Congenital heart disease (CHD) is the most common congenital anomaly, with single ventricle (SV) defects accounting for nearly 10% of all CHD. SV defects tend to be the most severe forms of CHD: all patients born with SV require multiple open heart surgeries, often beginning in the neonatal period, ultimately leading to the Fontan procedure. Due to improvements in surgical procedures and medical care, more patients are surviving into adolescence and adulthood. Brain imaging and pathology studies have shown that patients with SV have differences in brain structure and metabolism even before the first surgery, and as early as in utero. Furthermore, a significant number of patients have new or more severe lesions after the initial surgery, and many still have brain abnormalities into early childhood. However, there are no detailed brain structural data of SV patients in adolescence. Our group recruited a large cohort of post-Fontan SV patients aged 10-19 years. Separate analyses of neuropsychological and behavioral outcomes in these patients show deficits in multiple areas of cognition, increased rates of attention deficit-hyperactivity disorder (ADHD), and increased use of remedial and/or special education services compared to a control group. Post-Fontan adolescents have more gross brain abnormalities, including evidence of chronic ischemic stroke. Furthermore, there are widespread reductions in cortical and subcortical gray matter volume and cortical thickness, some of which are associated with medical and surgical variables. Diffusion tensor imaging (DTI) analyses show widespread areas of altered white matter microstructure in deep subcortical and cerebellar white matter. In this dissertation, I use graph theory methods to characterize structural connectivity based on gray matter (cortical thickness covariance) and white matter (DTI tractography), and examine associations between brain structure and neurodevelopment. I found that brain network connectivity differs in post-Fontan patients compared with controls, both at the global and regional level. Additionally, deficits in overall network structure were associated with impaired neurodevelopment in several domains, including general intelligence, executive function, and visuospatial skills. These data suggest that early neuroprotection should be a major focus in the care of SV patients, with the goal of improving long-term neurodevelopmental outcomes.

Cognitive aging has profound effects on the hippocampus, a brain region that is critical for episodic memory formation and spatial navigation. Accurate episodic memory formation requires pattern separation, a neurocomputational process that orthogonalizes similar stimulus input into distinct neural representations and can be examined using behavioral mnemonic discrimination paradigms in humans. Age-related impairment in hippocampally-dependent cognition emphasizes the importance of identifying modulators of hippocampal plasticity. Critically, studies in older adults have demonstrated that greater cardiorespiratory fitness (CRF) is associated with greater volume of the hippocampus, mitigated age-related decline in spatial mnemonic discrimination, and better visuospatial memory. Nonetheless, how CRF modulates the underlying structural and functional neural correlates of mnemonic discrimination and spatial navigation in cognitive aging remains unknown. Therefore, the overall objective of this dissertation was to examine CRF as a modulator of hippocampal memory system structure and function, specifically regarding the hippocampally-dependent processes of mnemonic discrimination and spatial navigation, in cognitively healthy older adults. In a series of three experiments, we tested the central hypothesis that CRF enhances hippocampal plasticity and modulates the underlying neural correlates of mnemonic discrimination and spatial navigation in older adults. Data for these three experiments came from two studies. In the first, young adults (ages 18-35 years) and older adults (ages 55-85 years) underwent high-resolution fMRI to examine hippocampal subfield blood-oxygenation level-dependent (BOLD) signal during mnemonic discrimination. In the second, older adults (ages 60-80 years) underwent whole-brain, high-resolution fMRI to examine whole-brain BOLD signal during spatial navigation. In both studies, participants performed a submaximal treadmill test to estimate CRF and underwent high-resolution structural MRI to measure hippocampal subfield volumes as markers of neuroplasticity in the hippocampus. The primary goal of Experiment 1 was to examine the prediction that CRF is positively associated with mnemonic discrimination task performance and dentate gyrus (DG)/CA3 volume in older adults, given that these hippocampal subfields are thought to support pattern separation. Contrary to our initial prediction, we did not observe a relationship between CRF and mnemonic discrimination task performance or CRF and DG/CA3 volume. Instead, we observed a significant positive relationship between CRF and the volume of another hippocampal subfield, the bilateral subiculum, in older adult women, but not men. The primary goal of Experiment 2 was to examine the prediction that mnemonic discrimination-related BOLD signal in the hippocampal subfields is modulated by aging and CRF in young and older adults. In line with our initial prediction, there was a significant difference between young and older adults in right DG/CA2-3 BOLD signal during mnemonic discrimination task performance. Most importantly, CRF significantly modulated bilateral subiculum BOLD signal in an opposing fashion in young and older adults. The primary goal of Experiment 3 was to extend the current study beyond the function of the hippocampus in isolation and to examine whole-brain activation in association with an ecologically valid task that engages a large network of brain regions including but not limited to the hippocampus. We predicted that CRF modulates BOLD signal activation patterns driven by the employed spatial navigation task, specifically in brain regions in the frontal, parietal, and temporal cortices, and the cerebellum, given that the previously published literature in older adults has suggested that CRF enhances structural integrity in these regions in addition to the hippocampus. Our results demonstrated that CRF is significantly positively associated with BOLD signal in the right cerebellum lobule VIIa Crus I and Crus II, a region that has been implicated in sequence-based navigation. And, consistent with our results from Experiment 2, this relationship was observed in older adult women. Importantly, the findings of these experiments highlight novel targets of fitness-related neuroplasticity in the older adult brain, including the subiculum subfield of the hippocampus and the cerebellum lobule VIIa Crus I and Crus II. Furthermore, these findings underscore the importance of examining sex as a modulating factor of fitness-related neuroplasticity.

Osteoarthritis is the most common type of arthritis, and is characterized by the loss of articular cartilage in a joint. This eventually leads to painful bone on bone interactions. Since the loss of cartilage is permanent, the main treatment for this disease is pain management until a full joint replacement is needed. To test new potential treatments a consistent way to measure cartilage thickness is needed. The current standard used in the knee to represent cartilage uses joint space width measured from x-rays. This measurement is highly variable, and does not directly show cartilage. Unlike x-rays, magnetic resonance imaging (MRI) can provide direct visualization of soft tissues in the body, like cartilage. One specific MRI method called balanced steady-state free precession (bSSFP) provides useful contrast between cartilage and its surrounding tissues. This allows easy delineation of the cartilage for volume and thickness measurements. Using bSSFP has unique challenges, but can provide quantitative MR tissue parameter information in addition to volume and thickness measurements.Although bSSFP provides useful contrast, it is highly sensitive to variations in the main magnetic field. This results in dark bands of signal null across an image referred to as banding artifacts. There are a few new methods for mitigating this artifact. An analysis of banding artifact reduction methods is presented in this dissertation. The new methods are shown to be better than traditional methods at reducing banding artifact. However, they do not provide as of high signal to noise ratio as traditional methods in most cases. This analysis is helpful in obtaining artifact free images for volume and thickness measurements.Image distortion can be created when there is a magnetic susceptibility mismatch between bordering substances being imaged, like in the sinuses where air and body tissues meet. A map of the main magnetic field variation can be used to fix this distortion in post processing. A novel method for obtaining a map of the main magnetic field variation is developed using bSSFP in this dissertation. In cases where bSSFP contrast is desirable this map can be obtained with no additional scan time.A new way to sift out MR tissue parameters: T2, T1, and M0 is presented in this dissertation using bSSFP. This method obtains biomarkers that can potentially show the presence of Osteoarthritis before cartilage degeneration begins at the same time as anatomical images. Adjunct scans do not need to be run to get these extra parameters saving scan time. Unlike many adjunct scans, it is also resolution matched to the anatomical images.

Graph Signal Processing (GSP) has been used in the analysis of functional Magnetic Resonance Imaging(fMRI). As a holistic view of brain function and the connections between and within brain regions, by structuring data as node points within the brain and modelling the edge connections between nodes. Many studies have used GSP with Blood Oxygenation Level Dependent (BOLD) imaging of the brain and brain activation. Meanwhile, the methodology has seen little use in muscle imaging. Similar to brain BOLD, muscle BOLD (mBOLD) also aims to demonstrate muscle activation. Muscle BOLD depends on oxygenation, vascularization, fibre type, blood flow, and haemoglobin count. Nevertheless the mBOLD signal still follows muscle activation closely. Electromyography (EMG) is another modality for measuring muscle activation. Both mBOLD and EMG can be represented and analyzed with GSP. In order to better understand muscle activation during contraction the proposed method focused on using GSP to model mBOLD data both alone and jointly with EMG. Simultaneous mBOLD imaging and EMG recording of the calf muscles was performed, creating a multimodal dataset. A generalized filtering methodology was developed for the removal of the MRI gradient artifact in EMG sensors within the MR bore. The filtered data was then used to generate a GSP model of the muscle, focusing on gastrocnemius, soleus, and tibialis anterior muscles. The graph signals were constructed along two edge connection dimensions; coherence and fractility. For the standalone mBOLD graph signal models, the models’ goodness of fits were 1.3245 × 10-05 and 0.06466 for coherence and fractility respectively. The multimodal models showed values of 2.3109 × -06 and 0.0014799. These results demonstrate the promise of modelling muscle activation with GSP and its ability to incorporate multimodal data into a singular model. These results set the stage for future investigations into using GSP to represent muscle with mBOLD, EMG, and other biosignal modalities. / Thesis / Master of Applied Science (MASc) / Magnetic Resonance Imaging(MRI) and electromyography (EMG) are techniques used in the analysis of muscle, for detecting injury or deepening the understanding of muscle function. Graph Signal Processing (GSP) is a methodology used to represent data and the information flow between positions. While GSP has been used in modelling the brain, applications to muscle are scarce. This work aimed to model muscle activation using GSP methods, using both MRI and EMG data. To do so, a method for being able to simultaneously record MRI and EMG data was developed through hardware construction and the software implementation of EMG signal filtering. The collected data were then used to construct multiple GSP models based on the coherence and complexity of the signals, the goodness of fit for each of the constructed models were then compared. In conclusion, it is feasible to use GSP to model muscle activity with multimodal MRI and EMG data. This shows promise for future investigations into the applications of GSP to muscle research.

Diagnosing diffuse liver disease first involves measurement of blood enzymes followed by biopsy. However, blood markers lack spatial and diagnostic specificity and biopsy is highly risky and variable. Although structural changes have been evaluated using diffusion weighted imaging (DWI), the technique is minimally quantitative. Quantitative MR diffusion approaches, such as intra-voxel incoherent motion (IVIM) and diffusion tensor imaging (DTI) have been proposed to better characterize diseased liver. However, the so called pseudo-hepatic artefact due to cardiac motion, drastically affects DWI results. The overall goals of this thesis were thus to evaluate the pseudo-hepatic anisotropy artefact on the quality of diffusion tensor (DT) and IVIM metrics, and to identify potential solutions. Intra- and intersession DTI repeatability was evaluated in healthy human livers when varying the number of diffusion encoding gradients (NGD) and number of signal averages (NSA). Although no further advantage was observed with increasing NGD beyond 6 directions, increased NSA improved intra- and inter-session repeatability. The pseudo-hepatic artefact resulted in increased fractional anisotropy (FA) and tensor eigenvalues (λ1, λ2, λ3), most prominent in the left liver lobe during systole of the cardiac cycle. Without taking advantage of tensor directional information, increasing the acquired NGD slightly improved IVIM fit quality thus helping to minimize the pseudo-hepatic artefact. Combining IVIM and DTI resulted in FA values closer to the hypothesized value of 0.0, which, based on liver microstructure is most logical. Although both IVIM-DTI and DTI-IVIM exhibited similar fit R2 values, the latter failed more often, especially near major blood vessels. Thus, IVIM-DTI was concluded to be more robust and thus the better approach. / Thesis / Doctor of Philosophy (PhD)

Molecular breast imaging (MBI) is a relatively new clinical breast imaging modality, which has the potential to have a significant impact in breast cancer screening and perioperative breast imaging for women with high risk factors for developing breast cancer. Two objectives were proposed in this thesis to increase the use of MBI. First, a magnetic resonance (MR)-compatible gamma camera was developed for combined molecular/MR breast imaging. MBI is a functional imaging technique with high specificity and sensitivity but could benefit from the addition of anatomical information from breast MRI for lesion localization, cancer staging, treatment planning and monitoring. A small area (8cm x 8cm) cadmium zinc telluride (CZT) based gamma camera was developed and tested for MR compatibility in both sequential and simultaneous imaging conditions. Results indicated that the gamma camera was minimally affected during both sequential and simultaneous imaging with a gradient echo (GRE) and spoiled gradient echo (GRE) sequence. Signal to noise ratio (SNR) degradation was observed in the MR images but no geometric distortions were observed. Simultaneous imaging is feasible, but a reassessment of the RF shielding would be required to minimize the noise contribution degrading image quality. Second, backscatter photons were investigated as a potential dose reduction technique for MBI. While the effective dose from MBI is relatively low in comparison to other nuclear medicine procedures, the dose is considered high in relation to mammography and in order to increase acceptance as an alternative breast imaging method, dose reduction is an important objective. Backscatter photons have the same spatial information as primary photons but are typically discarded along with other scattered photons. A scatter compensation method called the triple energy window (TEW) was used to extract backscatter photons from the Compton scattering spectrum and added to the primary photons, increasing count sensitivity by 6%. The noise level matched the increase in contrast leading to negligible change in lesion contrast to noise ratio (CNR). Dose reduction is not justified with this particular technique because of the elevated noise level, but the use of backcsatter photons show potential with improved contrast. / Dissertation / Doctor of Philosophy (PhD)

Nanoparticles have the potential to revolutionize medicine, but many obstacles complicate the translation of nanoparticles from the bench to the clinic. A deeper understanding of nanoparticle synthesis parameters that influence nanoparticle size, drug loading, and surface chemistry is needed to accelerate the design of efficacious therapeutic nanoparticle systems. In this work, organic and inorganic nanoparticles were prepared with hydrodynamic diameters below 200 nm for applications in cancer treatment and immunology. Hydrophobic ion pairing was applied to enhance the loading capacity of drugs and peptides in polyester and polysaccharide nanoparticles systems. Polyester nanoparticles were successfully functionalized with streptavidin-Cy3, interferon gamma (IFN-γ), and CX3CL1. Poly(methacrylic acid), chitosan, and polyinosinic-polycytidylic acid (poly(I:C)) were successfully adsorbed to the surfaces of nanoparticles to enhance particle stability and targeting. Iron-based coupling media capable of eliminating ~ 90% of the water signal from an acoustic coupling bath during gradient echo magnetic resonance imaging (MRI) thermometry was successfully designed using magnetic iron oxide nanoparticles to improve the clinical efficacy of MRI-guided focused ultrasound surgery (MRI-FUS). While the critical nanoparticle design criteria may change depending on the biomedical application, fundamental concepts of nanoparticle design and synthesis can be applied across applications. The projects presented here help to bridge the knowledge gap regarding the use of flash nanoprecipitation (FNP) for nanoparticle synthesis. FNP is a scalable nanoparticle fabrication method that produces small, well-defined nanoparticle populations through rapid, turbulent mixing of multiple solvent streams. This work elucidates nanoparticle design concepts that can be applied across a wide variety of biomedical applications. / Doctor of Philosophy / Cancer remains a critical public health issue worldwide because many promising therapies never make it from the lab into the hospital. Many chemotherapeutic drugs are hindered by poor solubility and serious, undesirable side effects. In the past few decades, new production techniques have been developed to create carriers for these drugs to help overcome these obstacles. These carriers can be made from a variety of materials including metals and biodegradable polymers. In fact, it is even possible to create "smart" carriers that react to their environment to travel within the body or release the drugs they contain. Understanding how to design these carriers for different biomedical applications is critical. This work shows how carriers made from metal or polymer can be designed to exhibit desirable characteristics for use in biomedical applications ranging from vaccines to cancer treatment. Various ways to modify the surfaces of these carriers to tailor them for different applications are presented. This work provides valuable information that can help drive the next generation of biomedical innovation.

The possibility to perform virtual, non-invasive, quantitative, in vivo histological assessments might revolutionize entire fields, among which clinical and cognitive neurosciences. Magnetic Resonance Imaging (MRI) is an ideal non-invasive imaging technique to achieve these goals. Tremendous advancements in the last decades have favored the transition of MRI scanners from “imaging devices” to “measurement devices” (Novikov, 2021), thus capable to yield measurements in physical units, which might be further combined to provide quantities describing histological properties of substrates. A central role in this community endeavor has been played by diffusion-weighted MRI (dMRI), which by measuring the dynamics of spin diffusion, allows inferences on geometrical properties of tissues. Yet, conventional dMRI methodologies suffer from poor specificity. In this thesis, techniques aiming at improving the specificity of microstructural descriptions have been explored in dMRI datasets supporting an increasing level of complexity of the dMRI signal representations. Applications in individuals with different age range, in different populations, and for different MRI scanner fields, have been considered. Firstly, tractography has been combined with Diffusion Tensor Imaging (DTI), an along-tract framework, and morphometry, in the study of the microstructure of the optic radiations in different groups of blind individuals. Secondly, DTI has been combined with Free-Water Imaging (FWI) to monitor the effect of proton-irradiation in a pediatric brain tumor case study. Thirdly, FWI and Diffusion Kurtosis Imaging (DKI) have been combined with an advanced thalamic segmentation framework to study the associations between motor performance and thalamic microstructure in a cohort of individuals affected by Parkinson’s disease. Finally, the largest contribution of this thesis is represented by the adaptation of the Correlation Tensor Imaging - a technique increasing the specificity of DKI harnessing Double Diffusion Encoding previously applied only in preclinical settings - for a clinical 3 T scanner. The ensuing investigation revealed new important insights on the sources of diffusional kurtosis, in particular of the microscopic kurtosis (μK), a component so far neglected by contemporary neuroimaging techniques, which might carry an important clinical role (Alves et al., 2022), and can now be accessed by clinical scanners. In conclusion, strategies to increase the specificity of microstructural descriptions in the brain are presented for different datasets, and their strength and limitations are discussed.

Deep brain stimulation (DBS) improves the quality of life for patients suffering from neurological disorders such as Parkinson’s disease and, more recently, psychiatric/cognitive disorders such as depression and addiction. This treatment option involves the implantation of an implantable pulse generator (or neurostimulator) and leads (or electrodes) implanted deep within the human brain. Magnetic resonance imaging (MRI) is a powerful diagnostic tool that offers superior soft tissue contrast and is routinely used in clinics for neuroimaging applications. MRI is advantageous in DBS pre-surgical planning as precise lead placement within the brain is essential for optimal treatment outcomes. DBS patients can also benefit from post-surgery MRI, and studies have shown that DBS patients are more likely to require MRI within 5-10 years post-surgery. However, imaging DBS patients is restricted by substantial safety concerns that arise from localized electric charge accumulation along the implanted device during resonant radiofrequency (RF) excitation, which can potentially lead to tissue heating and bodily damage. With the technological advancement of ultra-high field (UHF) MRI systems and a growing DBS patient population, DBS MRI safety will become increasingly problematic in the future and needs to be addressed. Parallel RF transmission (pTx) is a promising technology that utilizes multiple transmit channels to generate a desired electromagnetic profile during MRI RF excitation. Several proof-of-concept studies successfully demonstrated its efficacy in creating a "safe mode" of imaging that minimizes the localized RF heating effects. However, pTx MRI systems are not easily accessible and are often custom-built and integrated onto existing MRI systems. Consequently, it adds system characterization and verification complexity to the DBS MRI safety problem. System channel count is also an important consideration as implementation costs can be very high, and the impact of system transmit channel count remains unexplored. Furthermore, in practice, DBS patients with motor-related disorders will impact the pTx MRI system’s ability to precisely generate these safe mode electromagnetic profiles. Commercial DBS devices (i.e., the neurostimulator and leads) are manufactured with fixed dimensions, and the caring surgeon typically manages the surgical orientation of the implanted DBS device and leads. Therefore, lead trajectories can vary hospital-to-hospital. As a result, standard phantoms, i.e., the ASTM International Standard, used in safety verification experiments may not be suitable for DBS MRI applications. To advance DBS patient safety in MRI, this thesis studied the implant heating effects of pTx system uncertainty, system channel count, patient motion on a novel pTx MRI research platform and its associated safe mode of imaging. It developed a new anthropomorphic heterogeneous phantom to improve safety verification experiments. / Dissertation / Doctor of Philosophy (PhD)

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