Multi-modal Image Registration

In different areas, particularly medical image analysis, there is a vital need to access and analyse dynamic three dimensional (3D) images of the anatomical structures of the human body. This can enable specialists to track events as well as clinically conduct and evaluate surgical and radio therapeutical procedures. For example, measuring the 3D kinematics of knee joints in a dynamic manner is essential for understanding their normal functions and diagnosing any pathology, such as ligament injury and osteoarthritis. For evaluations of subsequent treatments, such as surgery and rehabilitation, and designs of joint replacements, having knowledge of the movements of knee joints is necessary. Image registration is increasingly being applied to medical image analysis. Whereas in mono-modal registration, the images to be registered are acquired by the same sensor, in multi-modal image registration, they can be taken from different devices or imaging protocols which makes this registration process much more challenging. The invasive or non-invasive nature of the registration method used, the computational time it requires as well as its accuracy and robustness against a large range of initial displacements are the most important features used for its evaluation. As currently available approaches have limited capabilities to register images with large initial displacements and are either not sufficiently accurate or very computationally expensive, the objective of this research is to propose new registration methods, that provide dynamic 3D images, to address these issues. In the first part of this study, I conducted research on registering an individuals’ natural knee bones that can provide 3D information of knee joint kinematics which can be very helpful for improving the accuracy of diagnosis and enabling targeted treatments. A fast, accurate and robust hybrid rigid body registration method based on two different multi-modal similarity measures, the edge position difference (EPD) and sum-of-conditional variance (SCV), is proposed. It uses a gradient descent optimisation technique to register multi-modal images and determine the best transformation parameters. It helps to achieve a trade-off among different challenges, including time complexity, accuracy and robustness against a large range of initial displacements. To evaluate it, several experiments were performed on two different databases: one collected from the knee bones of four patients and the other from three knee cadavers installed on a mechanical positioning system, with the results showing that this method is accurate, fast and robust against large initial displacement. Then, I conducted research on registering implanted human knee joints and proposed a non-invasive, robust 3D-to-2D registration method which can be used for 3D evaluations of the status of knee implants after joint replacement surgeries. In this method, 3D models of the implants for an individual with the relevant post-operative fluoroscopy frames are able to be used in the registration process. As a result, it is possible to perform 3D analysis at any time after a surgery by simply taking single-plane radiographs. This approach uses the EPD multi-modal similarity measure together with a steepest descent optimisation method. It applies coarse-to-fine registration steps to determine the transformation parameters that lead to the best alignment between the model used and X-ray images to be registered. The experimental results showed that not only does the proposed registration method have a high success rate but that it is also much faster than the most relevant competitive approach. Although the experiments were designed for a 3D analysis of total knee arthroplasty (TKA) components, this proposed method can be applied to other joints such as the ankle or hip. In the final part of my research, I developed a multi-frame 2D fluoroscopy to 3D model registration method for measuring the kinematics of post-operative knee joints. It uses a coarse-to-fine approach and applies the normalised EPD (NEPD) and SCV similarity measures together with a gradient descent optimisation method and an interpolation estimation one. In order to measure the kinematics of post- operative knee joints, after a TKA surgery, a 3D knee implant model can be registered with a number of single-plane fluoroscopy frames of the patient’s knee. Generally, when this number is quite high, the computational cost for registering the frames and a 3D model is expensive. Therefore, in order to speed up the registration process, a cubic spline interpolation prediction method is applied to initialise and estimate the 3D positions of the 3D model in each fluoroscopy frame instead of applying a registration algorithm on all the frames, one after the other. The estimated 3D positions are then tuned using a registration improvement step. The experimental results demonstrated that the proposed registration method is much faster than the best existing one and achieves almost the same accuracy. It also provides smooth registration results which can lead to more natural 3D modelling of joint movements

An Exploration into the Relationship between Knee Shape and Kinematics Before and After Total Knee Replacement

The knee joint is unique in its design and it is thought that its articular shape is the main driver of biomechanical behaviour. Although the shape of the bony knee is acknowledged to change with osteoarthritis, the specific relationship between shape changes and function is not well understood. Deep flexion, specifically kneeling, is an ideal testing environment for the tibiofemoral joint because it is both a difficult and a desirable activity for people with knee osteoarthritis. Total knee replacement (TKR) is a surgery which attempts to restore the articular shape in order to enhance function. However, the influence of implant design on kneeling kinematics is unclear. This thesis examines the role of knee shape on kneeling kinematics before and following total knee replacement. The four aims of this thesis were to: 1) describe and quantify the main modes of shape variation which distinguish end-stage OA from age- and sex-similar healthy knees; 2) determine whether bony shape can predict deep kneeling kinematics in people with and without OA; 3) examine the published literature to determine whether there are any differences in contact patterns as a function of TKR design; and 4) to prospectively compare the six-degree-of-freedom kneeling kinematics of posterior-stabilised fixed bearing, cruciate-retaining fixed bearing and cruciate retaining rotating platform designs. Statistical shape modelling identified differences between osteoarthritic and healthy bony knee shape. Specifically: large expansions around the femoral cartilage plate; expansion and depression at the medial tibial border; and an area of corresponding bony expansion on the posterior aspect of the medial femur and tibia. Statistical shape modelling and image registration derived six degree of freedom kinematics were used to test for associations between knee shape and kneeling kinematics. The kinematic variability was described using bivariate principle component analysis. While we found weak associations between knee shape and kinematics, BMI and group (OA vs Healthy) also predicted kneeling kinematics. This indicates that factors other than bony shape are important in predicting kneeling kinematics. The third study was a systematic review with meta-analyses using quality effects models which characterised the influence of TKR implant design on kneeling contact patterns. The review found posterior stabilised designs were different to cruciate retaining designs, but the heterogeneity was high limiting any firm conclusions. The final study was a prospective randomised clinical trial examining the influence of TKR design on kneeling kinematics. The study found that posterior-stabilised fixed-bearing and cruciate-retaining rotating-platform designs had higher maximal flexion compared to cruciate retaining-fixed bearing designs. Furthermore, posterior-stabilised fixed-bearing femoral components were more posterior and the cruciate-retaining rotating-platform was in more external femoral rotation throughout flexion. However, there was substantial between-patient variability. This research breaks new ground around which aspects of bony shape are altered in osteoarthritis and how these shapes, and prosthetic design, influence kneeling kinematics. Furthermore, the methodologies employed in this thesis provide new ways of describing the variability in complex shape and kinematics datasets, which may contribute to the identification of therapeutic efficacy. Knee shape is considered to be an important driver for normal movement. However, the results of this thesis indicate that there are potentially other factors, including soft-tissue properties and patient-specific movement strategies, which might influence the kinematics of deep kneeling. The message for surgeons and other clinicians is that bony shape and TKR design are not the primary drivers of functional performance and that kneeling should be on their radar as an activity to which their patients should aspire

Biomechanical function in knee osteoarthritis and post-total knee replacement: comparing subjective and objective outcomes and predicting gait function post-total knee arthroplasty

Patient-reported outcomes (PROMs) have been widely utilised to evaluate the TKR outcome and to predict it. Although PROMs inform on the patient’s perception of function, they are mostly influenced by pain levels, and relate poorly to what patients can achieve objectively. For this reason, both subjective and objective function should be measured to comprehensively quantify the impairments pre-TKR, the improvement post-TKR and to aid in TKR outcome prediction. However, there is no gold standard to measure function objectively. This research aimed to advance the application of the Cardiff classifier, a measure of gait biomechanics, to subjects with severe knee OA and post-TKR, to compare the Classifier to similar measures of gait biomechanics, identify factors predictive of the gait biomechanics post-TKR, and investigate the relationship between biomechanics, patient-reported outcomes and physical performance pre- and post-TKR. 3D gait analysis was performed in two cohorts of non-pathological subjects (NPs) and patients pre and one-year post-TKR (Cardiff and Karolinska cohorts). The Cardiff classifier’s Belief of OA (BOA), Gait Deviation Index (GDI) and the GDI-kinetics were utilised to evaluate patients’ objective gait function at each time point in both cohorts. The BOA had a large responsiveness to change, which was greater than the GDI and GDI-kinetic in 39 patients from the UK and 29 from Sweden. While the correlation between BOA-GDI and BOA-GDI-kinetic was moderate pre-TKR in both cohorts, the two gait indexes and their change pre to post-TKR showed poor or mixed agreement with the BOA post-TKR or its change score. By comparing the outputs of the classifiers developed from each cohort, it was found that about 55% of the highest-ranking gait features discriminating patients pre-TKR to their references were the same or similar between Cardiff and Karolinska patients. Gait biomechanics improved in both patients’ groups but mostly did not return to normal one-year post-TKR. In the Cardiff cohort mentioned above, it was demonstrated that when comparing the patients gait function to NPs of similar age (NP50 classifier), the BOA was significantly lower (=better gait) pre- and one-year post-TKR versus comparing patients to a younger group of NPs (mixed-age classifier), but the change in gait function was comparable between the NP50 and mixed-age classifiers one-year post-surgery. Pre-surgical and surgical factors did not correlate to the change in BOA one-year post-TKR (NP50 classifier). A regression model revealed that the objective gait function pre-TKR, sex and BMI explained 56% of the variance of the gait function one-year post-TKR; there was a significant association between a worse gait function pre-surgery and a worse gait biomechanics one-year post-TKR, irrespective of sex and BMI. A patient sub-group analysis also showed that a greater knee ROM pre-TKR was associated with a better gait function post-TKR. 3D gait analysis data, performance-based tests (PBTs) (timed-up and go, 40m fast-paced walk test, stair climb test and 30s chair test), Oxford Knee Score and Knee Injury and Osteoarthritis Outcome Score were collected from patients pre, three and six months post-TKR. It was found that trunk kinematics in the frontal, sagittal and transverse planes were not relevant in aiding in the discrimination of gait biomechanics between 9 NPs (n = 18 knees) and 18 subjects with late-stage OA (n = 20 knees) within the Cardiff classifier. Results showed a correlation, or trends of association, between gait biomechanics and the core PBTs suggested by OARSI (40m fast-paced walk test, stair climb test and 30s chair test), pre, three and six months post-TKR. However, no correlation, nor a trend of association, could be found between PROMs and gait biomechanics or between PROMs and PBTs pre- or three, six and approximately twelve months post-TKR. Employing the Cardiff classifier to assess in vivo knee kinematics during a step-up motion showed an 83.8% accuracy in discriminating between severe knee OA (n = 18) and NP knee function (n = 19). The novel application of the Cardiff classifier to knee kinematics data collected via single-plane fluoroscopy showed that two years post-TKR, the knee function improved but was not comparable to NPs. The level of classification uncertainty was higher than previous studies employing the classifier, suggesting the need to include additional knee arthrokinematics features. Additionally, the results showed that knee kinematics was not associated with the OKS pre- or post-TKR or satisfaction, nor the patient’s perception of their knee “feeling like a normal knee” two years post-TKR. This work aided in expanding the application of the Cardiff classifier beyond the assessment of gait biomechanics, supported the use of PBTs for the assessment of function, reinforced the evidence that objective measures of function and PROMs measure different constructs and should be utilised together in evaluating OA or TKR outcomes