Abstract
The presence of amyloid plaques in the brain is one of prominent histological features of Alzheimers disease. However, imaging of amyloid plaques in vivo in humans is not yet feasible. Recent technical advances in high resolution magnetic resonance imaging (MRI) methods in mice have enabled not only the detection of amyloid plaques in vivo but also allowed to monitor the development of these plaques with age in the same animals. Further technical improvements to visualize small plaques at early stages at ultrahigh magnetic fields are underway. This review summarizes some of the current developments in visualizing AD plaques in vivo in mice and highlights their future impact on drug discovery. Furthermore potential application and challenges of translation of these methods for visualizing plaques in human are discussed.
Keywords: Magnetic resonance imaging (MRI), Alzheimer's disease (AD), amyloid plaque imaging, longitudinal study, transgenic mouse
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
26