Correction , Coregistration and Connectivity Analysis of Multi - Contrast Brain Mri

Magnetic Resonance Imaging (MRI) is a versatile imaging technique that allows probing of the various properties of the soft-tissue within the body of living organisms and has been widely used for diagnostic as well as research purposes. MRI has been very useful for studying the brain as it provides an exceptional ability to study structural, functional and dynamic properties in a non-invasive fashion. For example, diffusion MRI allows imaging of the microstructural details of soft-tissue by probing the diffusion of water in tissue, while functional MRI allows the study of neuronal activity by probing blood oxygenation levels as the subject performs a task or function. The availability of diverse in vivo image contrasts facilitates the study of the brain by fusing information across multiple MRI images with different contrast mechanisms. However, analysis with multi-contrast MRI poses image and signal processing challenges. Different MRI sequences are required to obtain images with different tissue contrasts, which unfortunately also results in artifacts that are unique to the MRI sequence used. A reliable analysis of multi-contrast images requires the correction of image artifacts, co-registration of the images to establish a one-to-one mapping between voxels, use of appropriate models, filtering, and normalization techniques to extract meaningful parameters from these data. In this dissertation, we present and validate novel approaches and methods to address some of the challenges associated with multi-contrast MRI image analysis. Diffusion MRI frequently makes use of echo planar imaging (EPI) for fast acquisition. EPI is very sensitive to inhomogeneity in the main magnetic field. These inhomogeneities are particularly pronounced at air-tissue interfaces where there are large changes in magnetic susceptibility and can cause severe local geometric distortion in the reconstructed EPI images. We present a new method for the distortion correction which uses an interlaced phase encoding scheme that exploits the dependency of distortion on phase encoding direction to obtain diffusion weighted images with differing distortion patterns but without repetitive acquisition of the same diffusion weighted images, as done in the state-of-the-art reversed-gradient method. The distorted diffusion-weighted images are corrected in a novel constrained joint reconstruction formulation which leverages the prior knowledge about the smoothness of diffusion processes and spatial smoothness of the images. Our approach demonstrates high-quality correction of susceptibility-induced geometric distortion artifacts in diffusion MRI images and requires half the scan