Στην Πυρηνική Ιατρική, κατά τη διαδικασία ιατρικής απεικόνισης, η κίνηση των οργάνων λόγω της αναπνευστικής λειτουργίας και της σύσπασης του μυοκαρδίου αλλά και των υπόλοιπων κινούμενων οργάνων, δημιουργεί αλλοιώσεις στη διαγνωστική πληροφορία. Η σημαντικότερη αλλοίωση παρατηρείται στον καθορισμό των ορίων συγκεκριμένων οργάνων. Η μελέτη και η ποσοτικοποίηση του φαινομένου καθίσταται αναγκαία στα ευρέως χρησιμοποιούμενα κλινικά πρωτόκολλα πυρηνικής απεικόνισης (SPECT, PET). Το πακέτο προσομοιώσεων Gate είναι ένα πολύ δυνατό εργαλείο που παρέχει τη δυνατότητα ρεαλιστικής μοντελοποίησης συστημάτων πυρηνικής ιατρικής και χρήσης διακριτοποιημένων ομοιωμάτων. Με τη βοήθεια αυτού του εργαλείου και κάνοντας χρήση διακριτοποιημένων ομοιωμάτων XCAT και ITIS μπορούν να προσομοιωθούν ρεαλιστικά κλινικές εξετάσεις που επηρεάζονται από την κίνηση οργάνων. Τα XCAT και ITIS είναι ρεαλιστικά και ευέλικτα μοντέλα ανθρώπινης ανατομίας και φυσιολογίας. Τα XCAT παρέχουν την επιπλέον δυνατότητα της εισαγωγής κίνησης. Αυτή η μελέτη είναι σημαντική, καθώς πολλές ομάδες της επιστημονικής κοινότητας ασχολούνται με την παραγωγή αλγορίθμων διόρθωσης της κίνησης των πνευμόνων και της καρδιάς, κάνοντας χρήση προσομοιώσεων Monte Carlo, με σκοπό τη βελτίωση της απεικόνισης σημαντικών ιατρικών πληροφοριών που αλλοιώνονται λόγω της κίνησης. Οι προσομοιώσεις κλινικών εξετάσεων με το GATE, εισάγοντας ρεαλιστικά ανθρώπινα ομοιώματα, είναι μια μεθοδολογία αιχμής η οποία ανοίγει το δρόμο στη βελτιστοποίηση των διαγνωστικών και θεραπευτικών προσεγγίσεων, παρέχοντας ένα ισχυρό εργαλείο για το σχεδιασμό κλινικών πρωτοκόλλων, την ανάπτυξη διορθωτικών αλγορίθμων και τη μοντελοποίηση παραμέτρων όπως η κίνηση του σώματος εξαιτίας της λειτουργίας της καρδιάς καθώς και του αναπνευστικού συστήματος. / In nuclear medicine, during medical imaging procedures, organs' motion creates artifacts and loss in the diagnostic information, due to respiratory motion and myocardial contraction. The most significant challenge is to define the limits of specific organs and quantify the blurring caused by this motion. The study and quantification of this phenomenon is necessary for clinical protocols used in nuclear imaging (SPECT, PET), so as to achieve accurate diagnosis. GATE is a powerful Monte Carlo simulation toolkit, which enables the realistic modeling of a nuclear imaging system, using voxelized phantoms as input. Using this tool and computational anthropomorphic phantoms such as XCAT and ITIS phantom series can simulate realistically clinical tests. XCAT and ITIS are realistic and flexible models of human anatomy and physiology. XCAT provide the additional capability of importing motion. In the present thesis the XCAT and the ITIS anthropomorphic computational phantoms are used in a series of simulations modeling several clinical cases. Several groups in the scientific community are dealing with the development of motion correction algorithms in order to decrease the blurring in specific organs of interest and to increase the diagnostic value of nuclear imaging. Monte Carlo techniques combined with realistic human models can provide the ground truth for such applications. This is a cutting edge methodology that paves the way for optimization of diagnostic and therapeutic approaches, providing a powerful tool for the design of clinical protocols, developing algorithms and modeling parameters such as body movement due to pulmonary and heart motion.

<p>To date, image localization of mobile tumors prior to radiation delivery has primarily been confined to 2D and 3D technologies, such as fluoroscopy and 3D cone-beam CT (3D-CBCT). Due to the limited information from these images, larger volumes of healthy tissue are often irradiated in order to ensure the radiation field encompasses the entirety of the target motion. Since the overarching goal of radiation therapy is to deliver maximum dose to cancerous cells and simultaneously minimize the radiation delivered to healthy surrounding tissues, it would be ideal to use 4D imaging to obtain time-resolved volume images of the tumor motion during respiration. </p><p>4D-CBCT imaging has been previously investigated, but has not yet seen large clinical translation due to the obstacles of long acquisition time and large image radiation dose. Furthermore, 4D-CBCT currently requires the use of external surrogates to correlate the patient's respiration with the image acquisition process. This correlation has been under question by a multitude of studies demonstrating the uncertainties that exist between the surrogate and the actual motion of the internal anatomy. Errors in the correlation process may result in image artifacts, which could potentially lead to reconstructions with inaccurate target volumes, thereby defeating the purpose of even using 4D-CBCT. </p><p>It is therefore the aim of this dissertation to initially highlight an additional limitation of using 3D-CBCT for imaging respiratory motion and thereby reiterate the need for 4D-CBCT imaging in the treatment room, develop a simple and efficient technique to achieve markerless, self-sorted 4D-CBCT and finally to comprehensively evaluate its robustness across a variety of potential clinical scenarios with a digital human phantom. </p><p>People often spend a longer period of time exhaling as compared with inhaling, and some do so in an extremely disproportionate manner. To demonstrate the disadvantage of using 3D-CBCT in such instances, a dynamic thorax phantom was imaged with a large variety of simulated and patient-derived respiratory traces of ratios of time spent in the inspiration phase versus time spent in the expiration phase (I/E ratio). Canny edge detection and contrast measures were employed to compare the internal target volumes (ITVs) generated per profile. The results revealed that an I/E ratio of less than one can lead to potential underestimation of the ITV with the severity increasing as the inspiration becomes more disproportionate to the expiration. This occurs because of the loss of contrast in the inspiration phase, due to the fewer number of projections acquired there. The measured contrast reduction was as high as 94% for small targets (0.5 cm) moving large amplitudes (2.0 cm) and still as much as 22.3% for large targets (3.0 cm) moving small amplitudes (0.5 cm). This is alarming because the degraded visibility of the target in the inspiration phase may inaccurately impact the alignment of the planning ITV with that of the FB-CBCT and thereby affect the accuracy of the localization and consequent radiation delivery. These potential errors can be avoided with the use of 4D-CBCT instead, to form the composite volume and serve as the verification ITV for alignment.</p><p>In order to delineate accurate target volumes from 4D-CBCT phase images, it is crucial that the projections be properly associated with the patient's respiration. Thus, in order to improve previously developed 4D-CBCT techniques, the basics of Fourier Transform (FT) theory were utilized to extract the respiratory signal directly from the acquired projection data. Markerless, self-sorted 4D-CBCT reconstruction was achieved by developing methods based on the phase and magnitude information of the Fourier Transform. Their performance was subsequently compared to the gold standard of visual identification of peak-inspiration projections. Slow-gantry acquired projections of two sets of physical phantom data with sinusoidal respiratory cycles of 3 and 6 seconds as well as three patients were used as initial evaluation of the feasibility of the Fourier technique. Quantitative criteria consisted of average difference in respiratory phase (ADRP) and percentage of projections assigned within 10% respiratory phase of the gold standard (PP10). For all five projection datasets, the results supported feasibility of both FT-Phase and FT-Magnitude methods with ADRP values less than 5.3% and PP10 values of 87.3% and above. </p><p>Because the technique proved to be promising in the initial feasibility study, a more comprehensive evaluation was necessary in order to assess the robustness of the technique across a larger set of possibilities that may be encountered in the clinic. A 4D digital XCAT phantom was used to generate an array of respiratory and anatomical variables that affect the performance of the technique. The respiratory variables studied included: inspiration to expiration ratio, respiratory cycle length, diaphragmatic motion amplitude, AP chest wall expansion amplitude, breathing irregularities such as baseline shift and inconsistent peak-inspiration amplitude, as well as six breathing profiles derived from cine-MRI images of three healthy volunteers and three lung cancer patients. The anatomical variables studied included: male and female patient size (physical dimension and adipose content), body-mass-index (BMI) category, tumor location, and percentage of the lung in the field-of-view (FOV) of the projection data. CBCT projections of each XCAT phantom were then generated. Additional external imaging factors such as image noise and detector wobble were added to select cases with different percentages of lung in the projection FOV to investigate any effects on the robustness. FT-Phase and FT-Magnitude were each applied and quantitatively compared to the gold standard. Both methods proved to be robust across the studied scenarios with ADRP<10% and PP10>90%, when incorporating minor modifications to region-of-interest (ROI) selection and/or low-frequency location to certain cases of diaphragm amplitude and lung percentage in the FOV of the projection (for which a method may have previously struggled). Nevertheless, in the instance where one method initially faltered, the other method prevailed and successfully identified peak-inspiration projections. This is promising because it suggests that the two methods provide complementary information to each other. To ensure appropriate clinical adaptation of markerless, self-sorted 4D-CBCT, perhaps an optimal integration of the two methods can be developed.</p> / Dissertation

En Radiothérapie Interne Vectorisée (RIV), l'estimation de la dose absorbée aux tumeurs et tissus sains est un outil d'évaluation. et d'optimisation du traitement. Ce travail de thèse a pour objet de déterminer l'apport du code Monte-Carlo GATE dans un contexte d'amélioration des pratiques dosimétriques en RIV. Dans le cadre du projet DosiTest, visant à identifier les étapes critiques de la chaîne dosimétrique au moyen d'une intercomparaison multicentrique virtuelle basée sur une modélisation Monte-Carlo, des jeux de données scintigraphiques ont ainsi été générés avec GATE pour deux patients virtuels (basés sur les modèles XCAT et CIPR 110) et deux radiopharmaceutiques (OctreoscanTM et LutatheraTM), selon une modélisation compartimentale de la biodistribution. Après une étude de validation de GATE pour des applications de dosimétrie clinique en médecine nucléaire - par la comparaison avec le code MCNPX - des calculs dosimétriques de référence ont été réalisés avec GATE. / In Targeted Radionuclide Therapy (TRT), assessing the absorbed dose delivered to tumours and healthy tissues participates to the evaluation and optimisation of the therapy. This PhD work investigates the input of Monte Carlo code GATE as a toolkit for applications in internal dosimetry in a context of improvement of dosimetric methods. Within DosiTest project, which aims at evaluating the impact of the various steps contributing to the realization of a dosimetric study by means of a virtual multi-centric inter-comparison based on Monte-Carlo modelling, scintigraphic datasets were generated with GATE for two virtual patients (using XCAT and ICRP 110 models), for two different radiopharmaceuticals (OctreoscanTM and LutatheraTM) following a compartmental modelling of biodistribution. After a validation study of GATE for clinical internal dosimetry applications by a comparison with radiation transport code MCNPX, reference dosimetric calculations were performed with GATE.

Las enfermedades cardiovasculares (ECVs) son la principal causa de mortalidad en el mundo Occidental. El interés de prevenir y tratar las ECVs ha desencadenado un rápido desarrollo de los sistemas de adquisición de imágenes médicas. Por este motivo, la cantidad de datos de imagen recolectados en las instituciones de salud se ha incrementado considerablemente. Este hecho ha aumentado la necesidad de herramientas automatizadas para dar soporte al diagnóstico, mediante una interpretación de imagen confiable y reproducible. La tarea de interpretación requiere traducir los datos crudos de imagen en parámetros cuantitativos, los cuales son considerados relevantes para clasificar la condición cardiaca de un paciente. Para realizar tal tarea, los métodos basados en modelos estadísticos de forma han recibido favoritismo dada la naturaleza tridimensional (o 3D+t) de las imágenes cardiovasculares. Deformando el modelo estadístico de forma a la imagen de un paciente, el corazón puede analizarse de manera integral. Actualmente, el campo de las imágenes cardiovasculares esta constituido por diferentes modalidades. Cada modalidad explota diferentes fenómenos físicos, lo cual nos permite observar el órgano cardiaco desde diferentes ángulos. El personal clínico recopila todas estas piezas de información y las ensambla mentalmente en un modelo integral. Este modelo integral incluye información anatómica y funcional que muestra un cuadro completo del corazón del paciente. Es de alto interés transformar este modelo mental en un modelo computacional capaz de integrar la información de manera global. La generación de un modelo como tal no es simplemente un reto de visualización. Requiere una metodología capaz de extraer los parámetros cuantitativos relevantes basados en los mismos principios técnicos. Esto nos asegura que las mediciones se pueden comparar directamente. Tal metodología debe ser capaz de: 1) segmentar con precisión las cavidades cardiacas a partir de datos multimodales, 2) proporcionar un marco de referencia único para integrar múltiples fuentes de información, y 3) asistir la clasificación de la condición cardiaca del paciente. Esta tesis se basa en que los modelos estadísticos de forma, y en particular los Modelos Activos de Forma, son un método robusto y preciso con el potencial de incluir todos estos requerimientos. Para procesar múltiples modalidades de imagen, separamos la información estadística de forma de la información de apariencia. Obtenemos la información estadística de forma a partir de una modalidad de alta resolución y aprendemos la apariencia simulando la física de adquisición de otras modalidades. Las contribuciones de esta tesis pueden ser resumidas así: 1) un método genérico para construir automáticamente modelos de intensidad para los Modelos Activos de Forma simulando la física de adquisición de la modalidad en cuestión, 2) la primera extensión de un simulador de Resonancia Magnética Nuclear diseñado para producir estudios cardiacos realistas, y 3) un método novedoso para el entrenamiento automático de modelos de intensidad y de fiabilidad aplicado a estudios cardiacos de Resonancia Magnética Nuclear. Cada una de estas contribuciones representa un artículo publicado o enviado a una revista técnica internacional. / Cardiovascular diseases (CVDs) are the major cause of death in the Western world. The desire to prevent and treat CVDs has triggered a rapid development of medical imaging systems. As a consequence, the amount of imaging data collected in health care institutions has increased considerably. This fact has raised the need for automated analysis tools to support diagnosis with reliable and reproducible image interpretation. The interpretation task requires to translate raw imaging data into quantitative parameters, which are considered relevant to classify the patient’s cardiac condition. To achieve this task, statistical shape model approaches have found favoritism given the 3D (or 3D+t) nature of cardiovascular imaging datasets. By deforming the statistical shape model to image data from a patient, the heart can be analyzed in a more holistic way. Currently, the field of cardiovascular imaging is constituted by different modalities. Each modality exploits distinct physical phenomena, which allows us to observe the cardiac organ from different angles. Clinicians collect all these pieces of information to form an integrated mental model. The mental model includes anatomical and functional information to display a full picture of the patient’s heart. It is highly desirable to transform this mental model into a computational model able to integrate the information in a comprehensive manner. Generating such a model is not simply a visualization challenge. It requires having a methodology able to extract relevant quantitative parameters by applying the same principle. This assures that the measurements are directly comparable. Such a methodology should be able to: 1) accurately segment the cardiac cavities from multimodal datasets, 2) provide a unified frame of reference to integrate multiple information sources, and 3) aid the classification of a patient’s cardiac condition. This thesis builds upon the idea that statistical shape models, in particular Active Shape Models, are a robust and accurate approach with the potential to incorporate all these requirements. In order to handle multiple image modalities, we separate the statistical shape information from the appearance information. We obtain the statistical shape information from a high resolution modality and include the appearance information by simulating the physics of acquisition of other modalities. The contributions of this thesis can be summarized as: 1) a generic method to automatically construct intensity models for Active Shape Models based on simulating the physics of acquisition of the given imaging modality, 2) the first extension of a Magnetic Resonance Imaging (MRI) simulator tailored to produce realistic cardiac images, and 3) a novel automatic intensity model and reliability training strategy applied to cardiac MRI studies. Each of these contributions represents an article published or submitted to a peer-review archival journal.