Abstract
Diffusion tensor imaging (DTI) provides information on tissue microstructure that is different from conventional T1 and T2-weighted MRI. Although, the value of DTI is likely to be greatest for assessing white matter degeneration in Alzheimers disease (AD), cross-sectional DTI studies consistently show increased diffusivity in gray matter regions that are typically involved with the neurodegenerative pathology in AD such as the medial temporal lobes and temporoparietal association cortices. The white matter tracts that connect these gray matter regions such as the limbic pathways, long association fibers of inferior and superior longitudinal fasciculi also show elevated diffusivity and decreased directionality of diffusivity. Although the pathological underpinnings of DTI abnormalities in AD are yet unclear, DTI changes in AD are thought to represent disruption of myelin and axons in the white matter and neuronal cell bodies in the gray matter.
Keywords: Diffusion tensor imaging(DTI), Alzheimer's disease, dementia, mild cognitive impairment, mean diffusivity, fractional anisotropy, mild cognitive impairmet, CSF, gray matter
Call for Papers in Thematic Issues
Intelligent Information Retrieval for Multispectral and Hyperspectral Imaging
Multispectral and hyperspectral imaging is an interdisciplinary research area, widely focuses on spectral, spatial, and temporal data. It offers a plethora of opportunities to efficiently analyze the vast area of the earth's surface. However, there are numerous factors such as dimensionality and size of the hyperspectral data, lesser training samples, ...read more
Machine and Deep Learning in Medical Image Diagnosis
Medical Image Processing regards a set of methodologies that have been developed over recent years with the purpose of improving medical image quality, improving medical data visualization, understanding, and assisting medical diagnosis, and so on. The application of machine and deep learning methods in sensing and imaging can potentially have ...read more
Quantum Machine Learning for Medical Data and Imaging
Quantum computing has promised a significant speedup in certain computationally intensive tasks that are intractable on classical computers. Researchers in quantum machine learning, such as those working in computer vision, image processing, biomedical analysis, and related topics, may play an important role in comprehending and working on complicated medical data, ...read more
21