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Hight-to-low Resolution Image Registration for investigation of Multiscale Osteocyte Lacunar Properties in Osteoporosis
The project aims at investigating the influence of osteoporosis and the effect of different pharmacological treatments and mechanical loading on osteocyte lacunar properties and void spaces in vertebral bone in 3D.
This can be achieved by registering high (1.2 µm) to low (10.5 µm) resolution microCT images and running morphological analyses.
Osteoporosis is the most common musculoskeletal disease occurring during aging and is characterized by a reduction in bone strength and increased risk of fracture due to low bone mass and impaired bone microarchitecture. [1] The main therapeutic interventions for osteoporosis are antiresorptive (BIS, SERM, Denosumab, Estrogen) and anabolic drugs (PTH). A change of lifestyle, such as to quit smoking or increase physical exercise, can be supportive of the above mentioned pharmacological interventions.
Osteocytes are the most abundant cells in bone tissue and are involved in mechanosensing and mechanotransduction through their lacunocanalicular network (LCN). During aging and the onset of osteoporosis, the LCN is of particular interest due to its role in bone remodeling and adaptation to mechanical load [2]. Multiple studies have investigated lacunar density in the aging human population [3-8] and in osteoporotic patients [4,9-11] using histomorphometric techniques, however, these techniques don’t allow for an accurate 3D representation of the whole LCN.
MicroCT can be considered the gold standard in hierarchical 3D imaging of bone structure and function [12] and delivers the key images relevant in this project of assessing the influence of osteoporosis and the effect of different treatments on osteocyte lacunar properties.
The 3D-3D registration of high resolution (1.2 µm) to low resolution (10.5 µm) microCT images (see image, courtesy of Zihui, 2016) of the same murine vertebra samples gives insight into the hierar-chical and structural organization of trabecular bone.
References:
1. Sambrook P,et al. 2006. doi:10.1016/s0140-6736(06)68891-0
2. Miyauchi A, et al. J Biol Chem. 2000. doi:10.1074/jbc.275.5.3335
3. Mori S, et al. Bone. 1997. doi:10.1016/s8756-3282(97)00200-7
4. Mullender MG, et al. Bone. 1996. doi:10.1016/8756-3282(95)00444-0
5. Qiu S, et al. Bone. 2002. doi:10.1016/s8756-3282(02)00819-0
6. Torres-Lagares D, et al. J Craniomaxillofac Surg. 2010. doi:10.1016/j.jcms.2009.07.012
7. Vashishth D, et al. Anat Rec A Discov Mol Cell Evol Biol. 2005. doi:10.1002/ar.a.20146
8. Vashishth D, et al. Bone. 2000. doi:10.1016/s8756-3282(00)00236-2
9. Soicher MA, et al. Bone. 2011. doi:10.1016/j.bone.2010.11.009
10. Mullender MG, et al. Calcif Tissue Int. 2005. doi:10.1007/s00223-005-0043-6
11. Qiu S, et al. J Bone Miner Res. 2003. doi:10.1359/jbmr.2003.18.9.1657
12. Müller R. Nat Rev Rheumatol. 2009. doi:10.1038/nrrheum.2009.107
Osteoporosis is the most common musculoskeletal disease occurring during aging and is characterized by a reduction in bone strength and increased risk of fracture due to low bone mass and impaired bone microarchitecture. [1] The main therapeutic interventions for osteoporosis are antiresorptive (BIS, SERM, Denosumab, Estrogen) and anabolic drugs (PTH). A change of lifestyle, such as to quit smoking or increase physical exercise, can be supportive of the above mentioned pharmacological interventions.
Osteocytes are the most abundant cells in bone tissue and are involved in mechanosensing and mechanotransduction through their lacunocanalicular network (LCN). During aging and the onset of osteoporosis, the LCN is of particular interest due to its role in bone remodeling and adaptation to mechanical load [2]. Multiple studies have investigated lacunar density in the aging human population [3-8] and in osteoporotic patients [4,9-11] using histomorphometric techniques, however, these techniques don’t allow for an accurate 3D representation of the whole LCN.
MicroCT can be considered the gold standard in hierarchical 3D imaging of bone structure and function [12] and delivers the key images relevant in this project of assessing the influence of osteoporosis and the effect of different treatments on osteocyte lacunar properties.
The 3D-3D registration of high resolution (1.2 µm) to low resolution (10.5 µm) microCT images (see image, courtesy of Zihui, 2016) of the same murine vertebra samples gives insight into the hierar-chical and structural organization of trabecular bone.
References: 1. Sambrook P,et al. 2006. doi:10.1016/s0140-6736(06)68891-0 2. Miyauchi A, et al. J Biol Chem. 2000. doi:10.1074/jbc.275.5.3335 3. Mori S, et al. Bone. 1997. doi:10.1016/s8756-3282(97)00200-7 4. Mullender MG, et al. Bone. 1996. doi:10.1016/8756-3282(95)00444-0 5. Qiu S, et al. Bone. 2002. doi:10.1016/s8756-3282(02)00819-0 6. Torres-Lagares D, et al. J Craniomaxillofac Surg. 2010. doi:10.1016/j.jcms.2009.07.012 7. Vashishth D, et al. Anat Rec A Discov Mol Cell Evol Biol. 2005. doi:10.1002/ar.a.20146 8. Vashishth D, et al. Bone. 2000. doi:10.1016/s8756-3282(00)00236-2 9. Soicher MA, et al. Bone. 2011. doi:10.1016/j.bone.2010.11.009 10. Mullender MG, et al. Calcif Tissue Int. 2005. doi:10.1007/s00223-005-0043-6 11. Qiu S, et al. J Bone Miner Res. 2003. doi:10.1359/jbmr.2003.18.9.1657 12. Müller R. Nat Rev Rheumatol. 2009. doi:10.1038/nrrheum.2009.107
Developing a framework (Python) for the successful registration of high to low resolution microCT images of murine vertebras for 3D morphometric and hierarchical analysis of osteocyte lacunar properties (IPL/Windows).
Previous knowledge of python programming and advanced image analysis are of advantage.
The project goals will be tailored to the student’s interests, expertise, and project requirements.
Developing a framework (Python) for the successful registration of high to low resolution microCT images of murine vertebras for 3D morphometric and hierarchical analysis of osteocyte lacunar properties (IPL/Windows). Previous knowledge of python programming and advanced image analysis are of advantage. The project goals will be tailored to the student’s interests, expertise, and project requirements.
Sara Lindenmann, doctoral student (e-mail: sara.lindenmann@hest.ethz.ch), ETH Zürich / Laboratory for Bone Biomechanics (https://www.bone.ethz.ch/)
For application, please provide your CV and transcripts of B.Sc. and M.Sc.
Sara Lindenmann, doctoral student (e-mail: sara.lindenmann@hest.ethz.ch), ETH Zürich / Laboratory for Bone Biomechanics (https://www.bone.ethz.ch/) For application, please provide your CV and transcripts of B.Sc. and M.Sc.