Accurate 3D reconstruction of bony surfaces using ultrasonic synthetic aperture techniques for robotic knee arthroplasty

Robotically guided knee arthroplasty systems generally require an individualized, preoperative 3D model of the knee joint. This is typically measured using Computed Tomography (CT) which provides the required accuracy for preoperative surgical intervention planning. Ultrasound imaging presents an attractive alternative to CT, allowing for reductions in cost and the elimination of doses of ionizing radiation, whilst maintaining the accuracy of the 3D model reconstruction of the joint. Traditional phased array ultrasound imaging methods, however, are susceptible to poor resolution and signal to noise ratios (SNR). Alleviating these weaknesses by offering superior focusing power, synthetic aperture methods have been investigated extensively within ultrasonic non-destructive testing. Despite this, they have yet to be fully exploited in medical imaging. In this paper, the ability of a robotic deployed ultrasound imaging system based on synthetic aperture methods to accurately reconstruct bony surfaces is investigated. Employing the Total Focussing Method (TFM) and the Synthetic Aperture Focussing Technique (SAFT), two samples were imaged which were representative of the bones of the knee joint: a human-shaped, composite distal femur and a bovine distal femur. Data were captured using a 5MHz, 128 element 1D phased array, which was manipulated around the samples using a robotic positioning system. Three dimensional surface reconstructions were then produced and compared with reference models measured using a precision laser scanner. Mean errors of 0.82 mm and 0.88 mm were obtained for the composite and bovine samples, respectively, thus demonstrating the feasibility of the approach to deliver the sub-millimetre accuracy required for the application

Image Guidance in Telemanipulator Assisted Urology Surgery

This thesis outlines the development of an image guided surgery system, intended for use in \davinci assisted radical prostatectomy but more generally applicable to laparoscopic urology surgery. We defined the key performance parameter of the system as the accuracy of overlaying modelled anatomy onto the surgical scene. This thesis is primarily concerned with determining the system accuracy based on an analysis of the system's components. A common error measure was defined for all system components. This is an on screen error (measured in pixels) based on the error in projecting a single point lying near the apex of the prostate with the endoscope in a typical surgical pose. In this case the projected point was approximately 200 mm from the endoscope lens. An intraoperative coordinate system is first defined as the coordinate system of an optical tracking system used to track the endoscope. The MRI image of the patient is transformed into the intraoperative coordinate system. Prior to surgery the endoscope is calibrated and during surgery the endoscope is tracked, defining a transform from the coordinates of the optical tracking system to the endoscope screen. This transform is used to project the MRI image onto the endoscope video display. The early part of the thesis describes a novel algorithm for registering MRI to ultrasound images of the bone which was used to put the MRI image into the intraoperative coordinate system. Using this algorithm avoids the need for fiducial markers. The table below shows the errors (as on screen pixel RMS) due to using this algorithm. An approximate value as RMS distance error at the prostate apex point is also included

Statistical Modeling to Investigate Anatomy and Function of the Knee

The natural knee is a hinge joint with significant functional requirements during activities of daily living; as a result, acute and chronic injuries can occur. Pathologies are influenced by joint anatomy and may include patellar maltracking, cartilage degeneration (e.g. osteoarthritis), or acute injuries such as meniscal or ligamentous tears. Population variability makes broadly applicable conclusions about etiology of these conditions from small-scale investigations challenging. The work presented in this dissertation is a demonstration of statistical modeling approaches to evaluate population variability in anatomy of the knee and function of its tibiofemoral (TF) and patellofemoral (PF) joints. Three-dimensional (3D) computational models of the bone and cartilage in the knee were characterized using a principal component analysis (PCA) algorithm to understand the primary sources of variability in shape and motion and make predictions from sparse data. Statistical models were used to investigate relationships between natural knee anatomy and kinematics and make predictions of both shape and function from sparse data. A whole-joint characterization study identified key correlations between shape and function of the TF and PF joints, successfully recreating results from multiple studies and introducing new relationships under one unified approach. Results from this study were used in a subsequent investigation to build a statistical model of two-dimensional (2D) shape and alignment measures and 6 degree-of-freedom (DOF) kinematics to identify the key measures capable of predicting PF joint motion. The ability to reconstruct the 3D implanted patellar bone of a subject with a total knee replacement (TKR) was evaluated by a statistical shape model of the patella and simulated 2D edge profiles in a custom optimization algorithm. Lastly, a validated predictive algorithm was employed to assess the accuracy of subject-specific knee articular cartilage predictions from bony geometry. The utility of statistical modeling is elucidated by the population-based evaluations of the musculoskeletal system described in this work and could continue to inform characteristics related to pathological conditions and large-scale computational evaluations of implant performance

A Novel Imaging System for Automatic Real-Time 3D Patient-Specific Knee Model Reconstruction Using Ultrasound RF Data

This dissertation introduces a novel imaging method and system for automatic real-time 3D patient-specific knee model reconstruction using ultrasound RF data. The developed method uses ultrasound to transcutaneously digitize a point cloud representing the bone’s surface. This point cloud is then used to reconstruct 3D bone model using deformable models method. In this work, three systems were developed for 3D knee bone model reconstruction using ultrasound RF data. The first system uses tracked single-element ultrasound transducer, and was experimented on 12 knee phantoms. An average reconstruction accuracy of 0.98 mm was obtained. The second system was developed using an ultrasound machine which provide real-time access to the ultrasound RF data, and was experimented on 2 cadaveric distal femurs, and proximal tibia. An average reconstruction accuracy of 0.976 mm was achieved. The third system was developed as an extension of the second system, and was used for clinical study of the developed system further assess its accuracy and repeatability. A knee scanning protocol was developed to scan the different articular surfaces of the knee bones to reconstruct 3D model of the bone without the need for bone-implanted motion tracking reference probes. The clinical study was performed on 6 volunteers’ knees. Average reconstruction accuracy of 0.88 mm was achieved with 93.5% repeatability. Three extensions to the developed system were investigated for future work. The first extension is 3D knee injection guidance system. A prototype for the 3D injection guidance system was developed to demonstrate the feasibility of the idea. The second extension in a knee kinematics tracking system using A-mode ultrasound. A simulation framework was developed to study the feasibility of the idea, and to find the best number of single-element ultrasound transducers and their spatial distribution that yield the highest kinematics tracking accuracy. The third extension is 3D cartilage model reconstruction. A preliminary method for cartilage echo detection from ultrasound RF data was developed, and experimented on the distal femur scans of one of the clinical study’s volunteers to reconstruct a 3D point cloud for the cartilage

Applied AI/ML for automatic customisation of medical implants

Most knee replacement surgeries are performed using ‘off-the-shelf’ implants, supplied with a set number of standardised sizes. X-rays are taken during pre-operative assessment and used by clinicians to estimate the best options for patients. Manual templating and implant size selection have, however, been shown to be inaccurate, and frequently the generically shaped products do not adequately fit patients’ unique anatomies. Furthermore, off-the-shelf implants are typically made from solid metal and do not exhibit mechanical properties like the native bone. Consequently, the combination of these factors often leads to poor outcomes for patients. Various solutions have been outlined in the literature for customising the size, shape, and stiffness of implants for the specific needs of individuals. Such designs can be fabricated via additive manufacturing which enables bespoke and intricate geometries to be produced in biocompatible materials. Despite this, all customisation solutions identified required some level of manual input to segment image files, identify anatomical features, and/or drive design software. These tasks are time consuming, expensive, and require trained resource. Almost all currently available solutions also require CT imaging, which adds further expense, incurs high levels of potentially harmful radiation, and is not as commonly accessible as X-ray imaging. This thesis explores how various levels of knee replacement customisation can be completed automatically by applying artificial intelligence, machine learning and statistical methods. The principal output is a software application, believed to be the first true ‘mass-customisation’ solution. The software is compatible with both 2D X-ray and 3D CT data and enables fully automatic and accurate implant size prediction, shape customisation and stiffness matching. It is therefore seen to address the key limitations associated with current implant customisation solutions and will hopefully enable the benefits of customisation to be more widely accessible.Open Acces

Shape Modelling of Bones: Application to the Primate Shoulder

The aims of this work were to develop techniques for describing morphological variations of shoulder bones and to test these on real datasets. The robust measurement and description of anatomical geometry can provide accu- rate estimation and better understanding of bone morphology. Feature lines were detected automatically using crest line techniques and shape information from shoulder bones was extracted based on the extracted feature lines. Redefinition of local coordinate systems was proposed utilising the crest line technique. Three dimensional statistical shape models (SSM) were built for a set of primate humeri and scapulae. Two types of models were constructed: one incorporated the main- tained original scale whilst the other used scaled bones. Variations were captured and quantified by Principal Component Analysis (PCA). The application can be extended generally to long bones and other complex bones and was also tested on human femora. Techniques to predict the shape of one bone from its neighbour at a joint were presented. PCA was used to reduce data dimensionality to a few principal components. Canonical Correlation Analysis (CCA) and Partial Least Square (PLS) Regression were applied to explore the linear morphological correlations between the two shoulder bones and to predict the shape of one segment given the shape of the adjoining segment

The role of subchondral bone in osteoarthritis

Osteoarthritis (OA) is the most common form of arthritis. Affected individuals commonly suffer with chronic pain, joint dysfunction, and reduced quality of life. OA also confers an immense burden on health services and economies. Current OA therapies are symptomatic and there are no therapies that modify structural progression. The lack of validated, responsive and reliable biomarkers represents a major barrier to the development of structure-modifying therapies. MRI provides tremendous insight into OA structural disease and has highlighted the importance of subchondral bone in OA. The hypothesis underlying this thesis is that novel quantitative imaging biomarkers of subchondral bone will provide valid measures for OA clinical trials. The Osteoarthritis Initiative (OAI) provided a large natural history database of knee OA to enable testing of the validity of these novel biomarkers. A systematic literature review identified independent associations between subchondral bone features with structural progression, pain and total knee replacement in peripheral joint OA. However very few papers examined the association of 3D bone shape with these patient-centred outcomes. A cross-sectional analysis of the OAI established a significant association between 3D bone area and conventional radiographic OA severity scores, establishing construct validity of 3D bone shape. A nested case-control analysis within the OAI determined that 3D bone shape was associated with the outcome of future total knee replacement, establishing predictive validity for 3D bone shape. A regression analysis within the OAI identified that 3D bone shape was associated with current knee symptoms but not incident symptoms, establishing evidence of concurrent but not predictive validity for new symptoms. In summary, 3D bone shape is an important biomarker of OA which has construct and predictive validity in knee OA. This thesis, along with parallel work on reliability and responsiveness provides evidence supporting its suitability for use in clinical trials