Periodic Reporting for period 1 - HealinguFE (Development of an in silico model for prediction of in vivo human bone fracture healing using micro-finite element analysis)
Reporting period: 2020-01-01 to 2021-12-31
Bone fractures are a global public health issue and incidence rates continue to increase with our aging society. While most fractures heal without any complications, up to 10% of fractures exhibit delayed healing, referred to as non-union. These cases often lead to expensive and painful secondary interventions. Current knowledge of fracture healing is based on experimental studies using animal models and focuses on the midshaft of long bones (i.e. femur diaphysis), which is made up of a dense shell of cortical bone filled with bone marrow. However, the majority of bone fractures occur in the distal segment long bones, known as the metaphysis, or within the vertebral body, both made up of a marrow-filled network of trabecular bone and a thin cortical shell. The bone microstructure in these locations is vastly different, heavily impacting the nature of bone fracture healing in these regions.
Bone is a mechanosensetive tissue, meaning it responds to loading. Local mechanical tissue loading is a major regulator of the cellular activities in the healing process and might provide a unique therapeutic target for addressing non-unions. However, existing tools for the assessment of bone fracture healing lack the resolution to identify and isolate the initial fracture sites, especially if these occur in the metaphysis. Further, these tools are unable to provide detailed information on healing progression at the microstructural level. With the advent of high-resolution peripheral quantitative computed tomography (HR-pQCT), it is now possible to assess changes in microstructural and biomechanical properties in vivo. Further, medical image-based computational modeling (i.e. micro-finite element analysis (µFE)) has been shown to be a powerful, non-invasive tool for monitoring bone fracture and predicting the local bone remodelling response in both animal and human studies. Although such in silico approaches have become a popular laboratory method for predicting bone remodelling and fracture risk, it has yet to be validated for healing bone in humans. Such analyses could serve as a new tool for fracture assessment and may provide quantitative guidance for clinicians.
The aim of the EU-funded HealinguFE project was to develop a tool for identifying metaphyseal fractures and predicting their healing evolution. Using HR-pQCT data from patients with wrist fractures, scientists sought to develop an in silico model capable of detecting structural changes in the bone over time in order to advance existing knowledge on the mechanisms underlying trabecular bone healing and fuel future research towards novel interventions. During the project, a series of image processing tools and a robust µFE pipeline for assessing time-lapsed fracture healing in vivo were developed. Using these tools, drastically delayed fracture healing was observed in older patients and the µFE-derived mechanics proved to be a promising metric for tracking fracture healing in individual patients.
Bone is a mechanosensetive tissue, meaning it responds to loading. Local mechanical tissue loading is a major regulator of the cellular activities in the healing process and might provide a unique therapeutic target for addressing non-unions. However, existing tools for the assessment of bone fracture healing lack the resolution to identify and isolate the initial fracture sites, especially if these occur in the metaphysis. Further, these tools are unable to provide detailed information on healing progression at the microstructural level. With the advent of high-resolution peripheral quantitative computed tomography (HR-pQCT), it is now possible to assess changes in microstructural and biomechanical properties in vivo. Further, medical image-based computational modeling (i.e. micro-finite element analysis (µFE)) has been shown to be a powerful, non-invasive tool for monitoring bone fracture and predicting the local bone remodelling response in both animal and human studies. Although such in silico approaches have become a popular laboratory method for predicting bone remodelling and fracture risk, it has yet to be validated for healing bone in humans. Such analyses could serve as a new tool for fracture assessment and may provide quantitative guidance for clinicians.
The aim of the EU-funded HealinguFE project was to develop a tool for identifying metaphyseal fractures and predicting their healing evolution. Using HR-pQCT data from patients with wrist fractures, scientists sought to develop an in silico model capable of detecting structural changes in the bone over time in order to advance existing knowledge on the mechanisms underlying trabecular bone healing and fuel future research towards novel interventions. During the project, a series of image processing tools and a robust µFE pipeline for assessing time-lapsed fracture healing in vivo were developed. Using these tools, drastically delayed fracture healing was observed in older patients and the µFE-derived mechanics proved to be a promising metric for tracking fracture healing in individual patients.
These tools included a novel, automated image segmentation approach capable of isolating the fractured radius from surrounding tissue and an algorithm for estimating the in vivo loading on the radius to better characterize the mechanical environment for our in silico models. A pipeline for the analysis of local bone remodeling during fracture healing was developed and applied to our patient data set. Using the pipeline, elevated tissue dynamics were found well beyond the traditional fracture follow-up period of 6-12 weeks. The tools noted here have been published in interdisciplinary, peer-reviewed journals targeting biomedical engineers, clinicians, and industry partners interested in all aspects of bone and musculoskeletal research.
To address inconsistencies in the literature regarding HR-pQCT image processing and analysis, I partnered with colleagues at ETH Zurich to research and publish a comprehensive review on existing methods, highlighting the need for improved standardization across the field.
This MSCA Individual Fellowship provided me with the opportunity to step into the role of team lead at ETH Zurich. I managed a team of three researchers in the field of Clinical Mechanobiology. Our goal was to integrate non-invasive imaging methods with experimental and computational mechanics in order to develop clinically applicable tools to monitor bone integrity, fracture risk, and fracture healing in patients.
Results from HealinguFE were presented at over 5 international conferences. Additionally, our work was incorporated into a interdisciplinary course designed to enhance students’ understanding of imaging fundamentals and computational methods in medicine. An interactive lab tour at Scientifica 2021, gave our team the opportunity to share our research with the public. Our tour, titled Building Better Bones, gave participants a chance to simulate the effect of exercise as well as various medical treatments on bone.
To address inconsistencies in the literature regarding HR-pQCT image processing and analysis, I partnered with colleagues at ETH Zurich to research and publish a comprehensive review on existing methods, highlighting the need for improved standardization across the field.
This MSCA Individual Fellowship provided me with the opportunity to step into the role of team lead at ETH Zurich. I managed a team of three researchers in the field of Clinical Mechanobiology. Our goal was to integrate non-invasive imaging methods with experimental and computational mechanics in order to develop clinically applicable tools to monitor bone integrity, fracture risk, and fracture healing in patients.
Results from HealinguFE were presented at over 5 international conferences. Additionally, our work was incorporated into a interdisciplinary course designed to enhance students’ understanding of imaging fundamentals and computational methods in medicine. An interactive lab tour at Scientifica 2021, gave our team the opportunity to share our research with the public. Our tour, titled Building Better Bones, gave participants a chance to simulate the effect of exercise as well as various medical treatments on bone.
I served as a pivotal member of a research team on a project investigating local remodeling and mechanoregulation of bone fracture healing in healthy, aged, and osteoporotic humans. I also partnered with researchers across Europe looking into diabetes in bone (MSCA- ITN, FIDELIO-860898). As a result of these partnerships, additional publications within the theme of bone fragility are in preparation. Three original articles are in preparation, one on the use of mechanoregulation analysis for assessing the impact of therapeutic interventions on bone, one on the development of an automated machine learning tool for assessing image quality that is capable of being integrated into clinical protocols, and one summarizing the culmination of my fellowship work detailing the mechanoregulation of healing bone in patients. Additionally, a systemic review and meta-analysis on diabetes in bone and a systematic review on model parameterization strategies for in silico models of bone remodelling are in preparation.
As a direct result of the HealinguFE project, I was able to connect and build strong relationships with clinicians across the globe who are seeking new strategies for long term patient care for bone frailty. The tools developed during my project are in the process of being applied to new image datasets, reducing the time burden and computational costs incurred by clinical researchers. Additionally, the code developed for these tools was written using open-source software in an effort to increase accessibility.
In the final year of my fellowship I was offered and accepted an Assistant Professor position at Virginia Tech, achieving my long-term goal of becoming an independent researcher. Here I will start my own research group in the field of hard tissue biomechanics and computational mechanobiology. I plan to maintain expertise in experimental and computational biomechanics, but also collaborate with experts in tissue engineering. Through this new position I will have the opportunity to teach and mentor students in translational research, continuing to provide clinicians with new diagnostic and bone health monitoring tools.
As a direct result of the HealinguFE project, I was able to connect and build strong relationships with clinicians across the globe who are seeking new strategies for long term patient care for bone frailty. The tools developed during my project are in the process of being applied to new image datasets, reducing the time burden and computational costs incurred by clinical researchers. Additionally, the code developed for these tools was written using open-source software in an effort to increase accessibility.
In the final year of my fellowship I was offered and accepted an Assistant Professor position at Virginia Tech, achieving my long-term goal of becoming an independent researcher. Here I will start my own research group in the field of hard tissue biomechanics and computational mechanobiology. I plan to maintain expertise in experimental and computational biomechanics, but also collaborate with experts in tissue engineering. Through this new position I will have the opportunity to teach and mentor students in translational research, continuing to provide clinicians with new diagnostic and bone health monitoring tools.