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In-vivo Quantification of Bone Remodeling in Fracture Healing
Registered time-lapse in-vivo images of fractures of the radius and scaphoid can be used to quantify structural changes during the fracture healing process. By combining this data with clinical imaging and blood-based biomarkers, we hope to identify the specific metrics relevant to fracture healing.
Keywords: Fracture healing, in-vivo imaging, micro CT, personalized medicine
Clinically, fracture healing is assessed through either sequential x-rays or computed tomography (CT) images. Of these, only CT data captures the 3D environment. While traditional CT does not provide the fidelity necessary to quantify microstructural changes, high resolution peripheral quantitative CT (HR-pQCT) offers a unique platform to evaluate bone health at peripheral joints, such as the human hand and wrist. Several studies have shown the HR-pQCT data can be used to evaluate bone remodeling over time, revealing markers that indicate a degradation in bone integrity due to aging or disease. However, few studies to date have acquired the data necessary to investigate bone remodeling during fracture healing at the microstructural level. We have acquired one of the most extensive sets of patient specific remodeling and healing data for patients with radius and scaphoid fractures, two common fractures that often result in long-term pain and reduced quality of life. Specifically, at six time points during the first year after fracture, we have collected HR-pQCT, dual energy x-ray absorptiometry, and x-ray images, as well as blood samples. Combined, this provides us the comprehensive dataset necessary to elucidate the causes of fracture, nonunion, or delayed union.
By registering these HR-pQCT images over time, we can identify regions of bone resorption and growth, revealing remodeling patterns that may provide insight into delayed healing or nonunion. This data can then be used to quantify loading across the fracture region using finite element analysis. Combined with the quantification of bone density from DXA and the presence of bone biomarkers from blood samples, this provides a complete description of patient health and well-being relevant to bone remodeling and fracture healing.
Clinically, fracture healing is assessed through either sequential x-rays or computed tomography (CT) images. Of these, only CT data captures the 3D environment. While traditional CT does not provide the fidelity necessary to quantify microstructural changes, high resolution peripheral quantitative CT (HR-pQCT) offers a unique platform to evaluate bone health at peripheral joints, such as the human hand and wrist. Several studies have shown the HR-pQCT data can be used to evaluate bone remodeling over time, revealing markers that indicate a degradation in bone integrity due to aging or disease. However, few studies to date have acquired the data necessary to investigate bone remodeling during fracture healing at the microstructural level. We have acquired one of the most extensive sets of patient specific remodeling and healing data for patients with radius and scaphoid fractures, two common fractures that often result in long-term pain and reduced quality of life. Specifically, at six time points during the first year after fracture, we have collected HR-pQCT, dual energy x-ray absorptiometry, and x-ray images, as well as blood samples. Combined, this provides us the comprehensive dataset necessary to elucidate the causes of fracture, nonunion, or delayed union. By registering these HR-pQCT images over time, we can identify regions of bone resorption and growth, revealing remodeling patterns that may provide insight into delayed healing or nonunion. This data can then be used to quantify loading across the fracture region using finite element analysis. Combined with the quantification of bone density from DXA and the presence of bone biomarkers from blood samples, this provides a complete description of patient health and well-being relevant to bone remodeling and fracture healing.
To quantify fractured bone remodeling from time-lapse in-vivo images using custom python scripts and statistically analyze the clinical metrics relevant to fracture healing.
To quantify fractured bone remodeling from time-lapse in-vivo images using custom python scripts and statistically analyze the clinical metrics relevant to fracture healing.
Penny Atkins (penny.atkins@hest.ethz.ch), Institute for Biomechanics, ETH Zurich, Professorship Ralph Müller
Penny Atkins (penny.atkins@hest.ethz.ch), Institute for Biomechanics, ETH Zurich, Professorship Ralph Müller