Algorithm Development for Magnetic Resonance Images (MRI)

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The visual analysis of medical images is subjective (observer-dependent), qualitative, error prone, and is inefficient, which ultimately effects diagnostic accuracy and the ability to conduct large research studies. Image analysis techniques offer a better alternative since they automatically measure image properties in a quantitative, objective, efficient, and reproducible manner. The purpose of this program is to advance the state-of-the-art in image analysis algorithms that operate only on Fluid-Attenuation Inversion Recovery (FLAIR) Magnetic Resonance Images (MRI) to quantify neurological disease in a novel and efficient way. Although FLAIR MRI are now the preferred modality for neurodegenerative disease detection and analysis, only few image processing methods exist and novel image processing algorithms are much needed. The objectives of this research proposal surround the development of 1) segmentation algorithms for neuroanatomy and pathology in FLAIR MRI in order to extract features from tissues such as volume, texture and shape, and 2) registration of FLAIR MRI to atlases in order to analyze brain images in a spatially normalized space, to study progression of disease in longitudinal data (i.e. change in lesion volume), and also to identify important anatomical landmarks. Considering the neurological disease prevalence, aging demographics, as well as associated economic burden on the Canadian healthcare system, such a program has the potential to positively impact many Canadians.