the additional error scenarios of anatomical changes. Finally, real patient CT scans were used to investigate the relevance of changes based on clinical constraints. In the final model, a five-step filtering approach was used during pre-processing to select reliable pencil-beam-scanning spots for range verification. In this study, five features extracted from the filtered PGI data were used to classify the treatment deviation. The model is able distinguish four introduced scenarios into six classes as follows: (1) overestimation of range prediction, (2) underestimation of range prediction, (3) setup error with larger air gap, (4) setup error with smaller air gap, (5) anatomical change, and (6) non-relevant change. To ensure the application was effective, independent patient CT datasets were used to test the model. The results yielded an excellent performance of the DT classifier, with high accuracy, sensitivity, and specificity of 96%, 100%, and 85.7%, respectively. According to these findings, this model can sensitively detect treatment deviations in PT based on simulated PGI data. In the second part of this work, an alternative approach based on machine learning (ML) was taken to automatically classify the error sources. In the first stage, the two approaches were compared, using the same features as well as the same training and test datasets. The results show that the ML approach was slightly better than the heuristic DT approach in terms of accuracy. However, the performance of both approaches was excellent for the individual scenarios. Thus, these results confirm that the PGI-based data classification with five features can be applied to detect individual sources of treatment deviation in PT. In the second stage, there was an investigation of more complex and more realistic combinations of error scenarios, which was out of the scope of the DT approach. The results demonstrated that the performance of the ML-based classifiers declined in general. Furthermore, the additional features of the PG shift did not substantially improve the performance of the classifiers. As a consequence, these findings mark important issues for future research. Potentially, usage of the spatial information from the spot-based PGI data and more complex techniques such as deep learning may improve the performance of classifiers with respect to scenarios with multiple error sources. However, regardless of this, it is recommended that these findings be confirmed and validated in simulations under measurement-like conditions or with real PG measurements of H&N patients themselves. Moreover, this classification model could eventually be tested with other body sites and entities in order to assess its compatibility and adaptation requirements. In summary, this study yielded promising results regarding the automatic classification of treatment-deviation sources and the differentiation of relevant and non-relevant changes in H&N-tumor treatment in PT with PGI data. This simulation study marks an important step towards fully automated PGI-based proton-range verification, which could contribute to closing the treatment-workflow loop of adaptive therapy by supporting clinical decision-making and, ultimately, improving clinical PT.:1 Introduction 2 Background 2.1 Proton therapy 2.1.1 Rationale for proton therapy 2.1.2 Uncertainties and their mitigation 2.2 In vivo range-verification techniques 2.2.1 Range probing 2.2.2 Proton tomography 2.2.3 Magnetic resonance imaging 2.2.4 Ionoacoustic detection 2.2.5 Treatment-activated positron-emission tomography imaging 2.2.6 Prompt-gamma based detection 3 Prompt-gamma imaging with a knife-edged slit camera 3.1 Current state-of-the-art 3.2 Prompt-gamma camera system 3.3 Data acquisition and analysis 4 Error-source classification using heuristic decision tree approach 4.1 Study design 4.1.1 Case selection 4.1.2 Investigated scenarios 4.1.3 Prompt-gamma simulation and range shift determination 4.2 Development of the model 4.2.1 First-generation model 4.2.2 Second-generation model 4.2.3 Third-generation model 4.3 Model testing 4.4 Discussion: decision-tree model 5 Error-source classification using a machine-learning approach 5.1 Machine learning for classification 5.1.1 Support-vector-machine algorithm 5.1.2 Ensemble algorithm – random forest 5.1.3 Logistic-regression algorithm 5.2 Study design 5.2.1 Case selection 5.2.2 Feature selection 5.3 Model generation 5.4 Model testing 5.5 Discussion 6 Summary/ Zusammenfassung Bibliography Appendix List of Figures List of Tables List of Abbreviations

L'hadronthérapie est une modalité de radiothérapie innovante dans laquelle des ions légers -tels des protons ou des ions carbone- sont accélérés à une vitesse relativiste, puis focalisés afin d'irradier la zone tumorale du patient. Cette technique se démarque de la radiothérapie dite conventionnelle utilisant des photons- par l'existence d'un pic de dépôt d'énergie, appelé pic de Bragg, qui se situe à la fin du parcours des ions. L'existence de différents phénomènes qui aboutissent à une incertitude sur le parcours des ions représente toutefois une limite à la précision intrinsèque de cette modalité. Cela justifie la nécessité d'une assurance qualité des traitements et motive le développement de techniques de suivi en ligne et en temps réel du parcours des ions. Ces travaux de thèse ont pour objet la caractérisation d'une technique de suivi du parcours des ions, appelée imagerie des vertex d'interaction. Il a en effet été observé que lors du parcours des ions dans le patient, une fraction importante de ceux-ci subit des réactions nucléaires, à l'origine d'un rayonnement de particules promptes secondaires chargées. Un télescope constitué de capteurs pixélisés est proposé pour localiser les vertex d'interaction de ces particules et mesurer leur corrélation avec le parcours des ions, corrélation prédite par des travaux in-silico précédents. La réalisation de plusieurs expériences durant lesquelles des cibles homogènes et hétérogènes ont été irradiées dans des conditions réalistes a permis d'obtenir les premiers résultats expérimentaux relatifs à cette technique, confrontés à des simulations qui ont également été réalisées. Avant de discuter l'ensemble des résultats obtenus, ce manuscrit détaille les aspects matériels et logiciels des importants développements mis en oeuvre et qui ont abouti à un prototype complet et fonctionnel d'imageur, accompagné de simulations Monte Carlo basées sur le logiciel Geant4 / Hadrontherapy is an innovative radiotherapy modality in which light ions -such as protons or carbon ionsare accelerated to a relativistic speed and focused to irradiate a tumoral area. This technique differs from the conventional radiotherapy -which uses photons- by the existence of an energy deposition peak, called Bragg peak, which stands at the end of the ions path. However, different phenomena that lead to uncertainty in the real ion range exist, and limit the intrinsic accuracy of this modality. This justifies the need for a treatments quality assurance and motivates the development of in-line and real-time monitoring techniques to follow the real ions range. This PhD thesis work aims the characterization of an ion range monitoring technic, called interaction vertex imaging. It has been observed that during the ion path in the patient, a significant part of incoming ions undergoes nuclear reactions, causing a prompt secondary charged particles radiation. A telescope made up of pixelated sensors is proposed to locate these particles interaction vertex and to measure their correlation with the ions range, correlation predicted by a previous in-silico work. The first experimental results for this technique has been obtained with the realization of several experiments during which homogeneous and heterogeneous targets were irradiated under realistic conditions. Simulations were also performed to compare with experimental results. Before discussing the overall results, this manuscript details the hardware and software aspects of important developments that was made and that resulted in a complete and working prototype imager, with Monte Carlo simulations based on the Geant4 software