This research was undertaken in order to investigate the robustness of the Anzai respiratory gating system. Tests were performed to verify the transfer of image data, to identify the method of gating and the accuracy of phase identification. It was found to have sizeable limitations which could result in either incorrectly gated images or serious artefacts. For these reasons it is recommended it be used under the guidance of a suitably qualified physicist.

Lung cancer is the most deadly cancer in the United States. Radiation therapy uses ionizing radiation with high energy to destroy lung tumor cells by damaging their genetic material, preventing those cells from reproducing. The most challenging aspect of modern radiation therapy for lung cancer is the motion of lung tumors caused by patient breathing during treatment. Most gating based radiotherapy derives the tumor motion from external surrogates and generates a respiratory signal to trigger the beam. We propose a method that monitors internal diaphragm motion, which can provide a respiratory signal that is more highly correlated to lung tumor motion compared to the external surrogates. We also investigate direct tracking of the tumor in fluoroscopic video imagery. We tracked fixed tumor contours in fluoroscopic videos for 5 patients. The predominant tumor displacements are well tracked based on optical flow. Some tumors or nearby anatomy features exhibit severe nonrigid deformation, especially in the supradiaphragmatic region. By combining Active Shape Models and the respiratory signal, the deformed contours are tracked within a range defined in the training period. All the tracking results are validated by a human expert and the proposed methods are promising for applications in radiotherapy. Another important aspect of lung patient treatment is patient setup verification, which is needed to reduce inter- and intra-fractions geometry uncertainties and ensure precise dose delivery. Currently, there is no universally accepted method for lung patient verification. We propose to register 4DCT and 2D x-ray images taken before treatment to derive the couch shifts necessary for precise radiotherapy. The proposed technique leads to improved patient care.

Respiratory gating in lung PET imaging to compensate for respiratory motion artifacts is a current research issue with broad potential impact on quantitation, diagnosis and clinical management of lung tumors. However, PET images collected at discrete bins can be significantly affected by noise as there are lower activity counts in each gated bin unless the total PET acquisition time is prolonged, so that gating methods should be combined with imaging-based motion correction and registration methods. The aim of this study was to develop and validate a fast and practical solution to the problem of respiratory motion for the detection and accurate quantitation of lung tumors in PET images. This included: (1) developing a computer-assisted algorithm for PET/CT images that automatically segments lung regions in CT images, identifies and localizes lung tumors of PET images; (2) developing and comparing different registration algorithms which processes all the information within the entire respiratory cycle and integrate all the tumor in different gated bins into a single reference bin. Four registration/integration algorithms: Centroid Based, Intensity Based, Rigid Body and Optical Flow registration were compared as well as two registration schemes: Direct Scheme and Successive Scheme. Validation was demonstrated by conducting experiments with the computerized 4D NCAT phantom and with a dynamic lung-chest phantom imaged using a GE PET/CT System. Iterations were conducted on different size simulated tumors and different noise levels. Static tumors without respiratory motion were used as gold standard; quantitative results were compared with respect to tumor activity concentration, cross-correlation coefficient, relative noise level and computation time. Comparing the results of the tumors before and after correction, the tumor activity values and tumor volumes were closer to the static tumors (gold standard). Higher correlation values and lower noise were also achieved after applying the correction algorithms. With this method the compromise between short PET scan time and reduced image noise can be achieved, while quantification and clinical analysis become fast and precise.

With the introduction of four dimensional-computed tomography (4DCT), treatment centers are now better able to account for respiration-induced uncertainty in radiation therapy treatment planning for lung cancer. We examined two practices in which 4DCT is used in radiotherapy. Our first study investigated the dosimetric uncertainty in four-dimensional (4D) dose calculation using three temporal probability distributions: 1) uniform distribution, 2) sinusoidal distribution, and 3) patient-specific distribution derived from the respiratory trace. Four-dimensional dose was evaluated in nine lung cancer patients. First, dose was computed for each of 10 binned CTs using 4DCT and deformable image registration. Next, the 10 deformed doses were summed together using one of three temporal probability distributions. To compare the two approximated 4D dose calculations to the 4D calculation derived using the patient's respiratory trace, 3D gamma analysis was performed using a tolerance criteria of 3% dose difference and 3mm distance to agreement. Additionally, mean lung dose (MLD), mean tumor dose (MTD), and lung V20 were used to assess clinical impact. For all patients, both uniform and sinusoidal dose distributions were found to have an average gamma passing rate >99% for both the lung and PTV volumes. Compared with 4D dose calculated using the patient respiratory trace, uniform distribution and sinusoidal distribution showed a percentage difference on average of -0.1±0.6% and -0.2±0.4% in MTD, -0.2±2.0% and -0.2±1.3% in MLD, 0.9±2.8% and -0.7±1.8% in lung V20, respectively. We concluded that 4D dose computed using either a uniform or sinusoidal temporal probability distribution is able to approximate 4D dose computed using the patient-specific respiratory trace. Our second study evaluated the dosimetric and temporal effects of respiratory gated radiation therapy using four different gating windows (20EX-20IN, 40EX-40IN, 60EX-60IN, and 80EX-80IN) and estimated the corresponding treatment delivery times for normal (500MU/min) and high (1500MU/min) dose rates. Five patients (3 non-gated, 2 gated 80EX-80IN) were retrospectively evaluated. For each patient, four individual treatment plans corresponding to the four different gating windows were created, and treatment delivery time for each plan was estimated using a MATLAB (MathWorks, Natick, MA) algorithm. Results showed that smaller gating windows reduced PTV volume, mean lung dose, and lung V20, while maintaining mean tumor dose and PTV coverage. Treatment times for gated plans were longer when dose rate was unchanged, however, increased dose rates were shown to achieve treatment times comparable to or faster than non-gated delivery times. We concluded that gated radiation therapy in lung cancer patients could potentially reduce lung toxicity, while as effectively treating the target volume. Furthermore, increased dose rates with gated radiation therapy are able to provide treatment times comparable to non-gated treatment.

In radiation therapy, a treatment plan is designed to make the delivery of radiation to a target more accurate, effective, and less damaging to surrounding healthy tissues. In lung sites, the tumor is affected by the patient’s respiratory motion. Despite tumor motion, current practice still uses a static delivery plan. Unexpected changes due to coughs and sneezes are not taken into account and as a result, the tumor is not treated accurately and healthy tissues are damaged. In this thesis we detail a framework of using an accelerometer device to detect and forecast coughs. The accelerometer measurements are modeled as a ARMA process to make forecasts. We draw from studies in cough physiology and use amplitudes and durations of the forecasted breathing cycles as features to estimate parameters of Gaussian Mixture Models for cough and normal breathing classes. The system was tested on 10 volunteers, where each data set consisted of one 3-5 minute accelerometer measurements to train the system, and two 1-3 minute accelerometer measurements for testing.

In radiation therapy, a treatment plan is designed to make the delivery of radiation to a target more accurate, effective, and less damaging to surrounding healthy tissues. In lung sites, the tumor is affected by the patient’s respiratory motion. Despite tumor motion, current practice still uses a static delivery plan. Unexpected changes due to coughs and sneezes are not taken into account and as a result, the tumor is not treated accurately and healthy tissues are damaged. In this thesis we detail a framework of using an accelerometer device to detect and forecast coughs. The accelerometer measurements are modeled as a ARMA process to make forecasts. We draw from studies in cough physiology and use amplitudes and durations of the forecasted breathing cycles as features to estimate parameters of Gaussian Mixture Models for cough and normal breathing classes. The system was tested on 10 volunteers, where each data set consisted of one 3-5 minute accelerometer measurements to train the system, and two 1-3 minute accelerometer measurements for testing.

La chimioradiothérapie est le traitement des cancers de l’œsophage localement évolués et inopérables. Dans cette indication, la radiothérapie conformationnelle est utilisée couramment. Cependant, le pronostic de ces patients reste sombre. L’intérêt de la radiothérapie asservie à la respiration (RAR) a déjà été montré notamment dans le traitement des cancers pulmonaires, mammaires et hépatiques : elle permet de diminuer l’irradiation des tissus sains, et d’envisager une augmentation de dose au volume tumoral. Afin d’améliorer la prise en charge radiothérapique, nous proposons d’étudier la faisabilité de la RAR dans le traitement des cancers de l’œsophage. Nous étudierons la mobilité des cancers oesophagiens au cours de la respiration pour optimiser la définition des volumes cibles et notamment de la marge interne (IM). Nous analyserons la corrélation existant entre les mouvements tumoraux et les mouvements de la paroi thoracique afin de montrer que le mouvement des tumeurs oesophagiennes est induit par la respiration, pré-requis indispensable à l’utilisation des systèmes d’asservissement en respiration libre. Nous utiliserons différents outils d’analyse dosimétrique pour évaluer l’apport de la RAR dans le traitement des cancers de l’œsophage en comparant les plans dosimétriques effectués à différents temps respiratoires (fin d’expiration, fin d’inspiration et inspiration forcée) avec le plan dosimétrique effectué en respiration libre pour la même tumeur. Ceci nous permettra de quantifier le gain obtenu par la RAR et de déterminer la meilleure « fenêtre » de traitement au cours du cycle respiratoire en fonction des différents systèmes d’asservissement disponibles. Cette analyse dosimétrique sera complétée par un calcul de l’Equivalent de Dose Uniforme (EUD), dans sa forme linéaire quadratique, pour les différents volumes d’intérêt. Nous déterminerons au préalable ses conditions d’utilisation dans une étude théorique de dégradation des HDV / Chemoradiotherapy is now the standard treatment for locally advanced or inoperable esophageal carcinoma. In this indication, conformal radiotherapy is generally used. However, prognosis remains poor for these patients.Respiratory gating radiotherapy can decrease healthy tissus irradiation and allows escalation dose in lung, liver and breast cancer. In order to improve radiotherapy technique, we propose to study the feasibility of respiratory gating for esophageal cancer.We will study the respiratory motions of esophageal cancer to optimize target volume delineation, especially the internal margin (IM).We will test the correlation between tumour and chest wall displacements to prove that esophageal cancer motions are induced by respiration. This is essential before using free breathing respiratory gating systems.We will work out the dosimetric impact of respiratory gating using various dosimetric analysis parameters. We will compare dosimetric plans at end expiration, end inspiration and deep inspiration with dosimetric plan in free-breathing condition. This will allow us to establish the best respiratory phase to irradiate for each gating system.This dosimetric study will be completed with linear quadratic equivalent uniform dose (EUD) calculation for each volume of interest. Previously, we will do a theoretical study of histogram dose volume gradation to point up its use

L'objectif de cette thèse est d’étudier temporellement des phénomènes évolutifs à l'aide de la tomographie d'émission monophotonique (TEMP). La première partie de cette thèse traite le problème du mouvement respiratoire dans les images TEMP de souris. Nous présentons ici une méthode permettant de détecter ce mouvement respiratoire dans les images TEMP 4D, d'extraire un signal respiratoire intrinsèque, et de déterminer la phase du cycle respiratoire sans mouvement la plus large possible. Les données enregistrées durant ces phases sans mouvement sont alors utilisées pour reconstruire une seule image TEMP 3D sans artefact de mouvement par acquisition. Les images ainsi reconstruites présentent un bon compromis en terme de statistiques et de précision des mesures par rapport aux images TEMP 3D de base et TEMP 4D. Dans la deuxième partie, nous étudions la cinétique d'incorporation de l'iode dans l'estomac de souris à partir d'images TEMP 4D. Afin de comprendre le rôle biologique de cette accumulation dans l'estomac, nous avons modélisé le phénomène par une approche d'analyse compartimentale avec un modèle simplifiée à deux compartiments (paroi et cavité stomacale) et une entrée (sang). Les courbes temps-activité (TAC) de chaque compartiment sont déduites des observations et une première estimation des paramètres a été obtenue. / The aim of this thesis is to investigate temporally evolving phenomena with the use of single photon emission computed tomography (SPECT).The first part of this thesis addresses the problem of respiratory motion in SPECT images of mice. The presented method permits us to detect the respiratory motion in 4D SPECT images, to extract an intrinsic respiratory signal and to determine the widest possible phase of the respiratory cycle without movement. The data recorded during these motionless phases are then used to reconstruct a single 3D SPECT image without motion artefacts per acquisition. Reconstructed motionless SPECT images present a good compromise in terms of statistics and accuracy of the measurements with respect to basic 3D SPECT and 4D SPECT images. In the second part, we study the iodide uptake kinetics in the stomach 99mTc-pertechnetate biodistribution with the of mice through the study of use of 4D SPECT images. To understand the biological role of the iodide accumulation in the stomach, we modelled the phenomenon with a compartmental analysis approach using a simplified two-compartment (stomach wall and cavity) model with one input (blood). Time activity curves (TAC) of each compartment are deduced from observations and a first estimation of the parameters was obtained.

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Positron Emission Tomography (PET) is pivotal in medical imaging but is prone to artifactsfrom physiological movements, notably respiration. These motion artifacts both degradeimage quality and compromise precise attenuation correction. To counteract this, gatingstrategies partition PET data in synchronization with respiratory cycles, ensuring each gatenearly represents a static phase. Additionally, a 3D deep learning image registration modelcan be used for inter-gate motion correction, maximizing the use of the full acquired data. Thisstudy aimed to implement and evaluate two gating strategies: an external device-based approachand a data-driven centroid-of-distribution (COD) trace algorithm, and assess their impact on theperformance of the registration model. Analysis of clinical data from four subjects indicated thatthe external device approach outperformed its data-driven counterpart, which faced challengesin real-patient settings. Post motion compensation, both methods achieved results comparableto state-of-the-art reconstructions, suggesting the deep learning model addressed some data-driven method limitations. However, the motion corrected outputs did not exhibit significantimprovements in image quality over state-of-the-art standards. / Positronemissionstomografi (PET) är fundamentalt inom medicinsk avbildning men påverkasav artefakter orsakade av fysiologiska rörelser, framför allt andning. Dessa artefakter påverkarbildkvaliteten negativt och försvårar korrekt attenueringskorrigering. För att motverka dettakan tekniker för rörelsekorrigering tillämpas. Dessa innefattar gating-tekniker där PET-dataförst synkroniseras med andningscykeln för att därefter segmenterateras i olika så kalladegater som representerar en specifick respiratorisk fas. Vidare kan en 3D djupinlärningsmodellanvändas för att korrigera för rörelserna mellan gaterna, vilket optimerar användningen av allinsamlad data. Denna studie implementerade och undersökte två gating-tekniker: en externenhetsbaserad metod och en datadriven ”centroid-of-distribution (COD)” spår-algoritm, samtanalyserade hur dessa tekniker påverkar prestandan av bildregistreringsmodellen. Utifrånanalysen av kliniska data från fyra patienter visade sig metoden med den externa enhetenvara överlägsen den datadrivna metoden, som hade svårigheter i verkliga patient-situationer.Trots detta visade bildregistreringsmodellen potential att delvis kompensera för den datadrivnametodens begränsningar, då resultatet från båda strategeierna var jämförbara med befintligaklinisk bildrekonstruktion. Dock kunde ingen markant förbättring i bildkvalitet urskiljas av derörelsekorrigerade bilderna jämfört med nuvarande toppstandard.