The USC Andrew and Erna Viterbi School of Engineering USC Signal and Image Processing Institute USC Ming Hsieh Department of Electrical Engineering University of Southern California

Technical Report USC-SIPI-427


by Chitresh Bhushan

May 2016

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 invivo 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 time in comparison to the reversed‐gradient method.

We also developed a robust image co‐registration technique for T₁‐ and T₂‐weighted brain images which exploits the known inverted contrast relationships between these images to normalize the contrast differences. This effectively transforms the inter‐modal image registration problem to an intra‐modal problem which can leverage well‐behaved similarity measures such as the sum of squared differences and are substantially easier to solve. We use the contrast normalization technique for rigid alignment of T₁‐weighted anatomical and diffusion images as well as for distortion correction of the diffusion images in the absence of a field inhomogeneity map by formulating a non‐rigid image registration problem with physics‐based constraints. Our contrast normalization approach demonstrates superior performance and lower computation time as compared to mutual information based methods and other software tools.

Functional MRI (fMRI) is very useful in gaining insight into neuronal activity in the brain, however, data are usually corrupted by noise and unwanted physiological signals. Analysis of such data typically requires smoothing for improved sensitivity and visualization of the functional activity. We present a new filtering approach for fMRI that is based on the concept of non‐local means filtering and leverages the temporal similarity in the functional data to adaptively weight the smoothing kernel. This reduces the local intensity fluctuations by averaging across points which have similar time course without the spatial blurring that occurs with linear filtering methods and enables direct visualization of the dynamic brain activity. Our filtering approach also shows significant improvement in the accuracy of function‐based cortical sub‐division from resting fMRI when compared to task‐activated regions obtained with independent task fMRI.

We also developed another approach for brain sub‐division based on microstructural differences, which complements the functional subdivision. We propose to fuse information across multi‐contrast MRI images with appropriate normalization of vector‐valued diffusion information to extract features which can be indicative of microstructural difference and could be used with modern machine learning approaches such as unsupervised clustering to identify cyto‐ or myelo‐architectural subdivisions of the cerebral cortex. The preliminary application of our approach suggests a close correspondence with subdivisions found in several histological studies.

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