This thesis is concerned with model-based non-rigid registration of single-modality magnetic resonance images of compressed and uncompressed breast tissue in breast cancer diagnostic/interventional imaging. First, a volumetric registration algorithm is developed which solves the registration as a state estimation problem. Using a static deformation model. To reduce computations, the similarity measure is calculated at some specific points called control points. These control points can be from a low resolution image grid or any irregular image grid. Our numerical analysis has shown that control points placed in the area without much information; i.e with small or no changes in image intensity, yield negligible deformation. Therefore, the selection of the control points can significantly impact the accuracy and computation complexity of the registration algorithms. An extension of the speeded up robust features (SURF) to 3D is proposed for enhanced selection of the control points in deformable image registration. The impact of this new control point selection method on the performance of the registration algorithm is analyzed by comparing it to the case where regular grid control points are used. The results show that the number of control points could be reduced by a factor of ten with new selection methodology without sacrificing performance. Second image registration method is proposed in which, based on a segmented pre-operative image, a deformation model of the breast tissue is developed and discretized in the spatial domain using the method of finite elements. The compression of the preoperative image is modeled by applying smooth forces on the surface of the breast where compression plates are placed. Image registration is accomplished by formulating and solving an optimization problem. The cost function is a similarity measure between the deformed preoperative image and intra-operative image computed at some control point and the decision variables are the tissue interaction forces. / Thesis / Master of Applied Science (MASc)

Image-guided radiation therapy (IGRT) provides accurate and precise tumour targeting. To ensure adequate coverage in IGRT, a planning target volume (PTV) margin is added around the target to account for treatment uncertainties. Treatment plans are designed to deliver a high percentage of the prescription dose to the PTV; thus, portions of healthy tissue are also subjected to high radiation dose. IGRT employs dedicated devices that enable visual assessment of some treatment uncertainties, such as variations in patient set-up. Safe and effective IGRT delivery requires adherence to disease site-specific protocols that describe process details such as imaging technique, alignment method, and corrective action levels. Protocol design is challenging since its effect on treatment accuracy is currently unknown. This thesis aims to understand the interplay between lung IGRT protocol parameters by developing a framework that quantifies geometrical accuracy. Deformable image registration was used to account for changes in target shape and size throughout treatment. Sufficient accuracy was considered when at least 99% of the target surface fell within the PTV. This analysis revealed that the clinical 10 mm PTV margin can be safely reduced by at least 2 mm in each direction. Evaluation of IGRT accuracy was extended to spinal cord alignment. Simulations were carried out with various matching strategies to correct for set-up error, including rotational off-sets. Inappropriate combinations of matching strategies and safety margins resulted in sub-optimal geometrical coverage. Various lung IGRT protocol options were recommended to optimize accuracy and workflow efficiency. For example, an 8 mm PTV margin can be used with spinal cord alignment, a 4 mm cord margin, and up to 5° of rotational error. A more aggressive protocol involved a 6 mm PTV margin with direct target alignment, a 5 mm cord margin, and a 4° rotational tolerance. / Thesis / Master of Science (MSc)

Object Tracking plays a very pivotal role in many computer vision applications such as video surveillance, human gesture recognition and object based video compressions such as MPEG-4. Automatic detection of any moving object and tracking its motion is always an important topic of computer vision and robotic fields. This thesis deals with the problem of detecting the presence of debris or any other unexpected objects in footage obtained during spacecraft launches, and this poses a challenge because of the non-stationary background. When the background is stationary, moving objects can be detected by frame differencing. Therefore there is a need for background stabilization before tracking any moving object in the scene. Here two problems are considered and in both footage from Space shuttle launch is considered with the objective to track any debris falling from the Shuttle. The proposed method registers two consecutive frames using FFT based image registration where the amount of transformation parameters (translation, rotation) is calculated automatically. This information is the next passed to a Kalman filtering stage which produces a mask image that is used to find high intensity areas which are of potential interest.

Radiation therapy has been successful in treating lung cancer patients, but its efficacy is limited by the inability to account for the respiratory motion during treatment planning and radiation dose delivery. Physics-based lung deformation models facilitate the motion computation of both tumor and local lung tissue during radiation therapy. In this dissertation, a novel method is discussed to accurately register 3D lungs across the respiratory phases from 4D-CT datasets, which facilitates the estimation of the volumetric lung deformation models. This method uses multi-level and multi-resolution optical flow registration coupled with thin plate splines (TPS), to address registration issue of inconsistent intensity across respiratory phases. It achieves higher accuracy as compared to multi-resolution optical flow registration and other commonly used registration methods. Results of validation show that the lung registration is computed with 3 mm Target Registration Error (TRE) and approximately 3 mm Inverse Consistency Error (ICE). This registration method is further implemented in GPU based real time dose delivery simulation to assist radiation therapy planning.

Most previous research has explored how words are processed in isolation. However, reading is a complex process where an interplay of various factors affects word identification. Moreover, previous research has mainly focused on alphabetical languages, so extension of the existent findings to non-alphabetical languages is crucial. The current dissertation uses natural reading paradigms to study eye-movements and neurophysiological correlates of the statistical properties of words that affect word recognition during natural reading in English and Chinese. Chapter 2 concerns the time-courses of word frequency and semantic similarity effects in the reading of English derived words. Previous research pointed to a paradox where behavioural experimental techniques showed earlier signatures of these properties than neuro-imaging techniques. By combining eye-tracking and EEG and applying analytical techniques that target the onset of these effects, this study aims at investigating this paradox. Results still show that neurophysiological responses are either largely absent or appear at the same time as shown in eye-movement data. Chapter 3 shows that the existence of spelling errors negatively impacts the recognition of correct spellings in Chinese. This is revealed by the “spelling entropy effect”, which measures the uncertainty about choosing between correct and alternative spelling variants. This is the first study that used co-registration of eye-tracking and EEG to explore the behavioral and neurophysiological signatures of this uncertainty. Chapter 4 studies how segmentation probabilities influence word segmentation and identification when reading Chinese. The results reveal that space becomes beneficial only when located at places where segmentation probability is considered high. This study is among the first to show beneficial effects of spacing at the sentence level and demonstrates how segmentation probabilities play a crucial role in Chinese word segmentation. Cumulatively, the results obtained point to the existence of numerous factors involved in word identification in both alphabetic and logographic languages, which should be explored using natural reading experimental paradigms, such as co-registration of EEG and eye-tracking, for obtaining a multifaceted view of word recognition processes. / Thesis / Doctor of Philosophy (PhD)

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