AB S TRACT purpose : To further investigate the effects of respiratory motions on CBCT imaging, as well as 4D-CT examinations, with a future goal of using obtained results to implement new methods for individual margins and daily matching procedures into routine clinical practice. background : Since the implementation of CBCT combined with modern accelerators, a higher degree of accuracy has been made possible in RT. However, due to the slow gantry speed of linear accelerators, the imaging procedure of CBCT is a slow process which is thereby degraded by internal motion such as respiration. material and methods : Attain patient specific respiratory motion patterns from CBCT projection data of previous examinations. Utilize this data to perform simulations for both CBCT and 4D-CT using a steering system which allows for arbitrary motion patterns in the longitudinal direction. results : Various imaging with CBCT showed that the resulting images during respiratory motion, can be described by the Probability Density Function of the motion for as long as it does not cause related distortions. This also meant that convolution could be implemented as a model to estimate the CBCT images during oscillation, knowing the object and motion pattern. The 4D-CT examinations using the steering system showed that irregular motion patterns were less accurately described than regular patterns, making the actual motion an important feature to combine together with the measured amplitude. conclusions : It was made clear that CBCT images can be described by the PDF, and thus can be seen as a Color Intensity Projection of the object position. Also it has been shown that the projection data of CBCT images contains valuable information about the respiratory motion of the patient. Another conclusion is that with the help of fiducials, the position of the target within the respiratory cycle can be determined relative to the 4D-CT examination, enabling further input data as to the daily matching procedure, proper applied margins as well as dose to the OAR.

Die Kombination von Positronen-Emissions-Tomographie (PET) und Röntgen-Computertomographie (CT) in Form moderner PET/CT-Geräte ermöglicht die Nutzung der CT-Information zur Korrektur der Photonenschwächung in der PET. Allerdings können Bewegungen, die zum Beispiel durch die Atmung hervorgerufen werden können, zu einer fehlerhaften Schwächungskorrektur führen. Die Einführung von zeitlich aufgelöster Bildgebung für beide Modalitäten (4D-PET/4D-CT) ermöglicht nicht nur die Auflösung von periodischen Bewegungen, sondern auch die Reduktion dieser Fehler in der Schwächungskorrektur. Dazu werden die einzelnen Datensätze des 4D-PET, die jeweils einer bestimmten Bewegungsphase entsprechen, mit dem entsprechenden CT-Datensatz dieser Atemphase schwächungskorrigiert. In der vorliegenden Arbeit wurde diese phasenkorrelierte Schwächungskorrektur des 4D-PET mit dem 4D-CT am Universitästsklinikum Dresden installierten PET/CT ermöglicht und anhand von Phantomexperimenten mit anderen Schwächungskorrekturmethoden für 4D-PET verglichen. Dazu musste zunächst die Aufnahme von 4D-CT an dem verwendeten PET/CT ermöglicht und dessen Synchronität mit dem 4D-PET hergestellt werden. Außerdem wurde ein vorhandenes Atemphantom so modifiziert, dass es typische Bewegungen von Bronchialkarzinomen in zwei Dimensionen und mit zwei möglichen Atemmustern simuliert. Die phasenkorrelierte Schwächungskorrektur führte zu einer quantitativ korrekten Wiederherstellung des Aktivitätsvolumens, der darin enthaltenen Aktivität sowie der Bewegungsamplitude und stellt somit die beste der hier verglichenen 4D-PET-Schwächungskorrekturmethoden dar. Diese Ergebnisse lassen vermuten, dass die phasenkorrelierte Schwächungskorrektur auch bei klinischer Anwendung eine signifikante Verbesserung in oben genannten Punkten darstellt. Dies sollte in Zukunft an Patientendaten überprüft werden. / The combination of Positron Emission Tomography (PET) and Computed Tomography (CT) in one device allows the use of CT-information for attenuation correction in PET. Though motion, for example induced by respiration, can cause inaccurate attenuation correction. The implementation of time-resolved imaging methods for both modalities (4D-PET/4D-CT) enables not only the resolution of motion but also the reduction of artifacts caused by attenuation correction. Therefore, the single datasets of the 4D-PET that are related to a individual respiratory phase, are attenuation corrected with the corresponding dataset of the 4D-CT. This phase correlated attenuation correction of the 4D-PET with the 4D-CT was implemented at the PET/CT installed at the Universitätsklinikum Dresden. For that purpose the acquisition of 4D-CT was implemented at the PET/CT and its synchronisation with the 4D-PET was verified. Furthermore the new attenuation correction method was compared with other attenuation correction methods by performing phantom experiments. Therefore an exisisting respiratory phantom had to be modified to perform typical lung tumor motion in two dimensions with two possible patterns of respiration. The phase correlated attenuation correction leads to a quantitatively correct restauration of the activity volume, its total activity and its motion amplitude. Compared with other correction methods, the phase correlated attenuation correction shows the best results in all examined criteria. This findings suggest that the clinical application of the phase correlated attenuation correction will also lead to a significant improvement in all mentioned points. This has to be verified by analyzing patient data.

Die Kombination von Positronen-Emissions-Tomographie (PET) und Röntgen-Computertomographie (CT) in Form moderner PET/CT-Geräte ermöglicht die Nutzung der CT-Information zur Korrektur der Photonenschwächung in der PET. Allerdings können Bewegungen, die zum Beispiel durch die Atmung hervorgerufen werden können, zu einer fehlerhaften Schwächungskorrektur führen. Die Einführung von zeitlich aufgelöster Bildgebung für beide Modalitäten (4D-PET/4D-CT) ermöglicht nicht nur die Auflösung von periodischen Bewegungen, sondern auch die Reduktion dieser Fehler in der Schwächungskorrektur. Dazu werden die einzelnen Datensätze des 4D-PET, die jeweils einer bestimmten Bewegungsphase entsprechen, mit dem entsprechenden CT-Datensatz dieser Atemphase schwächungskorrigiert. In der vorliegenden Arbeit wurde diese phasenkorrelierte Schwächungskorrektur des 4D-PET mit dem 4D-CT am Universitästsklinikum Dresden installierten PET/CT ermöglicht und anhand von Phantomexperimenten mit anderen Schwächungskorrekturmethoden für 4D-PET verglichen. Dazu musste zunächst die Aufnahme von 4D-CT an dem verwendeten PET/CT ermöglicht und dessen Synchronität mit dem 4D-PET hergestellt werden. Außerdem wurde ein vorhandenes Atemphantom so modifiziert, dass es typische Bewegungen von Bronchialkarzinomen in zwei Dimensionen und mit zwei möglichen Atemmustern simuliert. Die phasenkorrelierte Schwächungskorrektur führte zu einer quantitativ korrekten Wiederherstellung des Aktivitätsvolumens, der darin enthaltenen Aktivität sowie der Bewegungsamplitude und stellt somit die beste der hier verglichenen 4D-PET-Schwächungskorrekturmethoden dar. Diese Ergebnisse lassen vermuten, dass die phasenkorrelierte Schwächungskorrektur auch bei klinischer Anwendung eine signifikante Verbesserung in oben genannten Punkten darstellt. Dies sollte in Zukunft an Patientendaten überprüft werden. / The combination of Positron Emission Tomography (PET) and Computed Tomography (CT) in one device allows the use of CT-information for attenuation correction in PET. Though motion, for example induced by respiration, can cause inaccurate attenuation correction. The implementation of time-resolved imaging methods for both modalities (4D-PET/4D-CT) enables not only the resolution of motion but also the reduction of artifacts caused by attenuation correction. Therefore, the single datasets of the 4D-PET that are related to a individual respiratory phase, are attenuation corrected with the corresponding dataset of the 4D-CT. This phase correlated attenuation correction of the 4D-PET with the 4D-CT was implemented at the PET/CT installed at the Universitätsklinikum Dresden. For that purpose the acquisition of 4D-CT was implemented at the PET/CT and its synchronisation with the 4D-PET was verified. Furthermore the new attenuation correction method was compared with other attenuation correction methods by performing phantom experiments. Therefore an exisisting respiratory phantom had to be modified to perform typical lung tumor motion in two dimensions with two possible patterns of respiration. The phase correlated attenuation correction leads to a quantitatively correct restauration of the activity volume, its total activity and its motion amplitude. Compared with other correction methods, the phase correlated attenuation correction shows the best results in all examined criteria. This findings suggest that the clinical application of the phase correlated attenuation correction will also lead to a significant improvement in all mentioned points. This has to be verified by analyzing patient data.

In radiation therapy, it is imperative to deliver high doses of radiation to the tumor while reducing radiation to the healthy tissue. Respiratory motion is the most significant source of errors during treatment. Therefore, it is essential to accurately model respiratory motion for precise and effective radiation delivery. Many approaches exist to account for respiratory motion, such as controlled breath hold and respiratory gating, and they have been relatively successful. They still present many drawbacks. Thus, research has been expanded to tumor tracking. The overall goal of 4D-CT is to predict tumor motion in real time, and this work attempts to move in that direction. The following work addresses both the temporal and the spatial aspects of four-dimensional CT reconstruction. The aims of the paper are to (1) estimate the temporal parameters of 4D models for anatomy deformation using a novel neural network approach and (2) to use intelligently chosen non-uniform, non-separable splines to improve the spatial resolution of the deformation models in image registration.

Cette thèse traite du mouvement respiratoire dans l'imagerie TEP/TDM. L'imagerie TEP est une modalité à exposition longue très influencée par les mouvements involontaires du patient. Ces mouvements produisent des artéfacts dont les conséquences sont sérieuses pour le diagnostic car les tumeurs paraissent plus larges et moins actives. Cette thèse contribue à la résolution de ce problème. En plus de proposer l'architecture d'un système d'acquisition TEP/TDM synchronisée à la respiration, on y développe trois méthodes de traitement de signal et d'images qui peuvent être appliquées pour résoudre différents sous-problèmes: Une méthode originale de segmentation et caractérisation du signal respiratoire pour découvrir les patterns respiratoires "normaux" du patient, une méthode de reconstruction TDM-4D qui permet d'obtenir des images anatomiques du corps à chaque niveau respiratoire désiré et un algorithme itératif amélioré pour reconstruire des images TEP-4D compensées en mouvement respiratoire. Toutes ces méthodes et algorithmes ont été validés et testés sur des données simulées, des données de fantômes, et des données réelles de patients. / This thesis deals with respiratory motion in PET/CT images. It is well known that PET is a modality that requires a long exposure time. During this time, patients moves and breath. These motions produce undesirable artefacts that alter seriously the images and their precision. This has important consequences when diagnosing thoracic, and particularly pulmonary, cancer. Tumours appear larger than they really are and their activity is weaker. This thesis proposes to contribute to solving these problems.We propose the architecture of an integrated PET/CT acquisition system synchronized to respiration. We also develop signal and image processing methods that can be applied to eliminating respiratory artefacts in CT and PET images. The thesis brings three main contributions : An original respiratory signal segmentation and characterization to detect "normal" respiratory patterns, a 4D-CT reconstruction method that creates 3D images of the whole body for any respiratory level and an enhanced iterative algorithm for reconstructing 4D-PET images without respiratory artefacts. The developed methods have validated and tested on simulated, phantom and real patients' data.

The data obtained from computational fluid dynamics (CFD) simulations of blood flow in the heart is plentiful, and processing this data takes time and the procedure for that is not straightforward. This project aims to develop a tool that can semi-automatically process CFD simulation data, which is based on 4D flow computed tomography (CT) data, with minimal user input. The tool should be able to time efficiently calculate flow parameters from the data, and automatically create overview images of the flow field while doing so, to aid the user's analysis process. The tool is coded using Python programming language, and the Python scripts are inputted to the application ParaView for processing of the simulation data.  The tool generates 3 chamber views of the heart by calculating three points from the given patient data, which represent the aortic and mitral valves, and the apex of the heart. A plane is generated that pass through these three points, and the heart is sliced along this plane to visualize 3 chambers of the heart. The camera position is also manipulated to optimize the 3 chamber view. The maximum outflow velocity over the cardiac cycle in the left atrial appendage (LAA) is determined by searching in a time range around the maximum outflow rate of the LAA in a cardiac cycle, and finding the highest velocity value that points away from the LAA in this range. The flow component analysis is calculated in the LAA and left ventricle (LV) by seeding particles in each at the start of the cardiac cycle, and tracking these particles forwards and backwards in time to determine where the particles end up and come from, respectively. By knowing these two aspects, the four different flow components of the blood can be determined in both the LAA and LV.  The tool can successfully create 3 chamber views of the heart model from three semi-automatically determined points, at a manipulated camera location. It can also calculate the maximum outflow velocity of the flow field over a cardiac cycle in the LAA, and perform a flow component analysis of the LAA and the LV by tracking particles forwards and backwards in time through a cardiac cycle. The maximum velocity calculation is relatively time efficient and produces results similar to those found manually, yet the output is dependent on the user-defined inputs and processing techniques, and varies between users. The flow component analysis is also time efficient, produces results for the LV that are comparable to pre-existing research, and produces results for the LAA that are comparable to the LVs' results. Although, the extraction process of the LAA sometimes includes part of the left atrium, which impacts the accuracy of the results. After processing each part, the tool creates a single file containing each part's main results for easier analysis of the patient data. In conclusion, the tool is capable of semi-automatically processing CFD simulation data which saves the user time, and it has thus met all the project aims