This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.

This thesis is focused on automatic detection of white matter lesions (WML) in Fluid Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) of the brain. There is growing interest within the medical community regarding WML, since the total WML volume per patient (lesion load) was shown to be related to future stroke as well as carotid disease. Manual segmentation of WML is time consuming, labourious, observer-dependent and error prone. Automatic WML segmentation algorithms can be used instead since they give way to lesion load computation in a quantitative, efficient, reproducible and reliable manner. FLAIR MRI are affected by at least two types of degradations, including additive noise and the partial volume averaging (PVA) artifact, which affect the accuracy of automated algorithms. Model-based methods that rely on Gaussian distributions have been extensively used to handle these two distortions, but are not applicable to FLAIR with WML. The distribution of noise in multicoil FLAIR MRI is non-Gaussian and the presence of WML modifies tissue distributions in a manner that is difficult to model. To this end, the current thesis presents a novel way to model PVA artifacts in the presence of noise. The method is a generalized and adaptive approach, that was applied to a variety of MRI weightings (with and without pathology) for robust PVA quantification and tissue segmentation. No a priori assumptions are needed regarding class distributions and no training samples or initialization parameters are required. Segmentation experiments were completed using simulated and real FLAIR MRI. Simulated images were generated with noise and PVA distortions using realistic brain and pathology models. Real images were obtained from Sunnybrook Health Sciences Centre and WML ground truth was generated through a manual segmentation experiment. The average DSC was found to be 0.99 and 0.83 for simulated and real images, respectively. A lesion load study was performed that examined interhemispheric WML volume for each patient. To show the generalized nature of the approach, the proposed technique was also employed on pathology-free T1 and T2 MRI. Validation studies show the proposed framework is classifying PVA robustly and tissue classes are segmented with good results.