Abstract
In vivo magnetic resonance spectroscopy is a powerful tool which can be applied non-invasively to monitor disease progression at a molecular level in the brain of human patients with Alzheimers disease (AD) and transgenic mouse models of AD. Application of magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) in transgenic mouse models of AD makes it possible to follow metabolic changes and structural changes at the same time longitudinally. This review focuses on in vivo MRS studies in transgenic mouse models of AD and highlights the future impact of MRS techniques in finding crucial biomarkers of AD.
Keywords: Alzheimer's Disease, magnetic resonance spectroscopy, transgenic mouse models, N-acetylaspartate NAA, APP, APP/PS1
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
25