Compensation of intra-fractional organ motion through multileaf collimator tracking

In this thesis, we present substantial improvements and extensions of a previously inhouse developed multileaf collimator (MLC) tracking system. The MLC tracking system compensates for intra-fractional organ motion by adapting the aperture of a dynamic MLC in real-time to continuously monitored target motion. Our main improvements are: Firstly, the integration of clinically applicable intra-fractional motion monitoring devices based on implanted electromagnetic transponders, a novel x-ray imaging system or a combined external surrogate monitoring and x-ray imaging system. Secondly, the implementation of state-of-the-art respiratory motion forward prediction models to compensate for total system latencies of 0.5 s to 0.6 s. Thirdly, a complete redesigned of the MLC control software towards a high level of application reliability and stability as well as software maintainability and further extendability. We assess the tracking performance in various phantom experiments with sinusoidal, irregular breathing and prostate trajectories. We can reduce the remaining geometric MLC tracking uncertainties to the respiratory motion forward prediction error. Our film dosimetry evaluations demonstrate that the integrated MLC tracking system can largely eliminate the negative effects of intra-fractional organ motion on the dose distribution

PET-guided delineation of radiation therapy treatment volumes: a survey of image segmentation techniques

Historically, anatomical CT and MR images were used to delineate the gross tumour volumes (GTVs) for radiotherapy treatment planning. The capabilities offered by modern radiation therapy units and the widespread availability of combined PET/CT scanners stimulated the development of biological PET imaging-guided radiation therapy treatment planning with the aim to produce highly conformal radiation dose distribution to the tumour. One of the most difficult issues facing PET-based treatment planning is the accurate delineation of target regions from typical blurred and noisy functional images. The major problems encountered are image segmentation and imperfect system response function. Image segmentation is defined as the process of classifying the voxels of an image into a set of distinct classes. The difficulty in PET image segmentation is compounded by the low spatial resolution and high noise characteristics of PET images. Despite the difficulties and known limitations, several image segmentation approaches have been proposed and used in the clinical setting including thresholding, edge detection, region growing, clustering, stochastic models, deformable models, classifiers and several other approaches. A detailed description of the various approaches proposed in the literature is reviewed. Moreover, we also briefly discuss some important considerations and limitations of the widely used techniques to guide practitioners in the field of radiation oncology. The strategies followed for validation and comparative assessment of various PET segmentation approaches are described. Future opportunities and the current challenges facing the adoption of PET-guided delineation of target volumes and its role in basic and clinical research are also addresse

Personalised body counter calibration using anthropometric parameters

This book describes the development of a new method for personalisation of efficiency factors in partial body counting. Its achieved goal is the quantification of uncertainties in those factors due to variation in anatomy of the measured persons, and their reduction by correlation with anthropometric parameters. The method was applied to a detector system at the In Vivo Measurement Laboratory at Karlsruhe Institute of Technology using Monte Carlo simulation and computational phantoms

Heterogeneous volumetric data mapping and its medical applications

With the advance of data acquisition techniques, massive solid geometries are being collected routinely in scientific tasks, these complex and unstructured data need to be effectively correlated for various processing and analysis. Volumetric mapping solves bijective low-distortion correspondence between/among 3D geometric data, and can serve as an important preprocessing step in many tasks in compute-aided design and analysis, industrial manufacturing, medical image analysis, to name a few. This dissertation studied two important volumetric mapping problems: the mapping of heterogeneous volumes (with nonuniform inner structures/layers) and the mapping of sequential dynamic volumes. To effectively handle heterogeneous volumes, first, we studied the feature-aligned harmonic volumetric mapping. Compared to previous harmonic mapping, it supports the point, curve, and iso-surface alignment, which are important low-dimensional structures in heterogeneous volumetric data. Second, we proposed a biharmonic model for volumetric mapping. Unlike the conventional harmonic volumetric mapping that only supports positional continuity on the boundary, this new model allows us to have higher order continuity $C^1$ along the boundary surface. This suggests a potential model to solve the volumetric mapping of complex and big geometries through divide-and-conquer. We also studied the medical applications of our volumetric mapping in lung tumor respiratory motion modeling. We were building an effective digital platform for lung tumor radiotherapy based on effective volumetric CT/MRI image matching and analysis. We developed and integrated in this platform a set of geometric/image processing techniques including advanced image segmentation, finite element meshing, volumetric registration and interpolation. The lung organ/tumor and surrounding tissues are treated as a heterogeneous region and a dynamic 4D registration framework is developed for lung tumor motion modeling and tracking. Compared to the previous 3D pairwise registration, our new 4D parameterization model leads to a significantly improved registration accuracy. The constructed deforming model can hence approximate the deformation of the tissues and tumor

Models and Analysis of Vocal Emissions for Biomedical Applications

The International Workshop on Models and Analysis of Vocal Emissions for Biomedical Applications (MAVEBA) came into being in 1999 from the particularly felt need of sharing know-how, objectives and results between areas that until then seemed quite distinct such as bioengineering, medicine and singing. MAVEBA deals with all aspects concerning the study of the human voice with applications ranging from the newborn to the adult and elderly. Over the years the initial issues have grown and spread also in other fields of research such as occupational voice disorders, neurology, rehabilitation, image and video analysis. MAVEBA takes place every two years in Firenze, Italy. This edition celebrates twenty-two years of uninterrupted and successful research in the field of voice analysis

Non-invasive lung tumor motion estimation and mitigation in real-time during radiation therapy

Die interfraktionelle Bewegung von Lungentumoren während der Applikation von externer Strahlentherapie kann ein limitierender Faktor für den ehandlungserfolg sein. Tumorbewegung kann sowohl eine Unterdosierung des Zielvolumens als auch eine schwerwiegende Überdosierung des umliegenden gesunden Gewebes zur Folge haben. Die vorliegende Arbeit besteht aus drei Teilen. Im ersten Teil wird eine Methode zur bildgestützten Bewegungsabschätzung von Lungentumoren in Echtzeit vorgestellt. Vorteile des Verfahrens sind die Unabhängigkeit von implantierten Markern (Pneumothorax Risiko) und die Vermeidung von zusätzlicher Bildgebungsstahlendosis, da das Verfahren mit dem Therapiestrahl akquirierte fluoroskopische Bildsequenzen nutzt. Die Validierung erfolgte sowohl anhand von Bildern, die mit einem beweglichen Thoraxphantom aufgenommen wurden, als auch anhand von Bildern, die während einer Reihe von Lungenstereotaxiebehandlungen aufgenommen wurden. Der Fehler (rmse) wurde für den Phantomdatensatz zu < 1mm und für den klinischen Datensatz zu (2.1 ± 1.7)mm bestimmt. Der zweite Teil dieser Arbeit befasst sich mit der Implementierung und Charakterisierung eines dynamischen, adaptiven Bestralungsystems, das durch das Nachführen des Therapiestrahls in Echtzeit Bewegungen des Zielvolumens kompensieren kann. Das System nutzt eine computergestützte, dynamisch ansteuerbare Strahlungsapertur (MLC), welche mit der Position des Zielvolumens aus dem ersten Teil in Echtzeit angesteuert wird. Die Latenzzeit wurde gemessen ( 250 ms) und durch einen linearen Vorhersagealgorithmus kompensiert. Das System wurde mit einem mit Lungentumortrajektorien programmierten dynamischen Thoraxphantom getestet. Der Fehler konnte von 2.4mm bis 3.5mm auf unter 1mm reduziert werden. Im dritten Teil dieser Arbeit wird der Algorithmus vom ersten Teil eingesetzt, um nach jeder Fraktion einer Lungenstereotaxiebehandlung die applizierte Dosis zu berechnen. Das Konzept wurde mit einem dynamischen Thoraxphantom validiert. Mit der retrospektiven Bildanalyse einer Lungenstereotaxiebehandlung konnte gezeigt werden, daß sich fraktionelle Unterdosierungen des Zielvolumens, etwa durch nicht optimale Patientenpositionierung, mit dieser Methode in Form eines Dosis-Volumen Histogramms (DVH) quantifizieren lassen

Surrogate-driven respiratory motion models for MRI-guided lung radiotherapy treatments

An MR-Linac integrates an MR scanner with a radiotherapy delivery system, providing non-ionizing real-time imaging of the internal anatomy before, during and after radiotherapy treatments. Due to spatio-temporal limitations of MR imaging, only high-resolution 2D cine-MR images can be acquired in real-time during MRI-guided radiotherapy (MRIgRT) to monitor the respiratory-induced motion of lung tumours and organs-at-risk. However, temporally-resolved 3D anatomical information is essential for accurate MR guidance of beam delivery and dose estimation of the actually delivered dose. Surrogate-driven respiratory motion models can estimate the 3D motion of the internal anatomy from surrogate signals, producing the required information. The overall aim of this thesis was to tailor a generalized respiratory motion modelling framework for lung MRIgRT. This framework can fit the model directly to unsorted 2D MR images sampling the 3D motion, and to surrogate signals extracted from the 2D cine-MR images acquired on an MR-Linac. It can model breath-to-breath variability and produce a motion compensated super-resolution reconstruction (MCSR) 3D image that can be deformed using the estimated motion. In this work novel MRI-derived surrogate signals were generated from 2D cine-MR images to model respiratory motion for lung cancer patients, by applying principal component analysis to the control point displacements obtained from the registration of the cine-MR images. An MR multi-slice interleaved acquisition potentially suitable for the MR-Linac was developed to generate MRI-derived surrogate signals and build accurate respiratory motion models with the generalized framework for lung cancer patients. The developed models and the MCSR images were thoroughly evaluated for lung cancer patients scanned on an MR-Linac. The results showed that respiratory motion models built with the generalized framework and minimal training data generally produced median errors within the MCSR voxel size of 2 mm, throughout the whole 3D thoracic field-of-view and over the expected lung MRIgRT treatment times