<p>Dynamic contrast-enhanced (DCE) MRI has been widely used as a quantitative imaging method for monitoring tumor response to therapy. The pharmacokinetic parameters derived from this technique have been used in more than 100 phase I trials and investigator led studies. The simultaneous challenges of increasing the temporal and spatial resolution, in a setting where the signal from the much smaller voxel is weaker, have made this MR technique difficult to implement in small-animal imaging. Existing preclinical DCE-MRI protocols acquire a limited number of slices resulting in potentially lost information in the third dimension. Furthermore, drug efficacy studies measuring the effect of an anti-angiogenic treatment, often compare the derived biomarkers on manually selected tumor regions or over the entire volume. These measurements include domains where the interpretation of the biomarkers may be unclear (such as in necrotic areas).</p><p>This dissertation describes and compares a family of four-dimensional (3D spatial + time), projection acquisition, keyhole-sampling strategies that support high spatial and temporal resolution. An interleaved 3D radial trajectory with a quasi-uniform distribution of points in k-space was used for sampling temporally resolved datasets. These volumes were reconstructed with three different k-space filters encompassing a range of possible keyhole strategies. The effect of k-space filtering on spatial and temporal resolution was studied in phantoms and in vivo. The statistical variation of the DCE-MRI measurement is analyzed by considering the fundamental sources of error in the MR signal intensity acquired with the spoiled gradient-echo (SPGR) pulse sequence. Finally, the technique was applied for measuring the extent of the opening of the blood-brain barrier in a mouse model of pediatric glioma and for identifying regions of therapeutic effect in a model of colorectal adenocarcinoma. </p><p>It is shown that 4D radial keyhole imaging does not degrade the system spatial and temporal resolution at a cost of 20-40% decrease in SNR. The time-dependent concentration of the contrast agent measured in vivo is within the theoretically predicted limits. The uncertainty in measuring the pharmacokinetic parameters with the sequences is of the same order, but always higher than, the uncertainty in measuring the pre-injection longitudinal relaxation time. The histogram of the time-to-peak provides useful knowledge about the spatial distribution of K^trans and microvascular density. Two regions with distinct kinetic parameters were identified when the TTP map from DCE-MRM was thresholded at 1000 sec. The effect of bevacizumab, as measured by a decrease in K^trans, was confined to one of these regions. DCE-MRI studies may contribute unique insights into the response of the tumor microenvironment to therapy.</p> / Dissertation

PET (positron emission tomography) scans are still in the experimental phase, as one of the newest breast cancer diagnostic techniques. It is becoming the new standard in neurology, oncology and cardiology. PET, like other nuclear medicine diagnostic and treatment techniques, involves the use of radiation. Because of the negative impact of radioactivity to our bodies the radiation doses in PET should be small. The existing computing algorithms for calculating PET images can be divided into two broad categories: analytical and iterative methods. In the analytical approach the relation between the picture and its projections is expressed by a set of integral equations which are then solved analytically. The Fourier backprojection (FBP) algorithm is a numerical approximation of this analytical solution. Iterative approaches use deterministic (ART = Algebraic Reconstructed Technique) or stochastic (EM = Expectation Maximization) algorithms. My proposed kernel density estimation (KDE) algorithm also falls also into the category of iterative methods. However, in this approach each coincidence event is considered individually. The estimate location of the annihilation event that caused each coincidence event is based on the previously assigned location of events processed earlier. To accomplish this, we construct a probability distribution along each coincidence line. This is generated from previous annihilation points by density estimation. It is shown that this density estimation approach to PET can reconstruct an image of an existing tumor using significantly less data than the standard CT algorithms, such as FBP. Therefore, it might be very promising technique allowing reduced radiation dose for patients, while retaining or improving image quality.

This dissertation outlines work taken towards the understanding, implementation, and improvements to the process of creating of quantitative images of the bulk-electrical parameters of the interior of unknown objects via the use of electromagnetic scattering data. Improvements are considered to both theory and experiments using low-power radiation in the microwave frequency range, known as Microwave Tomography (MWT). A detailed derivation of the Multiplicative-Regularized Contrast-Source Inversion (MR-CSI) method is given, and we compare the performance of MR-CSI with the other leading inversion technique used in MWT: the Gauss Newton/Distorted Born Iterative Method. The inversion results of the two algorithms are very similar, and thus most of the differences between them are in the relative ease of implementation and computational resource use. We further introduce a new version of the CSI algorithm, based on the Finite-Difference method. Using this algorithm, we show that when accurate information about a scatterer is known before the inversion process, this information is best utilized as an artificial computational background, as opposed to an initial guess of the scatterer. The MWT problem is also formulated inside of a conductive enclosure, which significantly changes the physics, and resultant Green's function, of the MWT problem. The implications and possible advantages of this type of MWT are discussed, and synthetic inversion results for a circular enclosed system are presented. These results show that the enclosure is capable of improving the inversion in some regions, although more research is required to realize the full potential of conductive-enclosure MWT. In the final section, experimental results from both open-region and conductor-enclosed type MWT systems developed at the University of Manitoba are shown. For the open-region system, we show that antenna coupling is a major factor affecting the data collection, and provide a simple method for avoiding the frequencies where this coupling is too strong to prevent effective imaging. For the conductor-enclosed type system, we have found the system to be extremely sensitive to presence of antennas in the chamber, and show that effective MWT imaging is possible in this type of system by taking the antenna elements into account in the inverse solver.

The objective of this study was to develop a fluorescent near-infrared endothelial cell binding conjugate using Lycopersicon esculentum lectin and Cy5.5 N-hydroxysuccinimide ester for the purpose of imaging the microvascular network in mouse hearts under in vivo and ex vivo conditions. Cy5.5-lectin conjugate was synthesized with a dye/protein ratio of 2.90 ± 1.54 (n=6). Mouse hearts were successfully labelled in both in vivo and ex vivo and showed similar labelling patterns. Cy5.5-lectin labelling patterns and that of ICAM2 and FITC-lectin co-localized, indicating binding to endothelial cells. Finally, it was shown that Cy5.5-lectin is capable of visualizing, in real-time, areas of normal and abnormal heart perfusion at resolutions of 76.8 pixels/mm. Areas of the heart that were not perfused post-ligation displayed no Cy5.5 staining on histological sections and during real-time cardiac imaging of intact hearts showed minimal fluorescent signal (~35 a.u.) compared to areas where normal perfusion occurred (~150 a.u.).

An extensive literature survey has revealed that the majority of previous work in infrared image processing has ignored the processes leading to the formation of infrared images. Processing has normally either been restricted to simple lowlevel image enhancement convolutions or has consisted of algorithms copied from computer vision without regard for the inherent differences between infrared and visible images. In this thesis, we address the problem of infrared image formation and derive an irradiance equation for simple infrared scenes. We consider the complications caused by mutual illumination of one or more bodies and indicate how the infrared irradiance equation can also be specified for more complex scenes. The infrared irradiance equation we derive is solved in closed form for some simple geometries for both Lambertian and non-Lambertian surfaces. An infrared imager has been built and is described. Images taken with the imager of a variety of scene geometries show that the experimental results compare favourably with the theoretically derived equations, indicating the validity of the theoretical analysis. We describe how a knowledge of the formation of infrared images can be used to predict the image irradiance pattern of a particular object. We also show how, with a knowledge of the radiance properties and surface geometry of the object, it is possible to detect instances of that object in a scene. Examples are given of successful object detection based on an understanding of the image irradiance. We present a brief history of infrared imagers and a description of the principles on which modern infrared imagers are based. In addition to the survey of the literature published on infrared image processing, a brief summary of some techniques from the computer vision literature and their suitability to infrared image processing is given. A selection of vision techniques are applied to both infrared and visible images to verify conclusions reached in the thesis.

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Information obtained from ultrasound images of fetal heads is often used to screen for various types of physical abnormality. In particular, at around 16 to 23 weeks' gestation two-dimensional cross-sections are examined to assess whether a fetus is affected by Neural Tube Defects, a class of disorders that includes Spina Bifida. Unfortunately, ultrasound images are of relatively poor quality and considerable expertise is required to extract meaningful information from them. Developing an ultrasound image recognition method that does not rely upon an experienced sonographer is of interest. In the course of this work we review standard statistical image analysis techniques, and explain why they are not appropriate for the ultrasound image data that we have. A new iterative method for edge detection based on a kernel function is developed and discussed. We then consider ways of improving existing techniques that have been applied to ultrasound Images. Storvik (1994)'s algorithm is based on the minimisation of a certain energy function by simulated annealing. We apply a cascade type blocking method to speed up this minimisation and to improve the performance of the algorithm when the noise level is high. Kass, Witkin and Terzopoulos (1988)'s method is based on an active contour or 'snake' which is deformed in such a way as to minimise a certain energy function. We suggest modifications to this energy function and use simulated annealing plus iterated conditional modes to perform the associated minimisation. We demonstrate the effectiveness of the new edge detection method, and of the improvements to the existing techniques by means of simulation studies.

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The development of a satisfactory the three-dimensional image system is a constant pursuit of the scientific community and entertainment industry. Among the many different methods of producing three-dimensional images, integral imaging is a technique that is capable of creating and encoding a true volume spatial optical model of the object scene in the form of a planar intensity distribution by using unique optical components. The generation of depth maps from three-dimensional integral images is of major importance for modern electronic display systems to enable content-based interactive manipulation and content-based image coding. The aim of this work is to address the particular issue of analyzing integral images in order to extract depth information from the planar recorded integral image. To develop a way of extracting depth information from the integral image, the unique characteristics of the three-dimensional integral image data have been analyzed and the high correlation existing between the pixels at one microlens pitch distance interval has been discovered. A new method of extracting depth information from viewpoint image extraction is developed. The viewpoint image is formed by sampling pixels at the same local position under different micro-lenses. Each viewpoint image is a two-dimensional parallel projection of the three-dimensional scene. Through geometrically analyzing the integral recording process, a depth equation is derived which describes the mathematic relationship between object depth and the corresponding viewpoint images displacement. With the depth equation, depth estimation is then converted to the task of disparity analysis. A correlation-based block matching approach is chosen to find the disparity among viewpoint images. To improve the performance of the depth estimation from the extracted viewpoint images, a modified multi-baseline algorithm is developed, followed by a neighborhood constraint and relaxation technique to improve the disparity analysis. To deal with the homogenous region and object border where the correct depth estimation is almost impossible from disparity analysis, two techniques, viz. Feature Block Pre-selection and “Consistency Post-screening, are further used. The final depth maps generated from the available integral image data have achieved very good visual effects.