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Supplemental material, sj-pdf-1-jcb-10.1177_0271678X231152001 for Amyloid-PET of the white matter: Relationship to free water, fiber integrity, and cognition in patients with dementia and small vessel disease by Julie Ottoy, Miracle Ozzoude, Katherine Zukotynski, Min Su Kang, Sabrina Adamo, Christopher Scott, Joel Ramirez, Walter Swardfager, Benjamin Lam, Aparna Bhan, Parisa Mojiri, Alex Kiss, Stephen Strother, Christian Bocti, Michael Borrie, Howard Chertkow, Richard Frayne, Robin Hsiung, Robert Jr Laforce, Michael D Noseworthy, Frank S Prato, Demetrios J Sahlas, Eric E Smith, Phillip H Kuo, Jordan A Chad, Ofer Pasternak, Vesna Sossi, Alexander Thiel, Jean-Paul Soucy, Jean-Claude Tardif, Sandra E Black, Maged Goubran in Journal of Cerebral Blood Flow & Metabolism

Diffusion tensor (DT) and blood oxygen-level dependent (BOLD) imaging are two noninvasive magnetic resonance (MR) techniques that have been used to probe skeletal muscle microstructure and microvasculature, respectively. Over a series of four studies, the work in this thesis aimed at furthering our understanding of baseline DT metrics and BOLD signals in lower limb muscles (calf and foot) of healthy young subjects. Since postural changes have been shown to alter numerous quantities, including fluid volumes and muscle cross sectional area, DT indices and BOLD signal characteristics were examined in response to movement from upright to supine position. Reductions of 3.2-6.7% and 3.4-7.5% were measured in calf DT eigenvalues and apparent diffusion coefficient (ADC) in the various muscles, following 34 and 64 minutes of supine rest, respectively (P Establishment of baseline diffusion metrics in the foot region was feasible (chapter 6). Examination of foot DT indices in response to positional change showed that the metrics decreased from 2.7-4.6% following 34 minutes of supine rest (P Doctor of Philosophy (PhD)

This thesis presents a family of automatic elastic registration methods applicable to single and multimodal images of similar or dissimilar dimensions. These registration algorithms employ a generic dynamic linear elastic continuum mechanics model of the tissue deformation which is discretized using the finite element method. The dynamic deformation model provides spatial and temporal correlation between images acquired from different orientations at different times. First, a volumetric registration algorithm is presented which estimates the deformation field by balancing internal deformation forces of the elastic model against external forces derived from an intensity-based similarity measure between images. The registration is achieved by iteratively solving a reduced form of the dynamic deformation equations in response to image-derived nodal forces. A general approach for automatic deformable image registration is also presented in this thesis which deals with different registration problems within a unified framework irrespective of the image modality and dimension. Using the dynamic deformation model, the problem of deformable image registration is approached as a classical state estimation problem with various image similarity measures providing an observation model. With this formulation, single and multiple-modality, 3D-3D and 3D-2D image registration problems can all be treated within the same framework.The registration is achieved through a Kalman-like filtering process which incorporates information from the deformation model and an observation error computed from an intensity-based similarity measure. Correlation ratio, normalized correlation coefficient, mutual information, modality independent neighborhood descriptor and sum of squared differences between images are similarity/distance measures employed for single and multiple modality image registration in this thesis Doctor of Philosophy (PhD)

Chronic kidney disease (CKD) has an annual mortality rate of 22% and can cause secondary complications including hypertension, anemia, secondary hyperparathyroidism, and malnutrition. Currently, clinical diagnosis and evaluation of CKD involves blood and urine testing and biopsy. MRI is not currently used to image CKD, but there is an interest in developing MRI techniques to test kidney function. Usually, renal functional MRI refers to single images reflecting tissue oxygenation. Using time series information may offer additional information about changes in kidneys as a result of disease. In this thesis, blood oxygen level-dependent (BOLD) MRI and diffusion weighted imaging (DWI) were used to investigate the effects of breath holding and water loading on kidneys. First, BOLD MRI was used to measure effects of breath holding on BOLD signal intensity. DWI and fractal analysis were used to measure changes in diffusion, perfusion and microcirculation shortly after water loading. Breath holding results showed no effect on temporal BOLD signal intensity in young, healthy subjects. A significant decrease in signal intensity was measured in the kidney of a single subject with impaired renal function. Although the renal BOLD signal was found to have fractal characteristics, no changes were measured using this technique between pre- and post-water loading scans during the time period examined. Because the signal appears to behave fractally, this technique may be a good candidate for similar kidney function studies in the future. DWI also remained unchanged as a result of water loading during the post-water loading time period examined. Master of Applied Science (MASc)

Alzheimer’s disease (AD) is a degenerative disease with progressive deterioration of neural networks in the brain. Fractal dimension analysis (FD) of resting state blood oxygen level dependent (BOLD) signals acquired using functional magnetic resonance imaging (fMRI) allows us to quantify complex signalling in the brain and may offer a window into the network erosion. This novel approach can provide a sensitive tool to examine early stages of AD. As AD progresses, we expect to see a reduction in brain connectivity and signal complexity concurrent with biochemical changes (e.g. altered levels of N-acetyl aspartate (NAA), myoinositol (mI) and glutamate as measured using magnetic resonance spectroscopy, MRS), volumetric changes and abnormally high levels of brain iron. Over a series of 4 studies we examined the relationship of BOLD signal complexity and functional connectivity with documented MRI markers of pathology in AD (n=38) as compared to normal controls (NC) (n=16). AD subjects were in early stage of illness (mild to moderate impairment on the mini mental state exam, MMSE). We validated the temporal (short term (within minutes) and longer term (over a number of months)) consistency of FD measurement and choice of BOLD acquisition method (spiral vs. EPI), provided MRI sequence repeat time (TR) was kept constant. FD reduction (decrease in signal complexity) correlated with worsening pathological values on MRS (­NAA decrease and mI increase) and with a decrease in functional connectivity. This demonstrates that FD (signal complexity) reduces in proportion to AD severity. FD reduction is connected to functional connectivity measured through resting state network (RSN) analysis suggesting the reduction in FD relates to neuronal loss rather than altered vascularity. The narrow range of cognitive impairment (such as scores on the MMSE or the clinical dementia rating scale, CDR) likely precluded correlation between these measures and FD or RSN. Functional connectivity (RSN) was also reduced when brain iron levels were increased within certain network nodes (posterior cingulate cortex and lateral parietal cortex). Therefore iron deposition may play a role in network disruption of AD brains. The overall conclusion of this thesis is that signal complexity of BOLD fMRI signals, as measured with FD, may detect early pathology in the progression of AD. FD can detect neuronal changes in deep brain structures before volume loss in these structures and before significant changes in MRS markers were detectable between the AD and NC groups. An FD change mirrors disruptions in functional connectivity but detection is not limited to RSN nodes in the brain. This novel approach could further our understanding of AD and may be applied to other pathologies of the brain. Doctor of Philosophy (PhD)

Breast cancer is a serious health problem that severely affects the society. Science and engineering communities are looking to find more advanced and new cures to contain this deadly disease. There has been considerable success in increasing the efficacy of conventional imaging techniques for breast cancer detection such as mammography. However, the use of ionizing radiation, the incidence of patient discomfort and the low specificity are some of the negative aspects of mammography. Also, the chances to successfully cure breast cancer can increase manifold with early detection of the malignant tumors. Magnetic Resonance Imaging (MRI) is successful with its highly clear and detailed images, but it is considered too expensive for wide screening. Its low specificity is another disadvantage. Microwave imaging of the breast, which uses non-ionizing radiation, is avery active research area of recent times. It is hoped that once developed, it can override the negative aspects of the existing imaging modalities. The idea is to detect malignant lesions in the breast based on their electrical-property contrast with healthy breast tissues. The electrical properties of biological tissues in the microwave region are described by their complex permittivity. Previous studies claimed that malignant tumors have very high permittivity and high loss compared to the surrounding healthy tissue. It was assumed that the modality exploiting this contrast can easily detect sub-centimeter tumors. Hence, very simple models with high permittivity and loss tumor models embedded in low permittivity homogenous models were employed to show the detection capabilities of microwave imaging techniques. The examples were fairly amateurish and claimed unrealistic performance of the rather simplistic microwave imaging techniques. Recent studies have shown that the breast is not as simple an electromagnetic medium as was thought previously. It is a complex heterogeneous environment with high permittivity and high loss healthy tissues (such as glandular tissue) and low permittivity and low loss healthy tissues (such as fat). The internal tissue distribution and volume varies significantly from woman to woman. Thus, simple models made with hemispheres, cylinders, etc., filled with water or oil cannot mimic the reality of the breast in imaging experiments and simulations. This is why there is a considerable demand of representing breasts as realistic physical and numerical models. Physical models are required to experimentally verify the imaging scanning systems with antennas. The numerical models are required for electromagnetic (EM) simulations that are indispensable in designing and testing image reconstruction algorithms. The work in this thesis mainly deals with the construction and representation of breast models both in experiments and in simulations. This is impoliant work toward making microwave imaging a practical stand-alone detection system. Master of Applied Science (MASc)