1. Novel magnetic resonance technique for characterizing mesoscale structure of trabecular bone
- Author
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Kristin M. James, Chantal Nguyen, Jean M. Carlson, Koichi Masuda, Kimberly J. Schlesinger, Robert L. Sah, and Timothy W. James
- Subjects
0301 basic medicine ,Aging ,Computer science ,Osteoporosis ,030209 endocrinology & metabolism ,Bioengineering ,histomorphometry ,magnetic resonance ,03 medical and health sciences ,0302 clinical medicine ,medicine ,Magnetic resonance technique ,lcsh:Science ,Multidisciplinary ,medicine.diagnostic_test ,Magnetic resonance imaging ,medicine.disease ,Mr imaging ,osteoporosis ,Trabecular bone ,030104 developmental biology ,trabecular bone ,Musculoskeletal ,Metric (mathematics) ,Osteoporotic bone ,Biomedical Imaging ,lcsh:Q ,Tomography ,Biomedical engineering - Abstract
Osteoporosis, characterized by increased fracture risk and bone fragility, impacts millions of adults worldwide, but effective, non-invasive and easily accessible diagnostic tests of the disease remain elusive. We present a magnetic resonance (MR) technique that overcomes the motion limitations of traditional MR imaging to acquire high-resolution frequency-domain data to characterize the texture of biological tissues. This technique does not involve obtaining full two-dimensional or three-dimensional images, but can probe scales down to the order of 40 μm and in particular uncover structural information in trabecular bone. Using micro-computed tomography data of vertebral trabecular bone, we computationally validate this MR technique by simulating MR measurements of a ‘ratio metric’ determined from a few k -space values corresponding to trabecular thickness and spacing. We train a support vector machine classifier on ratio metric values determined from healthy and simulated osteoporotic bone data, which we use to accurately classify osteoporotic bone.
- Published
- 2018