Abstract
The aim of this review is to present the different stages, from geometrical to mechanical data, in the development of sensitive and non-invasive MRI techniques for describing alterations in material properties of cartilage and intervertebral disc tissue with disease. The first section summarizes some MRI techniques used to quantify the geometrical changes in cartilage and intervertebral disc tissue that appears with diurnal activity, specific movements or loadings, or else with various diseases. The second section describes the MRI techniques used to evaluate the biochemical composition of cartilage and intervertebral disc tissue, mainly the glycosaminoglycans (GAGs) and water contents, and the collagen organization. The third section describes the MRI techniques used to quantify the mechanical and structural properties of bone tissue. The last section presents the new MRI techniques used to evaluate the mechanical properties of hydrated cartilage and intervertebral disc tissue. More studies highlighting the ability of MRI to describe the fluid phase behavior within the cartilage or intervertebral discs tissue should be perform. The long term goal would be to develop sensitive and non-invasive clinical techniques for the early detection of the changes in the mechanical properties of both solid and fluid phases that appear with degeneration.
Keywords: Material properties, Quantitative MRI, Cartilage, Intervertebral disc
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
3