Treatment planning for radiation therapy inherently involves tradeoffs, such as between tumor control and normal tissue sparing, between time-efficiency and dose quality, and between nominal plan quality and robustness. The purpose of this thesis is to develop methods that can facilitate decision making related to such tradeoffs. The main focus of the thesis is on multicriteria optimization methods where a representative set of treatment plans are first calculated and the most appropriate plan contained in this representation then selected by the treatment planner through continuous interpolation between the precalculated alternatives. These alternatives constitute a subset of the set of Pareto optimal plans, meaning plans such that no criterion can be improved without a sacrifice in another. Approximation of Pareto optimal sets is first studied with respect to fluence map optimization for intensity-modulated radiation therapy. The approximation error of a discrete representation is minimized by calculation of points one at the time at the location where the distance between an inner and outer approximation of the Pareto set currently attains its maximum. A technique for calculating this distance that is orders of magnitude more efficient than the best previous method is presented. A generalization to distributed computational environments is also proposed. Approximation of Pareto optimal sets is also considered with respect to direct machine parameter optimization. Optimization of this form is used to calculate representations where any interpolated treatment plan is directly deliverable. The fact that finite representations of Pareto optimal sets have approximation errors with respect to Pareto optimality is addressed by a technique that removes these errors by a projection onto the exact Pareto set. Projections are also studied subject to constraints that prevent the dose-volume histogram from deteriorating. Multicriteria optimization is extended to treatment planning for volumetric-modulated arc therapy and intensity-modulated proton therapy. Proton therapy plans that are robust against geometric errors are calculated by optimization of the worst case outcome. The theory for multicriteria optimization is extended to accommodate this formulation. Worst case optimization is shown to be preferable to a previous more conservative method that also protects against uncertainties which cannot be realized in practice. / En viktig aspekt av planering av strålterapibehandlingar är avvägningar mellan behandlingsmål vilka står i konflikt med varandra. Exempel på sådana avvägningar är mellan tumörkontroll och dos till omkringliggande frisk vävnad, mellan behandlingstid och doskvalitet, och mellan nominell plankvalitet och robusthet med avseende på geometriska fel. Denna avhandling syftar till att utveckla metoder som kan underlätta beslutsfattande kring motstridiga behandlingsmål. Primärt studeras en metod för flermålsoptimering där behandlingsplanen väljs genom kontinuerlig interpolation över ett representativt urval av förberäknade alternativ. De förberäknade behandlingsplanerna utgör en delmängd av de Paretooptimala planerna, det vill säga de planer sådana att en förbättring enligt ett kriterium inte kan ske annat än genom en försämring enligt ett annat. Beräkning av en approximativ representation av mängden av Paretooptimala planer studeras först med avseende på fluensoptimering för intensitetsmodulerad strålterapi. Felet för den approximativa representationen minimeras genom att innesluta mängden av Paretooptimala planer mellan inre och yttre approximationer. Dessa approximationer förfinas iterativt genom att varje ny plan genereras där avståndet mellan approximationerna för tillfället är som störst. En teknik för att beräkna det maximala avståndet mellan approximationerna föreslås vilken är flera storleksordningar snabbare än den bästa tidigare kända metoden. En generalisering till distribuerade beräkningsmiljöer föreslås även. Approximation av mängden av Paretooptimala planer studeras även för direkt maskinparameteroptimering, som används för att beräkna representationer där varje interpolerad behandlingsplan är direkt levererbar. Det faktum att en ändlig representation av mängden av Paretooptimala lösningar har ett approximationsfel till Paretooptimalitet hanteras via en metod där en interpolerad behandlingsplan projiceras på Paretomängden. Projektioner studeras även under bivillkor som förhindrar att den interpolerade planens dos-volym histogram kan försämras. Flermålsoptimering utökas till planering av rotationsterapi och intensitetsmodulerad protonterapi. Protonplaner som är robusta mot geometriska fel beräknas genom optimering med avseende på det värsta möjliga utfallet av de föreliggande osäkerheterna. Flermålsoptimering utökas även teoretiskt till att innefatta denna formulering. Nyttan av värsta fallet-optimering jämfört med tidigare mer konservativa metoder som även skyddar mot osäkerheter som inte kan realiseras i praktiken demonstreras experimentellt. / <p>QC 20130527</p>

Chemo-radiation therapy is a principal modality for the treatment of head and neck cancers, and its efficacy depends on the interaction of tumor oxygen with free radicals. In this study, we adopted a novel hybrid diffuse optical instrument combining a commercial frequency-domain tissue oximeter (Imagent) and a custom-made diffuse correlation spectroscopy (DCS) flowmeter, which allowed for simultaneous measurements of tumor blood flow and blood oxygenation. Using this hybrid instrument we continually measured tumor hemodynamic responses to chemo-radiation therapy over the treatment period of 7 weeks. We also explored monitoring dynamic tumor hemodynamic changes during radiation delivery. Blood flow data analysis was improved by simultaneously extracting multiple parameters from one single autocorrelation function curve measured by DCS. Patients were classified into two groups based on clinical outcomes: a complete response (CR) group and an incomplete response (IR) group with remote metastasis and/or local recurrence within one year. Interestingly, we found human papilloma virus (HPV-16) status largely affected tumor homodynamic responses to therapy. Significant differences in tumor blood flow index (BFI) and reduced scattering coefficient (μs’) between the IR and CR groups were observed in HPV-16 negative patients at Week 3. Significant differences in oxygenated hemoglobin concentration ([HbO2]) and blood oxygen saturation (StO2) between the two groups were found in HPV-16 positive patients at Week 1 and Week 3, respectively. Receiver operating characteristic curves were constructed and results indicated high sensitivities and specificities of these hemodynamic parameters for early (within the first three weeks of the treatment) prediction of one-year treatment outcomes. Measurement of tumor hemodynamics may serve as a predictive tool allowing treatment selection based on biologic tumor characteristics. Ultimately, reduction of side effects in patients not benefiting from radiation treatment may be feasible.

We study inverse treatment planning approaches for stereotactic radiosurgery using Leksell Gamma Knife Perfexion (PFX, Elekta, Stockholm, Sweden) to treat brain cancer and tumour patients. PFX is a dedicated head-and-neck radiation delivery device that is commonly used in clinics. In a PFX treatment, the patient lies on a couch and the radiation beams are emitted from eight banks of radioactive sources around the patient's head that are focused at a single spot, called an isocentre. The radiation delivery in PFX follows a step-and-shoot manner, i.e., the couch is stationary while the radiation is delivered at an isocentre location, and only moves when no beam is being emitted. To find a set of well-positioned isocentres in tumour volumes, we explore fast geometry-based algorithms, including skeletonization and hybrid grassfire and sphere-packing approaches. For the selected set of isocentres, the optimal beam durations to deliver a high prescription dose to the tumour are later found using a penalty-based optimization model. We next extend our grassfire and sphere-packing isocentre selection method to treatments with homogenous dose distributions. Dose homogeneity is required in multi-session plans where a larger volume is treated to account for daily setup errors, and thus large overlaps with surrounding healthy tissue may exist. For multi-session plans, we explicitly consider the healthy tissue overlaps in our algorithms and strategically select many isocentres in adjacent volumes to avoid hotspots. There is also interest in treating patients with continuous couch motion to decrease the total treatment session and increase plan quality. We therefore investigate continuous dose delivery treatment plans for PFX. We present various path selection methods along which the dose is delivered using Hamiltonian paths techniques, and develop mixed-integer and linear approximation models to determine the configuration and duration of the radiation time along the paths. We consider several criteria in our optimization models, including machine speed constraints and movement accuracy, preference for single or multiple paths, and smoothness of movement. Our plans in all proposed approaches are tested on seven clinical cases and can meet or exceed clinical guidelines and usually outperform clinical treatments.

At the BC Cancer Agency (BCCA), Vancouver Island Centre (VIC), the clinical verification of Intensity Modulated Radiation Therapy (IMRT) treatment plans involves comparing Portal Image (PI) -based three-dimensionally reconstructed (EPIDose) dose distributions to planned doses calculated using the Pencil Beam Convolution (PBC) algorithm. Discrepancies surpassing established action levels constitute failure. Since 2007, the failure rate of IMRT verification process had been increasing, reaching as high as 18.5% in 2009. A retrospective evaluation of clinical IMRT verification failures was conducted to identify causes and possible resolutions. Thirty clinical verification failures were identified. An equipment malfunction was discovered and subsequently repaired, and several failures were resolved in the process. Statistical uncertainty in measurement outcome was small in comparison to action levels and not considered significant to the production of failures. Still, over 50% of the redelivered plans were shown to consistently fail. A subgroup of consistent verification plans were compared to ion chamber point dose measurements. Relative to ion chamber measurements, EPIDose underestimated the dose while the dose calculation algorithm (PBC, Eclipse version 8.1.18) overestimated the same point dose. Comparisons of individual fields demonstrated that none were identifiably problematic; dose discrepancies were the result of minor but accumulating dose differences. Consistent verification failures were recalculated using two advanced dose calculation engines (the Anisotropic Analytical Algorithm and Monte Carlo). In general, verification metrics improved, and all failures were resolved. Three distinct indices of fluence modulation (or complexity) were shown to correlate with verification metrics. This indicated that deficiencies in both the leaf motion calculator and the PBC (Eclipse version 8.1.18) had likely contributed to the production of failures. In conclusion, clinical verification failures were resolved retrospectively by replacing faulty equipment and using more advanced methods of planned dose calculation, supporting the efficacy and continued use of PI-based three dimensional dose reconstruction for IMRT verification.

Radiotherapy is used in clinics to treat cancer with highly energetic ionizing particles. The radiation dose can be measured indirectly by means of radiation detectors or dosimeters. The dose deposited in a detector can be related to dose deposited in a point within the patient. In theory, however, this is only possible under charged particle equilibrium (CPE). The motivation behind the dissertation was driven by the difficult, yet crucial, dosimetry in non-CPE regions. Inaccurate dose assessment performed with standard dosimetry using ionization chambers may significantly impact the outcomes of radiotherapy treatments. Therefore, advanced dosimetry methods tailored specifically to suit non-CPE conditions must be used. This work aims to improve dosimetry in two types of non-CPE conditions that pose dosimetric challenges: regions near interfaces of tissues with low- and high- density media and in small photon fields. To achieve the main dissertation objectives, an enhanced film dosimetry protocol with a novel film calibration approach was implemented. This calibration method is based on the percent depth dose (PDD) tables and was shown to be efficient and accurate. As a result, the PDD calibration method was used for the film dosimetry process throughout the dissertation work. Monte Carlo (MC) calculations for the small field dosimetry were performed using phase-space files (PSFs) provided by Varian for TrueBeam linac. The MC statistical uncertainty in these types of calculations is limited by the number of particles (due to latent variance) in the used PSFs. This study investigated the behaviour of the latent variances (LV) with beam energy, depth in phantom, and calculation resolution (voxel size). LV was evaluated for standard 10x10 cm2 fields as well as small fields (down to 1.3 mm diameter). The results showed that in order to achieve sub-percent LV in open 10x10 cm2 field MC simulations a single PSF can be used, whereas for small SRS fields (1.3—10 mm) more PSFs (66—8 PSFs) would have to be summed. The first study in this dissertation compared the performance of several dosimetric methods in three multi-layer heterogeneous phantoms with water/air, water/lung, and water/steel interfaces irradiated with 6 and 18 MV photon beams. MC calculations were used, along with Acuros XB, anisotropic analytical algorithm (AAA), GafChromic EBT2 film, and MOSkin dosimeters. PDDs were calculated and measured in these heterogeneous phantoms. The result of this study showed that Acuros XB, AAA, and MC calculations were within 1% in the regions with CPE. At media interfaces and buildup regions, differences between Acuros XB and MC were in the range of +4.4% to -12.8%. MOSkin and EBT2 measurements agreed to MC calculations within ~ 2.5%-4.5%. AAA did not predict the backscatter dose from the high-density heterogeneity. For the third, multilayer lung phantom, 6 MV beam PDDs calculated by all treatment planning system (TPS) algorithms were within 2% of MC. 18 MV PDDs calculated by Acuros XB and AAA differed from MC by up to 3.2 and 6.8%, respectively. MOSkin and EBT2 each differed from MC by up to 3%. All dosimetric techniques, except AAA, agreed within 3% in the regions with particle equilibrium. Differences between the dosimetric techniques were larger for the 18 MV than the 6 MV beam. This study provided a comparative performance evaluation of several advanced dosimeters in heterogeneous phantoms. This combination of experimental and calculation dosimetry techniques was used for the first time to evaluate the dose near these interfaces. The second study in the dissertation aims to improve dose measurement accuracy in small radiotherapy fields. Field output factors of 6 MV beams from TrueBeam linear accelerator (linac) collimated with 1.27-40 mm diameter cones were calculated and measured using MC and EBT3 films. A set of detector specific correction factors for two widely used dosimeters (EFD-3G diode and PTW-60019 microDiamond detectors) were determined based on GafChromic EBT3 film measurements and calculated using MC methods. MC calculations were performed for microDiamond detector in parallel and perpendicular orientations relative to the beam axis. The result of this study showed that the measured OFs agreed within 2.4% for fields ≥10 mm. For the cones of 1.27, 2.46, and 3.77 mm diameter maximum differences were 17.9%, 1.8% and 9.0%, respectively. MC calculated OF in water agreed with those obtained using EBT3 film within 2.2% for all fields. MC calculated output correction factors for microDiamond detector in fields ≥10 mm ranged within 0.975-1.020 for perpendicular and parallel orientations. MicroDiamond detector correction factors calculated for the 1.27, 2.46 and 3.77 mm fields were 1.974, 1.139 and 0.982 with detector in parallel orientation, and these factors were 1.150, 0.925 and 0.914 in perpendicular orientation. EBT3 and MC obtained correction factors agreed within 3.7% for fields of ≥3.77 mm and within 5.9% for smaller cones. This work provided output correction factors for microDiamond and EFD-3G detectors in very small fields of 1.27 – 3.77 mm diameter and demonstrated over and under-response of these detectors in such fields. These correction factors allow improve the accuracy of dose measurements in small photon fields using these detectors. / Graduate / 2019-08-30

Das entwickelte Bildgeführte Präzisionsbestrahlungsgerät für Kleintiere (engl. Small Animal Image-Guided Radiation Therapy – SAIGRT) dient der schnellen, hochauflösenden Röntgenbildgebung und präzisen, konformalen Bestrahlung von Kleintieren im Rahmen präklinischer in-vivo Experimente für die translationale Krebsforschung. Speziell programmierte Softwares zur Gerätesteuerung sowie zur Bildkorrektur- und Bildrekonstruktion auf dem zentralen leistungsfähigen Arbeitsplatz-PC stellen alle Gerätefunktionen zur Verfügung und ermöglichen durch automatisierte Abläufe und intuitive grafische Nutzeroberflächen eine einfache, sichere Bedienung. Für die Bestrahlungsplanung wird eine vollwertige, aus der humanen klinischen Strahlentherapie adaptierte 3D-Bestrahlungsplanungssoftware eingesetzt, die etablierte Werkzeuge für den Transfer und die Koregistrierung multimodaler Bilddaten, die Konturierung und Segmentierung von Zielvolumina und Risikoorganen sowie die Erstellung und Validierung von Bestrahlungsplänen enthält. Die resultierende Dosisverteilung wird darin basierend auf dem individuellen CT-Datensatz des Versuchstieres und einem auf das SAIGRT angepassten Maschinenmodell mittels eines Monte-Carlo-Algorithmus exakt und realitätsnah simuliert. Durch geometrische Kalibrierungen und vielfältige Basisdatenmessungen für die Bildgebung und Bestrahlung im Rahmen der Gerätekommissionierung ist eine Zielgenauigkeit von ca. ±0,1 mm mit hoher geometrischer Abbildungstreue und guter Bildqualität bei Bildgebungsdosen vergleichbar denen klinischer Radiografie- und CT-Geräte möglich. Die Dosisverteilung zur Bestrahlung der Versuchstiere spiegelt bei der definierten Strahlungsqualität größenskaliert die humane Strahlentherapie mit hochenergetischer Photonenstrahlung von klinischen Linearbeschleunigern wider. Ein umfassendes Qualitätssicherungsprogramm bestehend aus regelmäßiger Wartung und wiederkehrenden Konstanzprüfungen der Bildgebung und Bestrahlung sichert dauerhaft den technisch einwandfreien Zustand und die ordnungsgemäße Verfügbarkeit aller Gerätefunktionen in gleichbleibender Güte. Das SAIGRT ist somit nachweislich geeignet, bildgeführte Bestrahlungen mit einem Ablauf analog dem einer modernen klinischen Strahlentherapie am Menschen in präklinischen in-vivo Experimenten präzise an Kleintieren zu applizieren. Es leistet dadurch einen essentiellen Beitrag zur translationalen Krebsforschung in Dresden, indem die klinische Situation realistischer modelliert und so potenziell die Übertragbarkeit der Ergebnisse auf Krebspatienten verbessert werden kann. / The Small Animal Image-Guided Radiation Therapy (SAIGRT) platform facilitates fast, high resolution X-ray imaging and precise, conformal irradiation of small animals in preclinical in-vivo experiments for translational cancer research. Dedicated software for device control as well as image correction and reconstruction on a central high performance PC provide all device functions and allow simple and safe operation by automated procedures and intuitive graphical user interfaces. A fully 3D treatment planning software adapted from human clinical radiation therapy is used for treatment planning, containing established tools and methods for the transfer and registration of multimodality imaging data, contouring and segmentation of target volumes and organs at risk as well as creation and evaluation of treatment plans. Based on an individual CT scan of the small animal and a machine model adapted for the SAIGRT, the resulting dose distribution is simulated by a Monte-Carlo algorithm in a precise and realistic manner. Geometrical calibrations as well as manifold basic data measurements for X-ray imaging and irradiation during commissioning resulted in a targeting and imaging accuracy of about ±0.1 mm, a correct representation of imaging geometry and a good image quality with imaging doses comparable with those of clinical radiography and CT systems. Dose distribution of the defined beam quality used for irradiation of small animals reflects a downsized human radiation therapy using high energy photon beams of clinical linear accelerators. A comprehensive quality assurance program comprising regular maintenance and periodic constancy tests of X-ray imaging and irradiation ensures permanent technically perfect condition and proper availability of all implemented functions in a stable high quality. The SAIGRT platform is feasible for image-guided irradiations precisely applied to small animals in preclinical in-vivo experiments using a workflow of modern human radiation oncology. Thus, it significantly contributes to translational cancer research by more realistic modelling the clinical situation and potentially brings the results closer to their clinical implementation.

In the medical field, the applications of ultraviolet (UV) radiation are limited to skin or reachable sites due to its low penetration depth into biological tissue. Contrary to UV radiation, X-rays can penetrate the body with almost no attenuation, but they result in toxic side effects. Inorganic scintillators absorb X-rays and convert them into UV or visible photons and are usually used for medical imaging. We propose the use of high density inorganic nanoscale scintillators with the ability to absorb externally applied ionizing radiation directly at the site of application, e.g., inside a tumor, and to convert this ionizing radiation into UV photons in situ, enabling new biomedical applications inside the body. In this thesis, two specific new biomedical applications are discussed in detail: The first application is the use of UV-B emitting nanoscale scintillators for highly localized drugs released or activation of photoactivable therapeutics using only X-rays. The second novel approach is the use of UV-C emitting nanoscale scintillators as radiation sensitizers. However, size-reduction of inorganic scintillators, and most inorganic phosphors in general, usually result in quenching of the photoluminescence properties, defects on the surface of the particles, and a decrease of radiation hardness. Colloidal solutions of nearly monodisperse LaPO4:Gd nanocrystals (5nm) were shown to strongly emit UV radiation upon excitation with X-rays or vacuum UV radiation (160nm). The UV emission of the particles consisted mainly of a single line at 311nm. This UV-B emission of the particles was used to excite the fluorescence of laser dyes dissolved in the colloids. The emission of the dyes was also observed in the case of high dye concentrations, proving that the concept of using radiation with a high penetration depth (X-rays) to excite fluorescence emission with a low penetration depth (UV-B) wavelength is feasible. Pr-doped LuPO4 emits UV-C radiation between 225 and 280nm, where DNA shows strong absorption bands. Therefore, a systematic study of the luminescence of LuPO4:Pr was performed: Different doping concentrations, particle sizes, and excitation sources were compared. Furthermore, it was found that Pr and Nd co-doped LuPO4 results in increased UV-C emission independent of excitation source due to energy transfer. The highest UV-C emission intensity was observed for LuPO4:Pr,Nd(1%,2.5%) upon X-ray irradiation. Finally, LuPO4:Pr,Nd nanoparticles were synthesized, and the biological efficacy of the combined approach (X-rays and UV-C) was assessed using the colony formation assay. Cell culture experiments confirm increased cell death compared to X-rays alone due to the formation of UV-specific DNA damages, supporting the application of the herein synthesized particles as radiation sensitizers.

Des efforts de recherche considérables ont été déployés afin d'améliorer les résultats de traitement de cancers pulmonaires. L'étude de la déformation de l'anatomie du patient causée par la ventilation pulmonaire est au coeur du processus de planification de traitement radio-oncologique. À l'aide d'images de tomodensitométrie quadridimensionnelles (4DCT), une simulation dosimétrique peut être calculée sur les 10 ensembles d'images du 4DCT. Une méthode doit être employée afin de recombiner la dose de radiation calculée sur les 10 anatomies représentant une phase du cycle respiratoire. L'utilisation de recalage déformable d'images (DIR), une méthode de traitement d'images numériques, génère neuf champs vectoriels de déformation permettant de rapporter neuf ensembles d'images sur un ensemble de référence correspondant habituellement à la phase d'expiration profonde du cycle respiratoire. L'objectif de ce projet est d'établir une méthode de génération de champs de déformation à l'aide de la DIR conjointement à une méthode de validation de leur précision. Pour y parvenir, une méthode de segmentation automatique basée sur la déformation surfacique de surface à été créée. Cet algorithme permet d'obtenir un champ de déformation surfacique qui décrit le mouvement de l'enveloppe pulmonaire. Une interpolation volumétrique est ensuite appliquée dans le volume pulmonaire afin d'approximer la déformation interne des poumons. Finalement, une représentation en graphe de la vascularisation interne du poumon a été développée afin de permettre la validation du champ de déformation. Chez 15 patients, une erreur de recouvrement volumique de 7.6 ± 2.5[%] / 6.8 ± 2.1[%] et une différence relative des volumes de 6.8 ± 2.4 [%] / 5.9 ± 1.9 [%] ont été calculées pour le poumon gauche et droit respectivement. Une distance symétrique moyenne 0.8 ± 0.2 [mm] / 0.8 ± 0.2 [mm], une distance symétrique moyenne quadratique de 1.2 ± 0.2 [mm] / 1.3 ± 0.3 [mm] et une distance symétrique maximale 7.7 ± 2.4 [mm] / 10.2 ± 5.2 [mm] ont aussi été calculées pour le poumon gauche et droit respectivement. Finalement, 320 ± 51 bifurcations ont été détectées dans le poumons droit d'un patient, soit 92 ± 10 et 228 ± 45 bifurcations dans la portion supérieure et inférieure respectivement. Nous avons été en mesure d'obtenir des champs de déformation nécessaires pour la recombinaison de dose lors de la planification de traitement radio-oncologique à l'aide de la méthode de déformation hiérarchique des surfaces. Nous avons été en mesure de détecter les bifurcations de la vascularisation pour la validation de ces champs de déformation. / Purpose: To allow a reliable deformable image registration (DIR) method for dose calculation in radiation therapy and to investigate an automatic vessel bifurcations detection algorithm for DIR assessment to improve lung cancer radiation treatment. Methods: 15 4DCT datasets are acquired and deep exhale respiratory phases are exported to Varian treatment planning system (TPS) Eclipse^{\text{TM}} for contouring. Voxelized contours are smoothed by a Gaussian filter and then transformed into a surface mesh representation. Such mesh is adapted by rigid and elastic deformations based on hierarchical surface deformation to match each subsequent lung volumes. The segmentation efficiency is assessed by comparing the segmented lung contour and the TPS contour considering two volume metrics, defined as Volumetric Overlap Error (VOE) [%] and Relative Volume Difference (RVD) [%] and three surface metrics, defined as Average Symmetric Surface Distance (ASSD) [mm], Root Mean Square Symmetric Surface Distance (RMSSD) [mm] and Maximum Symmetric Surface Distance (MSSD) [mm]. Vesselness filter was applied within the segmented lung volumes to identify blood vessels and airways. Segmented blood vessels and airways were skeletonised using a hierarchical curve-skeleton algorithm based on a generalized potential field approach. A graph representation of the computed skeleton was generated to assign one of three labels to each node: the termination node, the continuation node or the branching node. Results: The volume metrics obtained are a VOE of 7.6 ± 2.5[%] / 6.8 ± 2.1[%] and a RVD of 6.8 ± 2.4 [%] / 5.9 ± 1.9 [%] respectively for left and right lung. The surface metrics computed are an ASSD of 0.8 ± 0.2 [mm] / 0.8 ± 0.2 [mm], a RMSSD of 1.2 ± 0.2 [mm] / 1.3 ± 0.3 [mm] and a MSSD of 7.7 ± 2.4 [mm] / 10.2 ± 5.2 [mm] respectively for left and right lung. 320 ± 51 bifurcations were detected in the right lung of a patient for the 10 breathing phases. 92 ± 10 bifurcations were found in the upper half of the lung and 228 ± 45 bifurcations were found in the lower half of the lung. Discrepancies between ten vessel trees were mainly ascribed to the segmentation methode. Conclusions: This study shows that the morphological segmentation algorithm can provide an automatic method to capture an organ motion from 4DCT scans and translate it into a volume deformation grid needed by DIR method for dose distribution combination. We also established an automatic method for DIR assessment using the morphological information of the patient anatomy. This approach allows a description of the lung’s internal structure movement, which is needed to validate the DIR deformation fields.

L’appareil de radiochirurgie CyberKnife performe des traitements avancés de radiothérapie qui offrent des avantages nets pour certains types de cancer. Or, cet appareil produit uniquement des petits faisceaux circulaires ce qui complexifie les procédures de dosimétrie en milieu clinique. En effet, en conditions de petits champs, les diverses perturbations au niveau du détecteur peuvent être très grandes. Ainsi, l’utilisation de la méthode Monte Carlo est nécessaire lors de l’étalonnage et la caractérisation de faisceaux. Ces processus, lors desquels des valeurs de dose de référence et relative sont mesurées et entrées dans les systèmes de planification de traitement, assurent l’efficacité des traitements ainsi que la sécurité des patients. Cette thèse porte sur la modélisation Monte Carlo du CyberKnife M6 et étudie diverses applications à la dosimétrie de petits champs de radiothérapie. En premier lieu, une nouvelle méthode permettant la correction de la dépendance au débit de dose des diodes au silicium est proposée. Cette dernière est validée puis appliquée à des mesures relatives effectuées au CyberKnife du Centre hospitalier de l’Université de Montréal (CHUM). Les résultats illustrent la correction de l’erreur systématique induite dans les mesures due à la dépendance au débit de dose de la diode considérée. La méthode proposée fournit alors une solution efficace à cette problématique. En second lieu, une méthode pour l’optimisation des paramètres sources requis en entrée lors de la modélisation Monte Carlo de faisceaux de radiothérapie est introduite. Cette dernière est basée sur une approche probabiliste portant sur la comparaison de mesures et de simulations pour divers détecteurs, et permet la détermination de l’énergie du faisceau d’électrons incident sur la cible d’un appareil, ainsi que de la largeur à mi-hauteur de sa distribution radiale. La méthode proposée, qui est appliquée au CyberKnife du CHUM, fournit une nouvelle approche permettant l’optimisation d’un modèle Monte Carlo d'un faisceau ainsi que l’estimation des incertitudes sur ses paramètres sources. En troisième lieu, le modèle de faisceau du CyberKnife développé est utilisé afin d’estimer l’impact des incertitudes des paramètres sources sur diverses fonctions dosimétriques couramment utilisées en milieu clinique, ainsi que sur des distributions de dose obtenues par simulation de plans de traitement. Les résultats illustrent l’augmentation de l’impact des incertitudes du modèle de faisceau avec la réduction de la taille de champ, et fournissent une nouvelle perspective sur la précision de calcul atteignable pour ce type de calcul de dose Monte Carlo en petits champs. En quatrième lieu, les protocoles de dosimétrie TG-51 (version adaptée du manufacturier) et TRS-483 sont respectivement appliqués et comparés pour l’étalonnage du CyberKnife M6 se trouvant au CHUM. Il est observé que le TRS-483 est cohérent avec le TG-51. Des facteurs de correction de la qualité et corrigeant pour les effets de moyenne sur le volume propres au CyberKnife du CHUM sont estimés par simulations Monte Carlo pour une chambre à ionisation Exradin A12. Les résultats illustrent que la valeur générique fournie dans le TRS-483 pourrait être surestimée en comparaison à notre modèle de CyberKnife et que cette surestimation pourrait être due à la composante de moyenne sur le volume. / The CyberKnife radiosurgery system performs advanced radiotherapy treatments that offer clear benefits for certain types of cancer. However, this device produces small circular fields only, which complicates dosimetry procedures in a clinical environment. Indeed, under small field conditions, the various perturbations at the detector level can become very large. Thus, the use of the Monte Carlo method is necessary when calibrating and characterizing beams. Such processes, during which reference and relative dose values are measured and entered into treatment planning systems, ensure the validity of treatments as well as patient safety. This thesis focuses on the Monte Carlo modeling of the CyberKnife M6 and studies various applications to small photon fields dosimetry. Firstly, a new method for the correction of the dose rate dependency of silicon diode detectors is proposed. The latter is validated and applied to relative measurements performed at the CyberKnife of the Centre hospitalier de l’Université de Montréal (CHUM). Results illustrate the correction of the systematic error induced in the measurements due to the dose rate dependency of the considered diode. The proposed method provides an efficient solution to this issue. Secondly, a method for the optimization of the source parameters required as input during Monte Carlo beam modeling is introduced. The latter is based on a probabilistic approach and on the comparison of measurements and simulations for various detectors. The method allows the determination of the energy of the electron beam incident on the target of a linac, as well as the full width at half-maximum of its radial distribution. The proposed method, which is applied to the CyberKnife unit of the CHUM, provides a new approach for the optimization of a Monte Carlo beam model and a way to estimate the uncertainties on its source parameters. Thirdly, the developed CyberKnife beam model is used to estimate the impact of source parameter uncertainties on various dosimetric functions commonly used in the clinic environment, and on dose distributions obtained by simulation of treatment plans. Results illustrate the increase of the impact of beam modeling uncertainties with the decrease of the field size, and provide insights on the reachable calculation accuracy for this type of Monte Carlo dose calculation in small fields. Lastly, the TG-51 (manufacturer’s adapted version) and TRS-483 dosimetry protocols are respectively applied and compared for the calibration of the CHUM’s CyberKnife. We observe that TRS-483 is consistent with TG-51. Beam quality and volume averaging correction factors specific to the CHUM's CyberKnife are estimated using Monte Carlo simulations for an Exradin A12 ionization chamber. Results illustrate that the generic value provided in the TRS-483 could be overestimated in comparison to our CyberKnife model and that this overestimation could be due to the volume averaging component.

In radiation therapy treatment planning, recent works have used machine learning based on historically delivered plans to automate the process of producing clinically acceptable plans. Compared to traditional approaches such as repeated weighted-sum optimization or multicriteria optimization (MCO), automated planning methods have, in general, the benefits of low computational times and minimal user interaction, but on the other hand lack the flexibility associated with general-purpose frameworks such as MCO. Machine learning approaches can be especially sensitive to deviations in their dose prediction due to certain properties of the optimization functions usually used for dose mimicking and, moreover, suffer from the fact that there exists no general causality between prediction accuracy and optimized plan quality.In this thesis, we present a means of unifying ideas from machine learning planning methods with the well-established MCO framework. More precisely, given prior knowledge in the form of either a previously optimized plan or a set of historically delivered clinical plans, we are able to automatically generate Pareto optimal plans spanning a dose region corresponding to plans which are achievable as well as clinically acceptable. For the former case, this is achieved by introducing dose--volume constraints; for the latter case, this is achieved by fitting a weighted-data Gaussian mixture model on pre-defined dose statistics using the expectation--maximization algorithm, modifying it with exponential tilting and using specially developed optimization functions to take into account prediction uncertainties.Numerical results for conceptual demonstration are obtained for a prostate cancer case with treatment delivered by a volumetric-modulated arc therapy technique, where it is shown that the methods developed in the thesis are successful in automatically generating Pareto optimal plans of satisfactory quality and diversity, while excluding clinically irrelevant dose regions. For the case of using historical plans as prior knowledge, the computational times are significantly shorter than those typical of conventional MCO. / Inom strålterapiplanering har den senaste forskningen använt maskininlärning baserat på historiskt levererade planer för att automatisera den process i vilken kliniskt acceptabla planer produceras. Jämfört med traditionella angreppssätt, såsom upprepad optimering av en viktad målfunktion eller flermålsoptimering (MCO), har automatiska planeringsmetoder generellt sett fördelarna av lägre beräkningstider och minimal användarinteraktion, men saknar däremot flexibiliteten hos allmänna ramverk som exempelvis MCO. Maskininlärningsmetoder kan vara speciellt känsliga för avvikelser i dosprediktionssteget på grund av särskilda egenskaper hos de optimeringsfunktioner som vanligtvis används för att återskapa dosfördelningar, och lider dessutom av problemet att det inte finns något allmängiltigt orsakssamband mellan prediktionsnoggrannhet och kvalitet hos optimerad plan. I detta arbete presenterar vi ett sätt att förena idéer från maskininlärningsbaserade planeringsmetoder med det väletablerade MCO-ramverket. Mer precist kan vi, givet förkunskaper i form av antingen en tidigare optimerad plan eller en uppsättning av historiskt levererade kliniska planer, automatiskt generera Paretooptimala planer som täcker en dosregion motsvarande uppnåeliga såväl som kliniskt acceptabla planer. I det förra fallet görs detta genom att introducera dos--volym-bivillkor; i det senare fallet görs detta genom att anpassa en gaussisk blandningsmodell med viktade data med förväntning--maximering-algoritmen, modifiera den med exponentiell lutning och sedan använda speciellt utvecklade optimeringsfunktioner för att ta hänsyn till prediktionsosäkerheter.Numeriska resultat för konceptuell demonstration erhålls för ett fall av prostatacancer varvid behandlingen levererades med volymetriskt modulerad bågterapi, där det visas att metoderna utvecklade i detta arbete är framgångsrika i att automatiskt generera Paretooptimala planer med tillfredsställande kvalitet och variation medan kliniskt irrelevanta dosregioner utesluts. I fallet då historiska planer används som förkunskap är beräkningstiderna markant kortare än för konventionell MCO.