The main function of the respiratory system is gas exchange. Since many disease or injury conditions can cause biomechanical or material property changes that can alter lung function, there is a great interest in measuring regional lung function and mechanics. In this thesis, we present a technique that uses multiple respiratory-gated CT images of the lung acquired at different levels of inflation with both breath-hold static scans and retrospectively reconstructed 4D dynamic scans, along with non-rigid 3D image registration, to make local estimates of lung tissue function and mechanics. We validate our technique using anatomical landmarks and functional Xe-CT estimated specific ventilation. The major contributions of this thesis include: 1) developing the registration derived regional expansion estimation approach in breath-hold static scans and dynamic 4DCT scans, 2) developing a method to quantify lobar sliding from image registration derived displacement field, 3) developing a method for measurement of radiation-induced pulmonary function change following a course of radiation therapy, 4) developing and validating different ventilation measures in 4DCT. The ability of our technique to estimate regional lung mechanics and function as a surrogate of the Xe-CT ventilation imaging for the entire lung from quickly and easily obtained respiratory-gated images, is a significant contribution to functional lung imaging because of the potential increase in resolution, and large reductions in imaging time, radiation, and contrast agent exposure. Our technique may be useful to detect and follow the progression of lung disease such as COPD, may be useful as a planning tool during RT planning, may be useful for tracking the progression of toxicity to nearby normal tissue during RT, and can be used to evaluate the effectiveness of a treatment post-therapy.

Thesis (LL.M. (Labour law)) --University of Limpopo, 2010. / Refer to document

The main function of the respiratory system is gas exchange. Since many disease or injury conditions can cause biomechanical or material property changes that can alter lung function, there is a great interest in measuring regional lung ventilation and regional mechanical changes. We describe a technique that uses multiple respiratory-gated CT images of the lung acquired at different levels of inflation with both breath-hold static scans and retrospectively reconstructed dynamic scans, along with non-rigid 3D image registration, to make local estimates of lung tissue expansion. The degree of regional lung expansion is measured using the Jacobian (a function of local partial derivatives) of the registration displacement field. We compare the ventral-dorsal patterns of lung expansion estimated across seven phase changes and three pressure changes to a xenon CT based measure of specific ventilation in four anesthetized sheep studied in the supine orientation. Using 3D image registration to match images acquired at 50% and 75% phase points of the inspiratory portion of the respiratory cycle and 20 cm H2O and 25 cm H2O airway pressures gave the best match between the average Jacobian and the xenon CT specific ventilation respectively (linear regression, average r2=0.85 and r2=0.84). We validate the registration accuracy by 200 semi-automatically matched landmarks and both the dynamic and static scans show landmark error on the order of 2mm.

This thesis examines and identifies the problems of shape collapse in large deformation image registration. Shape collapse occurs in image registration when a region in the moving image is transformed into a set of near zero volume in the target image space. Shape collapse may occur when the moving image has a structure that is either missing or does not sufficiently overlap the corresponding structure in the target image. We state that shape collapse is a problem in image registration because it may lead to the following consequences: (1) Incorrect pointwise correspondence between different coordinate systems; (2) Incorrect automatic image segmentation; (3) Loss of functional signal. The above three disadvantages of registration with shape collapse are illustrated in detail using several examples with both real and phantom data. Shape collapse problem is common in image registration algorithms with large degrees of freedom such as many diffeomorphic image registration algorithms. This thesis proposes a shape collapse measurement algorithm to detect the regions of shape collapse after image registration in pairwise and group-wise registrations. We further compute the shape collapse for a whole population of pairwise transformations such as occurs when registering many images to a common atlas coordinate system. Experiments are presented using the SyN diffeomorphic image registration algorithm and diffeomorphic demons algorithm. We show that shape collapse exists in both of the two large deformation registration methods. We demonstrate how changing the input parameters to the SyN registration algorithm can mitigate the collapse image registration artifacts.

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<p>Buildings, maps and objects et cetera, can be modeled using a computer or reconstructed in 3D by data from different kinds of cameras or laser scanners. This thesis concerns the latter. The recent improvements of Time-of-Flight cameras have brought a number of new interesting research areas to the surface. Registration of several ToF camera point clouds is such an area.</p><p>A literature study has been made to summarize the research done in the area over the last two decades. The most popular method for registering point clouds, namely the Iterative Closest Point (ICP), has been studied. In addition to this, an error relaxation algorithm was implemented to minimize the accumulated error of the sequential pairwise ICP.</p><p>A few different real-world test scenarios and one scenario with synthetic data were constructed. These data sets were registered with varying outcome. The obtained camera poses from the sequential ICP were improved by loop closing and error relaxation.</p><p>The results illustrate the importance of having good initial guesses on the relative transformations to obtain a correct model. Furthermore the strengths and weaknesses of the sequential ICP and the utilized error relaxation method are shown.</p>

Since its first use in human in 1929, the electroencephalogram (EEG) has become one of the most important diagnostic tool in clinical neurophysiology. However, their use in clinical studies is limited because the huge quantity of collected information is complicated to treat. Indeed, it is very difficult to have an overall picture of this multivariate problem. In addition to the impressive quantity of data to be treated, an intrinsic problem with electroencephalograms is that the signals are "contaminated" by body signals not directly related to cerebral activity. However, these signals do not interest us directly to evaluate treatment effect on the brain. Removing these signals known as "parasitic noise" from electroencephalograms is a difficult task. We use clinical data kindly made available by the pharmaceutical company Eli Lilly (Lilly Clinical Operations S.A., Louvain-la-Neuve, Belgium). Particular types of analyses were already carried out on these data, most based on frequency bands. They mainly confirmed the enormous potential of EEG in clinical studies without much insight in the understanding of treatment effect on the brain. The aim of this thesis is to propose and evaluate a panel of statistical techniques to clean and to analyze electroencephalograms. The first presented tool enables to align curves such as selected parts of EEGs before any further statistical treatment. Indeed, when monitoring some continuous process on similar units (like patients in a clinical study), one often notices a typical pattern common to all curves but with variation both in amplitude and dynamics across curves. In particular, typical peaks could be shifted from unit to unit. This complicates the statistical analysis of sample of curves. For example, the cross-sectional average usually does not reflect a typical curve pattern: due to shifts, the signal structure is smeared or might even disappear. Another of the presented tools is based on the preliminary linear decomposition of EEGs into statistically independent signals. This decomposition provides on the one hand an effective cleaning method and on the other hand a considerable reduction of the quantity of data to be analyzed. The technique of decomposition of our signals in statistically independent signals is a well-known technique in physics primarily used to unmix sound signals. This technique is named Independent Component Analysis or ICA. The last studied tool is functional ANOVA. The analysis of longitudinal curve data is a methodological and computational challenge for statisticians. Such data are often generated in biomedical studies. Most of the time, the statistical analysis focuses on simple summary measures, thereby discarding potentially important information. We propose to model these curves using non parametric regression techniques based on splines.

This thesis aims at introducing a methodology for clinical evaluation of orthodontic treatments using three-dimensional dento-maxillofacial images. Since complementary information is achieved by integrating multiple modalities, cone-beam computed tomography (CBCT) and stereophotogrammetry technologies are used to develop a methodology for tracking bone and facial skin variations over time. Our proposed methodology consists of a two-phase registration procedure. In the first phase, the multimodal images are registered using an extrinsic landmark-based registration followed by a robust Iterative Closest Points (ICP) method. In the second phase, by utilizing specific anatomical landmarks, single modal images of the skull and the mandible are registered over time using an intrinsic landmark-based registration method followed by the robust ICP algorithm. The results of registrations show that the signed error distribution of both mandible and skull registrations follow a normal distribution while all the errors fall within the CBCT precision range.

Femoroacetabular impingement (FAI) has been increasingly recognized as a cause of early hip osteoarthritis. FAI is characterized by pathologic contact between the femur and acetabular rim during hip join movement, caused by morphological abnormalities. Arthroscopic technique has become increasingly popular for FAI surgical treatment because of its minimal invasiveness. However, it involves cumbersome procedures and over- or under-resection are likely to occur. To tackle this issue, robot-assisted FAI arthroscopy is a well suited approach because it results in high accuracy and reproducible surgical outcomes. This dissertation provides new approaches and methods for the current challenges in the development of robot-assisted FAI arthroscopy. The study has three objectives: 1) to develop a robust calibration method for the A-mode ultrasound probe used for noninvasive bone registration, 2) to develop a bone registration simulator for verifying the registration accuracy and consistency for any given registration point-pattern, and 3) to develop a hip range of motion simulation system that returns the virtual range of motion and determines the bone resection volume. Carefully designed calibration procedures and simulation experiments have been conducted during the study of this research. From the experimental results, the developed ultrasound calibration method successfully reduces the registration errors and is proved to be robust. The results from the registration simulator indicate that the pattern with widely distributed points lead to better registration accuracy and consistency. The hip range of motion simulation system results in acceptable accuracy and successfully generates the resection volume. With further modifications, the ultrasound probe can be successfully calibrated with the developed method, and will be applied for noninvasive bone registration. The registration simulator can also be served as a useful tool for determining the optimized registration point-pattern, which can lead to reduced surgical trauma and registration time. Finally, the developed range of motion simulation system can allow the surgeon to evaluate the surgical outcome and to determine the resection volume even before the surgery begins. To conclude, this dissertation provides useful approaches, methods, and software for developing robot-assisted FAI arthroscopy.

Buildings, maps and objects et cetera, can be modeled using a computer or reconstructed in 3D by data from different kinds of cameras or laser scanners. This thesis concerns the latter. The recent improvements of Time-of-Flight cameras have brought a number of new interesting research areas to the surface. Registration of several ToF camera point clouds is such an area. A literature study has been made to summarize the research done in the area over the last two decades. The most popular method for registering point clouds, namely the Iterative Closest Point (ICP), has been studied. In addition to this, an error relaxation algorithm was implemented to minimize the accumulated error of the sequential pairwise ICP. A few different real-world test scenarios and one scenario with synthetic data were constructed. These data sets were registered with varying outcome. The obtained camera poses from the sequential ICP were improved by loop closing and error relaxation. The results illustrate the importance of having good initial guesses on the relative transformations to obtain a correct model. Furthermore the strengths and weaknesses of the sequential ICP and the utilized error relaxation method are shown.