Radiotherapy is a potential curative option for liver cancer; however, respiratory motion creates uncertainty in treatment delivery. Advances in imaging and registration techniques can provide information regarding changes in respiratory motion. Currently image registration is challenged by computation and manual intervention. A Navigator Channel (NC) technique was developed to overcome these limitations. A population motion model was generated to predict patient-specific motion, while a point motion detection technique was developed to calculate the patient-specific liver edge motion from images. An adaptation technique uses the relative difference between the population and patient calculated liver edge motion to determine the patient's liver volume motion. The NC technique was tested on patient 4D-CT images for initial validation to determine the accuracy. Accuracy was less than 0.10 mm in liver edge detection and approximately 0.25 cm in predicting patient-specific motion. This technique can be used to ensure accurate treatment delivery for liver radiotherapy.

Radiotherapy is a potential curative option for liver cancer; however, respiratory motion creates uncertainty in treatment delivery. Advances in imaging and registration techniques can provide information regarding changes in respiratory motion. Currently image registration is challenged by computation and manual intervention. A Navigator Channel (NC) technique was developed to overcome these limitations. A population motion model was generated to predict patient-specific motion, while a point motion detection technique was developed to calculate the patient-specific liver edge motion from images. An adaptation technique uses the relative difference between the population and patient calculated liver edge motion to determine the patient's liver volume motion. The NC technique was tested on patient 4D-CT images for initial validation to determine the accuracy. Accuracy was less than 0.10 mm in liver edge detection and approximately 0.25 cm in predicting patient-specific motion. This technique can be used to ensure accurate treatment delivery for liver radiotherapy.

Objectives. The goal in radiotherapy is to deliver adequate radiation to the tumor volume while limiting damage to the surrounding healthy tissue. However, this goal is challenged by respiratory-induced motion. The objective of this work was to identify whether motion in electronic portal images can be tracked with an optical flow algorithm and whether a neural network can predict tumor motion. Methods. A multi-resolution optical flow algorithm that incorporates weighting based on the differences between image frames was used to automatically sample the vectors corresponding to the motion. The global motion was obtained by computing the average weighted mean from the set of vectors. The algorithm was evaluated using tumor trajectories taken from seven lung cancer patients, a 3D printed patient tumor and a virtual dynamic multi-leaf collimator (DMLC) system. The feasibility of detecting and tracking motion at the field edge was examined with a proof-of-concept implementation that included (1) an algorithm that detected local motion, and (2) a control algorithm that adapted the virtual MLC. To compensate for system latency, a generalized neural network, using both offline (treatment planning data) and online (during treatment delivery) learning, was implemented for tumor motion prediction. Results and Conclusions. The algorithm tracked the global motion of the target with an accuracy of around 0.5 mm. While the accuracy is similar to other methods, this approach does not require manual delineation of the target and can, therefore, provide real-time autonomous motion estimation during treatment. Motion at the treatment field edge was tracked with an accuracy of -0.4 ± 0.3 mm. This proof-of-concept simulation demonstrated that it is possible to adapt MLC leaves based on the motion detected at the field edges. Unplanned intrusions of external organs-at-risk could be shielded. A generalized network with a prediction error of 0.59 mm, and a shorter initial learning period (compared to previous studies) was achieved. This network may be used as a plug-and-play predictor in which tumor position could be predicted at the start of treatment and the need for pretreatment data and optimization for individual patients may be avoided. / February 2017

<p>In this project several tests were performed to evaluate the performance of an On-Board Imager® (OBI) mounted on a clinical linear accelerator. The measurements were divided into three parts; geometric accuracy, image registration and couch shift accuracy, and image quality. A cube phantom containing a radiation opaque marker was used to study the agreement with treatment isocenter for both kV-images and cone-beam CT (CBCT) images. The long term stability was investigated by acquiring frontal and lateral kV images twice a week over a 3 month period. Stability in vertical and longitudinal robotic arm motion as well as the stability of the center-of-rotation was evaluated. Further, the agreement of kV image and CBCT center with MV image center was examined.</p><p>A marker seed phantom was used to evaluate and compare the three applications in image registration; 2D/2D, 2D/3D and 3D/3D. Image registration using kV-kV image sets were compared with MV MV and MV-kV image sets. Further, the accuracy in 2D/2D matches with images acquired at non-orthogonal gantry angles was evaluated. The image quality in CBCT images was evaluated using a Catphan® phantom. Hounsfield unit (HU) uniformity and linearity was compared with planning CT. HU accuracy is crucial for dose verification using CBCT data.</p><p>The geometric measurements showed good long term stability and accurate position reproducibility after robotic arm motions. A systematic error of about 1 mm in lateral direction of the kV-image center was detected. A small difference between kV and CBCT center was observed and related to a lateral kV detector offset. The vector disagreement between kV- and MV-image centers was  2 mm at some gantry angles. Image registration with the different match applications worked sufficiently. 2D/3D match was seen to correct more accurately than 2D/2D match for large translational and rotational shifts. CBCT images acquired with full-fan mode showed good HU uniformity but half fan images were less uniform. In the soft tissue region the HU agreement with planning CT was reasonable while a larger disagreement was observed at higher densities. This work shows that the OBI is robust and stable in its performance. With regular QC and calibrations the geometric precision of the OBI can be maintained within 1 mm of treatment isocenter.</p>

In this project several tests were performed to evaluate the performance of an On-Board Imager® (OBI) mounted on a clinical linear accelerator. The measurements were divided into three parts; geometric accuracy, image registration and couch shift accuracy, and image quality. A cube phantom containing a radiation opaque marker was used to study the agreement with treatment isocenter for both kV-images and cone-beam CT (CBCT) images. The long term stability was investigated by acquiring frontal and lateral kV images twice a week over a 3 month period. Stability in vertical and longitudinal robotic arm motion as well as the stability of the center-of-rotation was evaluated. Further, the agreement of kV image and CBCT center with MV image center was examined. A marker seed phantom was used to evaluate and compare the three applications in image registration; 2D/2D, 2D/3D and 3D/3D. Image registration using kV-kV image sets were compared with MV MV and MV-kV image sets. Further, the accuracy in 2D/2D matches with images acquired at non-orthogonal gantry angles was evaluated. The image quality in CBCT images was evaluated using a Catphan® phantom. Hounsfield unit (HU) uniformity and linearity was compared with planning CT. HU accuracy is crucial for dose verification using CBCT data. The geometric measurements showed good long term stability and accurate position reproducibility after robotic arm motions. A systematic error of about 1 mm in lateral direction of the kV-image center was detected. A small difference between kV and CBCT center was observed and related to a lateral kV detector offset. The vector disagreement between kV- and MV-image centers was  2 mm at some gantry angles. Image registration with the different match applications worked sufficiently. 2D/3D match was seen to correct more accurately than 2D/2D match for large translational and rotational shifts. CBCT images acquired with full-fan mode showed good HU uniformity but half fan images were less uniform. In the soft tissue region the HU agreement with planning CT was reasonable while a larger disagreement was observed at higher densities. This work shows that the OBI is robust and stable in its performance. With regular QC and calibrations the geometric precision of the OBI can be maintained within 1 mm of treatment isocenter.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第19225号 / 医博第4024号 / 新制||医||1010(附属図書館) / 32224 / 京都大学大学院医学研究科医学専攻 / (主査)教授 戸井 雅和, 教授 富樫 かおり, 教授 一山 智 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

京都大学 / 新制・課程博士 / 博士(人間健康科学) / 甲第25214号 / 人健博第120号 / 新制||人健||8(附属図書館) / 京都大学大学院医学研究科人間健康科学系専攻 / (主査)教授 杉本 直三, 教授 高桑 徹也, 教授 溝脇 尚志 / 学位規則第4条第1項該当 / Doctor of Agricultural Science / Kyoto University / DFAM

To improve relatively poor outcomes for locally-advanced lung cancer patients, many current efforts are dedicated to minimizing uncertainties in radiotherapy. This enables the isotoxic delivery of escalated tumor doses, leading to better local tumor control. The current dissertation specifically addresses inter-fractional uncertainties resulting from patient setup variability. An automatic block-matching registration (BMR) algorithm is implemented and evaluated for the purpose of directly localizing advanced-stage lung tumors during image-guided radiation therapy. In this algorithm, small image sub-volumes, termed “blocks”, are automatically identified on the tumor surface in an initial planning computed tomography (CT) image. Each block is independently and automatically registered to daily images acquired immediately prior to each treatment fraction. To improve the accuracy and robustness of BMR, this algorithm incorporates multi-resolution pyramid registration, regularization with a median filter, and a new multiple-candidate-registrations technique. The result of block-matching is a sparse displacement vector field that models local tissue deformations near the tumor surface. The distribution of displacement vectors is aggregated to obtain the final tumor registration, corresponding to the treatment couch shift for patient setup correction. Compared to existing rigid and deformable registration algorithms, the final BMR algorithm significantly improves the overlap between target volumes from the planning CT and registered daily images. Furthermore, BMR results in the smallest treatment margins for the given study population. However, despite these improvements, large residual target localization errors were noted, indicating that purely rigid couch shifts cannot correct for all sources of inter-fractional variability. Further reductions in treatment uncertainties may require the combination of high-quality target localization and adaptive radiotherapy.

The purpose of this thesis is to validate the Monte Carlo algorithm for electron radiotherapy in the Eclipse™ treatment planning system (TPS), and to compare the accuracy of the Electron Monte Carlo algorithm (eMC) to the Pencil Beam algorithm (PB) in Eclipse™. Dose distributions from GafChromic™ EBT3 film measurements were compared to dose distributions from eMC and PB treatment plans. Measurements were obtained with 6MeV, 9MeV, and 12MeV electron beams at various depths. A 1 cm thick solid water template with holes for bone-like and lung-like plugs was used to create assorted configurations and heterogeneities. Dose distributions from eMC plans agreed better with the film measurements based on gamma analysis. Gamma values for eMC were between 83%-99%, whereas gamma values for PB treatment plans were as low as 38.66%. Our results show that using the eMC algorithm will improve dose accuracy in regions with heterogeneities and should be considered over PB. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2014. / FAU Electronic Theses and Dissertations Collection

Ce travail de thèse porte sur la quantification et la prise en compte des variations anatomiques en cours de radiothérapie guidée par l'image pour le cancer de la prostate. Nous proposons tout d'abord une approche basée population pour quantifier et analyser les incertitudes géométriques, notamment à travers des matrices de probabilité de présence de la cible en cours de traitement. Nous proposons ensuite une méthode d'optimisation des marges suivant des critères de couverture géométrique de la cible tumorale. Cette méthode permet d'obtenir des marges objectives associées aux différents types d'incertitudes géométriques et aux différentes modalités de repositionnement du patient. Dans un second temps, nous proposons une méthode d'estimation de la dose cumulée reçue localement par les tissus pendant un traitement de radiothérapie de la prostate. Cette méthode repose notamment sur une étape de recalage d'images de façon à estimer les déformations des organes entre les séances de traitement et la planification. Différentes méthodes de recalage sont proposées, suivant les informations disponibles (délinéations ou points homologues) pour contraindre la déformation estimée. De façon à évaluer les méthodes proposées au regard de l'objectif de cumul de dose, nous proposons ensuite la génération et l'utilisation d'un fantôme numérique reposant sur un modèle biomécanique des organes considérés. Les résultats de l'approche sont présentés sur ce fantôme numérique et sur données réelles. Nous montrons ainsi que l'apport de contraintes géométriques permet d'améliorer significativement la précision du cumul et que la méthode reposant sur la sélection de contraintes ponctuelles présente un bon compromis entre niveau d'interaction et précision du résultat. Enfin, nous abordons la question de l'analyse de données de populations de patients dans le but de mieux comprendre les relations entre dose délivrée localement et effets cliniques. Grâce au recalage déformable d'une population de patients sur une référence anatomique, les régions dont la dose est significativement liée aux événements de récidive sont identifiées. Il s'agit d'une étude exploratoire visant à terme à mieux exploiter l'information portée par l'intégralité de la distribution de dose, et ce en fonction du profil du cancer. / This work deals with the quantification and the compensation of anatomical deformations during image-guided radiotherapy of prostate cancer. Firstly, we propose a population-based approach to quantify the geometrical uncertainties by means of coverage probability matrices of the target tumor during the treatment. We then propose a margins optimization method based on geometrical coverage criteria of the tumor target. This method provides rationnal margins models associated to the different geometrical uncertainties and patient repositioning protocols. Secondly, we propose a method to estimate the locally accumulated dose during the treatment. This method relies on a deformable image registration process in order to estimate the organ deformations between each treatment fraction and the planning. Different registration methods are proposed, using different level of user interactions (landmarks specification or delineations) to constrain the deformation estimation. In order to evaluate the performance of the proposed methods, we then describe the generation of a numerical phantom based on a biomechanical model. The results are presented for the numerical phantom and real clinical cases. We show that the benefit brought by the manual placement of some landmarks to constrain the registration represents a good compromise between the required interaction level and the dose estimation accuracy. Finally, we address the issue of the analysis of population data in order to better understand the relationship between the locally delivered dose and clinical effects. With deformable image registration of a population of patients on an anatomical template, regions whose dose is significantly associated with recurrence events are identified. This last part is an exploratory study aiming to better use the information carried by the entire distribution dose, and according to the cancer profile.