Visual Knowledge Discovery for Diffusion Kurtosis Datasets of the Human Brain

Classification and visualization of structures in the human brain provide vital information to physicians who examine patients suffering from brain diseases and injuries. In particular, this information is used to recommend treatment to prevent further degeneration of the brain. Diffusion kurtosis imaging (DKI) is a new kind of magnetic resonance imaging rapidly gaining broad interest in the medical imaging community due to its ability to provide intricate details of the underlying microstructural characteristics of the whole brain. DKI produces a fourth-order tensor at every voxel of the imaged volume; unlike traditional diffusion tensor imaging (DTI), DKI measures the non-Gaussian property of water diffusion in biological tissues. It has shown promising results in studies on changes in grey matter and mild traumatic brain injury, a particularly difficult form of TBI to diagnose. In this paper, we use DKI imaging and report our results of the classification and visualization of various tissue types, diseases, and injuries. We evaluate segmentation performed using various clustering algorithms on different segmentation strategies including fusion of diffusion and kurtosis tensors. We compare our result to the well-known MRI segmentation technique based on Magnetization-Prepared Rapid Acquisition with Gradient Echo (MPRAGE) imaging. Sujal Bista Institute for Advanced Computer Studies, University of Maryland, College Park, MD, 20742, USA e-mail: [email protected] Jiachen Zhuo University of Maryland School of Medicine, Baltimore, MD, 21201, USA e-mail: jzhuo@umm.