Tissue identification and quantification plays a significant role in the study of aging and age-related diseases. For example, the accumulation of fat in the human body and its regional distribution with aging is associated with type 2 diabetes and cardiovascular diseases. Changes in muscle composition are strongly linked to decline of muscle strength, decreased mobility caused by aging, or musculoskeletal disorders. Especially interesting is analysis of longitudinal changes of morphometric descriptors that is significant for studying the aging process and for the diagnosis and prevention of age-related diseases. Medical imaging has emerged as a major tool for estimation of body composition mainly due to being non- invasive and producing multi-dimensional information. Nowadays MRI and CT acquisition is a central component of clinical trials. An abundance of imaging data is collected, but this wealth of information has not been utilized to full extent. Therefore research on image analysis techniques for tissue quantification that are reproducible and can be used on large-scale clinical trials is of particular importance. The technical hypothesis of this work is that quantitative image processing can robustly and accurately segment, register, and fuse body composition data from modern MRI and CT imaging. The central hypothesis of this proposal is that qualitative body composition phenotypes on clinical imaging will differentiate individuals who are healthy versus those who are not. The goal of our work is to provide a foundation for image analysis of the abdomen and lower extremities and to study the relationship between body morphological changes and age-related pathologies. We will build upon recent advances in medical image computing to segment muscle, regional adipose tissue, and bone in clinical CT and MRI scans. We will also develop image registration procedures to achieve intra- and inter-subject correspondence and make efficient use of information provided by multi-modal and multi-temporal imaging data collected in clinical trials (aim 1). After these methods have been developed, we will address the hypothesis that quantitative use of clinical imaging can increase the prognostic accuracy of age-related pathologies (aim2).
Age-related diseases such as type-2 diabetes, cardiovascular diseases, and sarcopenia have become a worldwide epidemic and affect the quality of life of millions. To give a global perspective, roughly 343.8 million people in the world have type-2 diabetes today, and 175 million don't know they have diabetes at all. Metabolic diseases are strongly linked to longitudinal changes in body composition, morphology and function. This project will contribute novel and non-invasive medical image analysis techniques for studying the human body composition and its changes with increasing age to achieve timely prognosis of these pathologies.
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| Boukari, Fatima; Makrogiannis, Sokratis (2016) Joint level-set and spatio-temporal motion detection for cell segmentation. BMC Med Genomics 9 Suppl 2:49 |
| Keni Zheng; Makrogiannis, Sokratis (2016) Bone texture characterization for osteoporosis diagnosis using digital radiography. Conf Proc IEEE Eng Med Biol Soc 2016:1034-1037 |
| Makrogiannis, S; Fishbein, K W; Moore, A Z et al. (2016) Image-Based Tissue Distribution Modeling for Skeletal Muscle Quality Characterization. IEEE Trans Biomed Eng 63:805-13 |
