Award Partner: Torus Biomedical Solutions Inc.

Background & Motivation of the Project:
Precise positioning and alignment of implants and surgical instruments are essential for successful outcomes in orthopedic surgery. This is especially true for minimally invasive procedures, which typically rely on mobile C-arm fluoroscopy for continuous, low-dose real-time assessment throughout the operation. While this imaging technique is popular for its versatility and real-time visualization, it has inherent limitations. These include a small field of view, difficulty in using 2D X-rays to assess complex 3D geometries, parallax and image distortion, and the absence of local scaling parameters—all of which can lead to inaccuracies in fluoroscopy-based assessments. Additionally, these techniques often require capturing the operative anatomy from specific angles through a trial-and-error process, which is highly sensitive to small changes in the position and orientation of the image intensifier, as well as the patient’s positioning and joint angles on the surgical table. Many of these variables are challenging, if not impossible, to control in the fast-paced surgical environment, leading to assessment inaccuracies. Ultimately, these challenges can compromise surgical outcomes, increase the risk of complications or disabilities, and may even result in costly repeat surgeries.

 

To address these challenges, Torus Biomedical is pioneering an innovative solution called the ConfirMap system. This system integrates seamlessly with the existing mobile fluoroscopy equipment in the operating room and uses advanced algorithms to generate bi-planar long radiographic views and measurements of the operative anatomy. By reconciling bi-planar views of the operative anatomy, the system can estimate the 3D pose and orientation of the anatomies in an approach that is least sensitive to non-idealities of the conventional fluoroscopy-based assessments. In collaboration with a leading spine company in the U.S., a version of the ConfirMap technology tailored for spinal surgery has recently been introduced to the market. Currently, the company is focused on incorporating an AI-enabled automated measurement layer to meet the requirements for use during orthopedic trauma and joint arthroplasty procedures. To support the development of this technology and benchmark its performance against alternative techniques, it’s essential to quantify the impact of intraoperative parameters on specific radiographic outcomes.

 

Project Objectives:
The proposed research will use computer simulations to study how different imaging and patient positioning factors affect measurements during various stages of minimally invasive hip replacement surgery. We will simulate hip replacement surgery on a group of virtual patients, modeled from CT scans, in a virtual environment. This will allow us to recreate surgical scenarios and generate digitally reconstructed radiographs (DRRs), while accounting for expected variations in the positioning of the imaging equipment, patient posture, and joint angles. As the first objective of the the project, we will use simulated radiographs and ground truth data to assess the accuracy and precision of conventional 2D radiographic measurements for key surgical parameters in total hip arthroplasty, such as cup alignment, leg length, and offset. As the second objective, we will perform the same analysis to evaluate the performance of the ConfirMap system, comparing it to ground truth data from CT scans. We hypothesize that various intraoperative factors can significantly reduce the accuracy and precision of conventional radiographic methods, making them unreliable during hip replacement surgery. In contrast, measuring the performance of the ConfirMap system could demonstrate its potential as a more precise and reliable alternative.

 

Opportunities for Skill/Career Development:
Through this project, the intern will develop a comprehensive skill set in medical imaging techniques, orthopedic biomechanics, and computer-assisted radiology and surgery. Working closely with the company’s engineering team, clinical collaborators, and academic co-applicants with subject matter expertise in orthopedic medical devices, medical image analysis, and computer vision, the intern will receive valuable mentorship and guidance throughout the project. The intern will gain hands-on experience, from conducting literature reviews to simulating various stages of hip surgery using computer tools. This immersive experience will enhance critical thinking, problem-solving, and collaboration skills as the intern addresses complex technical challenges. Additionally, participating in academic publications resulting from the project will boost the intern’s career development and academic credibility in the field. Overall, this internship offers a unique opportunity to expand knowledge, skills, and professional connections in a dynamic and impactful research environment.

 

Value for the Company:
This project will assist the company in advancing its core technology by testing, improving, and validating its performance in direct comparison with state-of-the-art methods. The results of the research will significantly aid the company in highlighting the shortcomings of existing techniques while demonstrating the value of its system to clinical users, strategic partners, and stakeholders. Furthermore, the outcomes of the research will be submitted to peer-reviewed scientific and clinical journals for broader dissemination and validation within the research community.

Achieving precise positioning and alignment of anatomical structures and surgical instruments is paramount for successful outcomes in orthopedic surgery. The current standard practice heavily relies on intraoperative fluoroscopy (low dose real-time X-ray imaging) to assess the position and alignment of operative anatomy. However, these methods are qualitative and prone to errors and poor repeatability due to variabilities in positioning and orienting the imaging equipment, leading to suboptimal patient outcomes and increased risk of disabilities, often necessitating repeat surgeries. To address these challenges, Torus Biomedical has pioneered a unique solution known as ConfirMap system. This system seamlessly integrates with existing mobile fluoroscopy equipment in operating rooms and employs innovative calibration, tracking, and image processing techniques to provide extended radiographic visualization and precise measurements. Through strategic collaboration with a leading spine company in the US, a specialized version of the ConfirMap technology tailored for spinal surgery is slated to launch in the US market by Q2 2024. Currently, the company is dedicated to enhancing its technology to meet the specific demands of the orthopedic trauma field, particularly in femoral and tibial bone fracture reduction surgeries. This initiative aims to tackle critical technical challenges, thereby enhancing the viability of the ConfirMap system for orthopedic trauma applications, especially for procedures involving long bone fractures.

 

To ensure the effectiveness of the ConfirMap system in the ortho-trauma market, automating image analysis and measurement reporting with minimal surgical staff intervention is paramount. Our primary focus lies in procedures involving subtrochanteric and midshaft femoral fractures, where assessing the length, alignment, and rotation of the fractured bone poses significant challenges. Our project aims to leverage machine learning and computer vision techniques to develop and train a convolutional neural network (CNN) specifically tailored for the automatic segmentation of key bone landmarks based on the image format generated by the ConfirMap system. The outcome will streamline quick access to critical radiographic measurements during surgery, promising to enhance surgical efficiency and precision in ortho-trauma cases. The post-doctoral intern assigned to this project will play a pivotal role in developing a new machine learning model for the solution. As the initial step, they will establish a pipeline for creating realistic synthetic fluoroscopic images and corresponding labeling metadata from Computed Tomography (CT) images. Subsequently, they will construct, train, and optimize a deep neural network model, drawing inspiration from architecture layers proposed in academic literature. An important advancement of this research in the clinical application of the ConfirMap product will be the incorporation of multiple fluoroscopic images along with their spatial coordinates as input to the model.

 

Through this project the intern will have the opportunity to cultivate a comprehensive skill set encompassing research and development in medical imaging and machine learning. Collaborating with the company’s engineering team and academic co-applicants, esteemed authorities in medical image analysis and computer vision, the intern will benefit from invaluable mentorship, supervision, and guidance across all project stages. From conducting thorough literature reviews to designing and developing the neural network model from inception, the intern will gain hands-on experience in applying cutting-edge techniques to an innovative surgical imaging solution. This immersive experience will nurture the development of critical thinking, problem-solving, and collaboration skills as the intern tackles complex technical challenges. Furthermore, the intern’s participation in academic publications resulting from the project will not only advance their career development but also enhance their academic credibility in the field. Overall, this internship provides a unique and enriching opportunity for the intern to expand their knowledge, skills, and professional network within a dynamic and impactful research environment.

 

This project will support the company in developing a crucial technical solution to propel its product into new market opportunities. Introducing a new software feature for automatic radiographic measurements holds the potential to significantly enhance the effectiveness and versatility of the ConfirMap system for ortho-trauma applications. Upon demonstrating that the developed techniques meet the necessary accuracy and performance standards, the research outcomes will be seamlessly integrated into the ConfirMap system. Additionally, the findings of the research will be submitted to peer-reviewed scientific and clinical journals for wider dissemination and validation within the research community.