Book chapter
Quantitative Characterization of Trabecular Bone Micro-architecture Using Tensor Scale and Multi-Detector CT Imaging
Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, pp.124-131
Lecture Notes in Computer Science, Springer Berlin Heidelberg
2012
DOI: 10.1007/978-3-642-33415-3_16
PMID: 23285543
Abstract
Osteoporosis, characterized by low bone mineral density (BMD) and micro-architectural deterioration of trabecular bone (TB), increases risk of fractures associated with substantial morbidity, mortality, and financial costs. A quantitative measure of TB micro-architecture with high reproducibility, large between-subjects variability and strong association with bone strength that may be computed via in vivo imaging would be an important indicator of bone quality for clinical trials evaluating fracture risks under different clinical conditions. Previously, the notion of tensor scale (t-scale) was introduced using an ellipsoidal model that yields a unified representation of structure size, orientation and anisotropy. Here, we develop a new 3-D t-scale algorithm for fuzzy objects and investigate its application to compute quantitative measures characterizing TB micro-architecture acquired by in vivo multi-row detector CT (MD-CT) imaging. Specifically, new measures characterizing individual trabeculae on the continuum of a perfect plate and a perfect rod and their orientation are directly computed in a volumetric BMD representation of a TB network. Reproducibility of these measures is evaluated using repeat MD-CT scans and also by comparing their correlation between MD-CT and μ-CT imaging. Experimental results have demonstrated that the t-scale-based TB micro-architectural measures are highly reproducible with strong association of their values at MD-CT and μ-CT resolutions. Results of an experimental mechanical study have proved these measures’ ability to predict TB’s bone strength.
Details
- Title: Subtitle
- Quantitative Characterization of Trabecular Bone Micro-architecture Using Tensor Scale and Multi-Detector CT Imaging
- Creators
- Yinxiao Liu - University of IowaPunam K Saha - University of IowaZiyue Xu
- Resource Type
- Book chapter
- Publication Details
- Medical Image Computing and Computer-Assisted Intervention – MICCAI 2012, pp.124-131
- Publisher
- Springer Berlin Heidelberg; Berlin, Heidelberg
- Series
- Lecture Notes in Computer Science
- DOI
- 10.1007/978-3-642-33415-3_16
- PMID
- 23285543
- eISSN
- 1611-3349
- ISSN
- 0302-9743
- Language
- English
- Date published
- 2012
- Academic Unit
- Electrical and Computer Engineering; Radiology
- Record Identifier
- 9984197075602771
Metrics
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