Thesis (Ph. D.)--University of Hong Kong, 2008.

Thesis (M.S.)--University of Missouri-Columbia, 2008. / The entire dissertation/thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file (which also appears in the research.pdf); a non-technical general description, or public abstract, appears in the public.pdf file. Title from title screen of research.pdf file (viewed on August 12, 2009) Includes bibliographical references.

Thesis (Ph. D.)--University of Hong Kong, 2009. / Includes bibliographical references (leaves 136-158) Also available online.

The semester thesis describes the basic principles of MRI, methods for measuring diffusion coefficients and creating DWI and DTI images. As a result a practical implementation of program was implemented in Matlab, based on theoretical knowledge of the problem.

published_or_final_version / Psychiatry / Doctoral / Doctor of Philosophy

published_or_final_version / Diagnostic Radiology / Doctoral / Doctor of Philosophy

Diffusion Tensor Magnetic Resonance Imaging (DTI) is a recently-developed technique that can image in vivo the white matter pathways of the central nervous system. This study used 12-direction diffusion-weighted MRI data from nine stroke patients acquired as part of a three-year stroke rehabilitation study coordinated by the Movement Neuroscience Laboratory at the University of Auckland. DTI was used to investigate corticospinal connectivity. From the FA maps, it is found that in those patients whose motor connectivity has been compromised by the stroke to the extent that no motor evoked potential (MEP) can be elicited from a selected affected muscle group, the asymmetry in mean FA values in the posterior limbs of the internal capsules (PLICs) is correlated with functional recovery as measured by the Fugl-Meyer clinical score. Using probabilistic tractography in the contralesional hemisphere produced CST location and somatotopy results that were consistent with those of previous studies. However, in the ipsilesional hemisphere, connectivity results were highly variable. A measure of change in symmetry of mean connectivity is found to correlate with functional recovery as measured by change in FM score. This supports previous work which has correlated CST integrity and functional improvement and it supports the theory that functional recovery after stroke depends on the extent to which motor CNS symmetry can be regained in the new post-stroke architecture. It also suggests that the movement of the fMRI activations occurs in such a way as to make the most of the preserved white matter connectivity.

Background: For Diffusion Tensor Imaging (DTI) to become a clinically useful tool in the detection of traumatic brain injury (TBI) and prediction of functional outcome, a reliable method enabling the identification of likely injury in individual patients needs to be developed. Objective: To explore different methods of analysing DTI measures to determine if individual TBI patients can be differentiated from a group of non-brain injured controls and if so, how these differences are associated with cognitive function. Method: 4 participants with TBI and 11 control participants were scanned using DTI and completed a battery of neuropsychological tests. The DTI measures of Fractional Anisotropy (FA) and Mean Diffusivity (MD) in the uncinate fasciculus were compared across individual TBI patients and a control group using 3 different methods of analysis. Results: The comparison of mean FA/MD from individual TBI patients with the overall mean FA/MD of the control group revealed that some TBI patients had lower values of FA whilst others had increased MD. This difference in FA may be associated with deficits in measures of attention. The histogram curves and cumulative frequency plots for individual TBI patients and the controls revealed subtle yet potentially significant differences in the distribution of FA/MD. However at this stage these differences could not be associated with cognitive function. Conclusion: Initial findings indicate that individual TBI patients can be differentiated from a control group using different methods with differing degrees of sensitivity. These differences may be related to cognitive function but further research is warranted before firm conclusions can be drawn.

Glioblastoma is a highly malignant tumor which mostly recurs locally around the resected contrast enhancement. However, it is difficult to identify tumor invasiveness pre-surgically, especially in non-enhancing areas. Thus, the aim of this thesis was to utilize multimodal MR technique to identify and characterize the peritumoral progression zone that eventually leads to tumor progression. Patients with newly diagnosed cerebral glioblastoma were included consecutively from our cohort between 2010 and2014. The presurgical MRI sequences included volumetric T1-weighted with contrast, FLAIR, T2-weighted, diffusion-weighted imaging, diffusion tensor and perfusion MR imaging. Postsurgical and follow-up MRI included structural and ADC images. Image deformation, caused by disease nature and surgical procedure, renders routine coregistration methods inadequate for MRIs comparison between different time points. Therefore, a two-staged non-linear semi-automatic coregistration method was developed from the modification of the linear FLIRT and non-linear FNIRT functions in FMRIB’s Software Library (FSL). Utilising the above mentioned coregistration method, a volumetric study was conducted to analyse the extent of resection based on different MR techniques, including T1 weighted with contrast, FLAIR and DTI measures of isotropy (DTI-p) and anisotropy (DTI-q). The results showed that patients can have a better clinical outcome with a larger resection of the abnormal DTI q areas. Further study of the imaging characteristics of abnormal peritumoural DTI-q areas, using MRS and DCS-MRI, showed a higher Choline/NAA ratio (p = 0.035), especially higher Choline (p = 0.022), in these areas when compared to normal DTI-q areas. This was indicative of tumour activity in the peritumoural abnormal DTI-q areas. The peritumoural progression areas were found to have distinct imaging characteristics. In these progression areas, compared to non-progression areas within a 10 mm border around the contrast enhancing lesion, there was higher signal intensity in FLAIR (p = 0.02), and T1C (p < 0.001), and there were lower intensity in ADC (p = 0.029) and DTI-p (p < 0.001). Further applying radiomics features showed that 35 first order features and 77 second order features were significantly different between progression and non-progression areas. By using supervised convolutional neural network, there was an overall accuracy of 92.4% in the training set (n = 37) and 78.5% in the validation set (n=14). In summary, multimodal MR imaging, particularly diffusion tensor imaging, can demonstrate distinct characteristics in areas of potential progression on preoperative MRI, which can be considered potential targets for treatment. Further application of radiomics and machine learning can be potentially useful when identifying the tumor invasive margin before the surgery.

Diffusion tensor imaging provides the ability to study white matter connectivity and integrity noninvasively. The information contained in the diffusion tensors is very complex. Therefore a simple way of dealing with tensors is to compute rotationally invariant scalar quantities. These scalar indices have been used to perform population studies between controls and patients with neurological and psychiatric disorders. Implementing the scalar values may reduce the information contained in the whole tensor. A group analysis using the full tensors may give better estimate of white matter changes that occur in the diseased subjects. For spatial normalization of diffusion tensors, it is necessary to interpolate the tensor representation as well as rotate the diffusion tensors after transformation to keep the tensors consistent with the tissue reorientation. Existing reorientation methods cannot be directly used for higher order diffusion models (e.g. q-ball imaging). A novel technique called gradient rotation is introduced where the rotation is directly applied to the diffusion sensitizing gradients providing a voxel by voxel estimate of the diffusion gradients instead of a volume of by volume estimate. The technique is validated by comparing it with an existing method where the transformation is applied to the resulting diffusion tensors. For better matching of diffusion tensors a novel multichannel registration method is proposed based on a non-parametric diffeomorphic demons algorithm. The channels used for the registration include T1-weighted volume and tensor components. A fractional anisotropy (FA) channel is used for defining the contribution of each channel. Including the anatomical data together with the tensors, allows the registration to accurately match the global brain shape and the underlying white matter architecture simultaneously. Using this multichannel registration framework, 10 healthy controls and 9 patients of schizophrenia were spatially normalized. For the group analysis, the tensors were transformed to log-euclidean space. Linear regression analysis was performed on the transformed tensors. Results show that there is a significant difference in the anisotropy between patients and controls especially in the anterior regions that include genu of the corpus callosum and anterior and superior corona radiata, forceps minor and anterior limb on the internal capsule.