The identification and characterization of regional body tissues is essential to understand changes that occur with aging and age-related metabolic diseases such as diabetes and obesity and how these diseases affect trajectories of health and functional status. Imaging technologies are frequently used to derive volumetric, area, and density measurements of different tissues. Despite the significance and direct applicability of automated tissue quantification and characterization techniques, these topics have remained relatively underexplored in the medical image analysis literature. We present a method for identification and characterization of muscle and adipose tissue in the midthigh region using MRI. We propose an image-based muscle quality prediction technique that estimates tissue-specific probability density models and their eigenstructures in the joint domain of water- and fat-suppressed voxel signal intensities along with volumetric and intensity-based tissue characteristics computed during the quantification stage. We evaluated the predictive capability of our approach against reference biomechanical muscle quality (MQ) measurements using statistical tests and classification performance experiments. The reference standard for MQ is defined as the ratio of muscle strength to muscle mass. The results show promise for the development of noninvasive image-based MQ descriptors.
- Magnetic resonance imaging (MRI)
- Probabilistic modeling
- Tissue identification and characterization
ASJC Scopus subject areas
- Biomedical Engineering