Brain tumours, even though rare, are one of the deadliest types of cancer. The five year survival rate for the most malignant type of brain tumours is below 5%. Modern medicine provides many options for treating brain cancer such as radiotherapy and chemotherapy. However, one of the most effective ways of fighting the disease is surgical resection. During such a procedure the tumour is partially or completely removed. Unfortunately, even after a complete resection some tumourous tissue is left behind and can grow back or metastasise to a different location in the brain. It has been shown, however, that more aggressive resections lead to longer life expectancy. This does not come without risks. Depending on tumour location, extensive resections can lead to transient or permanent post-operative neurological deficits. Therefore, when planning a procedure, the neurosurgeon needs to find balance between extending patients life and maintaining its quality. Recent developments in Magnetic Resonance Imaging (MRI) fueled by the field of human cognitive neuroscience have led to improved methods of non-invasive imaging of the brain function. Such methods allow the creation of functional brain maps of populations or individual subjects. Adapting this technique to the clinical environment enables the assessment of the risks and to plan surgical procedures. The following work aims at improving the use of functional MRI with a specific clinical goal in mind. The thesis begins with description of etiology, epidemiology and treatment options for brain tumours. This is followed by a description of MRI and related data processing methods, which leads to introduction of a new technique for thresholding statistical maps which improves upon existing solutions by adapting to the nature of the problem at hand. In contrast to methods used in cognitive neuroscience our approach is optimized to work on single subjects and maintain a balance between false positive and false negative errors. This balance is crucial for accurate assessment of the risk of a surgical procedure. Using this method a test-retest reliability study was performed to assess five different behavioural paradigms and scanning parameters. This experiment was performed on healthy controls and was aimed at selecting which paradigms produce reliable results and therefore can be used for presurgical planning. This allowed the creation of a battery of task that was applied to glioma patients. Functional maps created before the surgeries were compared with electrocortical stimulation performed during the surgeries. The final contribution of this work focuses on technical aspects of performing neuroimaging data analysis. A novel data processing framework which provides means for rapid prototyping and easy translation and adaptation of already existing methods taken from cognitive neuroscience field is introduced. The framework enables fully automatic processing of patient data and therefore greatly reduced costs while maintaining quality control. A discussion of future directions and challenges in using functional MRI for presurgical planning concludes the thesis.

Spinal cord injury (SCI) leads to axonal damage and limits the ability of the brain to communicate with the rest of the body. Several bioengineered approaches have been developed for the recovery of SCI. Among these techniques, degradable guidance tubes have shown promising results. However, design of nerve guide tubes requires several design considerations and has been a significant challenge. To assess the efficacy of a prototypical implanted nerve guide tubes, it is essential to perform continuous monitoring. In this respect, magnetic resonance imaging (MRI) is one of the most reliable imaging techniques as it offers the ability to achieve extraordinary high temporal and spatial resolution in addition to its non-invasive features. In spite of the excellent image quality of non-enhanced MRI various types of contrast agents have been developed to further enhance the contrast and allow improved visualization. The MRI contrast agents principally work by shortening the T1 or T2 relaxation times of protons located nearby. The presented study was intended to evaluate the in vitro degradation of the nerve guide tubes made of poly (lactic-co-glycolic acid) (PLGA). PLGA tubes incorporated with different concentrations of superparamagnetic iron oxide (SPIO) were scanned by MRI 3T on weekly basis during the degradation period. Spin-echo (SE) sequence with various echo times (TEs) ranged from 13.3 to 314.4 msec was applied. T2 mapping was computed using in-house algorithm developed in Matlab. Least square fit was used to find the slope of the decay curve by plotting log intensity on the y-axis and echo time on the x-axis. The average T2 values were calculated. Mass loss and water uptake of the degrading tubes were also measured weekly. Moreover, the micro-structural changes of the tubes were investigated using the scanning electron microscope (SEM). The MRI results showed that the concentration of SPIO affects the signal intensity of the T2 weighted images reducing the T2 relaxation time value. Accordingly, a linear correlation between SPIO concentration and T2 relaxation time was found. At the beginning of degradation, the SPIO nanoparticles were trapped within the polymeric network. Therefore, water penetration was the predominant factor affecting the T2 relaxation times. At week 5, a significant mass loss was observed. From this stage onwards, the trapped SPIO were released from the polymeric network increasing T2 relaxation time dramatically. According to SEM images, the size of the pores in PLGA guide tubes was increased with the degradation. Approaching the end of degradation, shrinkage of the tubes was observed and the degraded nerve guide tubes were shown to be collapsed. Similar shape variation was observed in T2 weighted MR images. In summary, this study provided an approach to non-invasive monitoring of degradation behavior of nerve guide tubes using contrast enhancement. The developed technique is of great importance since it opened an insight to non-invasive monitoring of tissue engineered scaffolds for in vivo studies.

No description available.

<p>Recent work has shown that hyperpolarized magnetic resonance spectroscopy (HP-MRS) can trace in vivo metabolism of biomolecules and is therefore extremely promising for diagnostic imaging. The most severe challenge this technique faces is the short signal lifetime for hyperpolarization, which is dictated by the spin-lattice (T1) relaxation. In this thesis we show with theory, simulation and experiment that the long-lived nuclear spin states in chemically equivalent or near equivalent spin systems offer a solution to this problem. Spin polarization that has lifetime much longer than T1 (up to 70-fold) has been demonstrated with pulse sequence techniques that are compatible with clinical imaging settings. Multiple classes of molecules have been demonstrated to sustain such long-lived hyperpolarization.</p> / Dissertation

No description available.

Subcortical hyperintensities (SH) are believed to be observable signs of cerebrovascular disease, indicating some form of subcortical vasculopathy. Also commonly referred to as leukoariosis, these hyperintense signals on proton density, T2-weighted and fluid attenuated inversion recovery magnetic resonance images, are commonly observed phenomena in Alzheimer’s disease patients and elderly persons. Several SH sub-types with differential brain-behavior associations have been proposed in the scientific literature: periventricular, deep white, cystic fluid filled lacunar-like infarcts and perivascular Virchow-Robin spaces. This study will present Lesion Explorer (LE): a comprehensive tri-feature MRI-based processing pipeline that effectively and reliably quantifies SH sub-types in the context of additional brain tissues volumetrics in a regionalized manner. The LE pipeline was validated using a scan-rescan procedure. Finally, the LE pipeline was applied in a cross-sectional study of Alzheimer’s disease patients and normal elderly controls. Brain-behavior relationships were demonstrated with regional SH volumes and executive functioning, speed of mental processing, and verbal memory.

Breast cancer is currently the most common diagnosed cancer among women and a significant cause of death. Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Therefore, ongoing epidemiological studies using MRI are evaluating quantitatively breast density in young women. One of the challenges is segmenting the breast in order to calculate total breast volume and exclude non-breast surrounding tissues. This thesis describes an automatic 3D breast volume segmentation based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume in 3D. The boundary localization framework is integrated on a subsequent atlas-based segmentation using a Laplacian framework. The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm respectively when tested on 409 MRI datasets.

Breast cancer is currently the most common diagnosed cancer among women and a significant cause of death. Breast density is considered a significant risk factor and an important biomarker influencing the later risk of breast cancer. Therefore, ongoing epidemiological studies using MRI are evaluating quantitatively breast density in young women. One of the challenges is segmenting the breast in order to calculate total breast volume and exclude non-breast surrounding tissues. This thesis describes an automatic 3D breast volume segmentation based on 3D local edge detection using phase congruency and Poisson surface reconstruction to extract the total breast volume in 3D. The boundary localization framework is integrated on a subsequent atlas-based segmentation using a Laplacian framework. The 3D segmentation achieves breast-air and breast-chest wall boundary localization errors with a median of 1.36 mm and 2.68 mm respectively when tested on 409 MRI datasets.

A novel framework for quantitative analysis of shape and function in magnetic resonance imaging (MRI) of the brain is proposed. First, an efficient method to compute invariant spherical harmonics (SPHARM) based feature representation for real valued 3D functions was developed. This method addressed previous limitations of obtaining unique feature representations using a radial transform. The scale, rotation and translation invariance of these features enables direct comparisons across subjects. This eliminates need for spatial normalization or manually placed landmarks required in most conventional methods [1-6], thereby simplifying the analysis procedure while avoiding potential errors due to misregistration. The proposed approach was tested on synthetic data to evaluate its improved sensitivity. Application on real clinical data showed that this method was able to detect clinically relevant shape changes in the thalami and brain ventricles of Parkinson's disease patients. This framework was then extended to generate functional features that characterize 3D spatial activation patterns within ROIs in functional magnetic resonance imaging (fMRI). To tackle the issue of intersubject structural variability while performing group studies in functional data, current state-of-the-art methods use spatial normalization techniques to warp the brain to a common atlas, a practice criticized for its accuracy and reliability, especially when pathological or aged brains are involved [7-11]. To circumvent these issues, a novel principal component subspace was developed to reduce the influence of anatomical variations on the functional features. Synthetic data tests demonstrate the improved sensitivity of this approach over the conventional normalization approach in the presence of intersubject variability. Furthermore, application to real fMRI data collected from Parkinson's disease patients revealed significant differences in patterns of activation in regions undetected by conventional means. This heightened sensitivity of the proposed features would be very beneficial in performing group analysis in functional data, since potential false negatives can significantly alter the medical inference. The proposed framework for reducing effects of intersubject anatomical variations is not limited to functional analysis and can be extended to any quantitative observation in ROIs such as diffusion anisotropy in diffusion tensor imaging (DTI), thus providing researchers with a robust alternative to the controversial normalization approach.

Current models of face perception suggest independent processing of identity and expression, though this distinction is still unclear. Using converging methods of psychophysics and functional magnetic resonance imaging (fMRI) in healthy and patient populations we assessed the relationship between these two perceptual processes. First, using perceptual aftereffects, we explored the neural representations underlying identity and expression. The expression aftereffect only partially transferred across different identities, suggesting adaptation within identity-invariant and identity-dependent expression representations. Contrarily, the identity aftereffect fully transferred across different expressions. This asymmetry cannot be explained through low-level adaptation. The identity-dependent component of the expression aftereffect relies on adaptation to a coherent expression, not low-level features, in the adapting face. Thus adaptation generating the expression aftereffect must occur within high-level representations of facial expression. Second, using fMRI adaptation, we examined identity and expression sensitivity in healthy controls. The fusiform face area and posterior superior temporal sulcus showed sensitivity for both identity and expression changes. Independent sensitivity for identity and expression changes was observed in the precuneus and middle superior temporal sulcus respectively. Finally, we explored identity and expression perception in a neuropsychological population. Selective identity impairments were associated with inferior occipitotemporal damage, not necessarily affecting the occipital or fusiform face areas. Impaired expression perception was associated with superior temporal sulcus damage, and also with deficits in the integration of identity and expression. In summary, psychophysics, neuroimaging and neuropsychological methods all provide converging evidence for the independent processing of identity and expression within the face network. However, these same methods also supply converging evidence for a partial dependence of these two perceptual processes: in the expression aftereffect, the functional sensitivities of the FFA and pSTS, and identity deficits observed in a patient with primarily impaired expression perception and a spared inferotemporal cortex. Thus, future models of face perception must incorporate representations or regions which independently process identity or expression as well as those which are involved in the perception of both identity and expression.