Over the last decade Computer Assisted Orthopaedic Surgery (CAOS) has emerged particularly in the area of minimally invasive Unicompartmental Knee Replacement (UKR) surgery. Image registration is an important aspect in all computer assisted surgeries which is a process of developing a spatial relationship between pre-operative data, such as Computerised Tomography (CT) scans or Magnetic Resonance Imaging (MRI) scans and the physical patient in the operation theatre. It allows the surgeon to visualise the 3D pre-operative scan data in-relation to the patient's anatomy in the operating theatre.

Current image registration techniques for CAOS in minimally invasive UKR are achieved by digitising points on the articulating surface of the knee joint using a navigated probe. By using these digitised points a rigid body is formed which is then fitted to the pre-operative scan data using a best fit type minimisation. However, this manual digitisation approach is time consuming and often takes 15–20 minutes and is therefore costly. The rationale for this study was to develop a new, quick, cost effective, contactless shape acquisition technique which could produce an accurate rigid body model in theatre from the ends of the exposed bones using 3D scans taken intra-operatively by a Laser Displacement Sensor.

Bespoke and automated 3D laser scanning techniques based on DAVID-Laserscanner have been developed and were used to scan surface geometry of the knee joint in 10 cadaveric legs. The Medial compartments of 9 joints had undergone a UKR procedure post donation but the lateral compartments were unaffected. The 9 legs were CT scanned and then segmented using Mimics 12.01 to generate 3D models of the medial compartments. The 10 legs were also MRI scanned using a 3D FLASH technique to produce 3D models of the lateral articular cartilage. All the samples were then 3D Laser scanned using a tailored plane-less technique and customised positioner assemblies. The CT and MRI generated 3D models were then registered with the corresponding 3D Laser scans in the Geomagic Qualify® package using manual surface registration. This is a type of surface (point based registration or free-form surface matching) registration which works closely on Iterative closest point (ICP) algorithm. Once the models were registered, a best fit alignment was performed between two datasets. Results indicate average best fit alignment errors and standard deviations ranging from 0.2 mm to 0.9 mm with errors normally distributed. Most of the errors could be attributed to calibration errors, segmentation errors and post-processing systematic errors.

We have demonstrated the feasibility of using a novel laser scanning technique where by acquiring multiple scans of the tibio-femoral joint in theatre, complete 3D models of the geometry and surface texture can be developed which can be registered with the pre-operative scan. The overall time for scanning, post-processing and the registration requires less than 5 minutes and is a non-invasive, cost-efficient approach. This study has provided proof of concept for a new automated registration technique with the potential for providing a quantitative assessment of the articular cartilage integrity during lower limb arthroplasty.

Over the last decade Computer Assisted Orthopaedic Surgery (CAOS) has emerged particularly in the area of minimally invasive Uni-compartmental Knee Replacement (UKR) surgery. Image registration is an important aspect in all computer assisted surgery including Neurosurgery, Cranio-maxillofacial surgery and Orthopaedics. It is possible for example to visualise the patient's medial or lateral condyle on the tibia in the pre-operated CT scan as well as to locate the same points on the actual patient during surgery using intra-operative sensors or probes. However their spatial correspondence remains unknown until image registration is achieved. Image registration process generates this relationship and allows the surgeon to visualise the 3D pre-operative scan data in-relation to the patient's anatomy in the operating theatre.

Current image registration for most CAOS applications is achieved through probing along the articulating surface of the femur and tibial plateau and using these digitised points to form a rigid body which is then fitted to the pre-operative scan data using a best fit type minimisation. However, the probe approach is time consuming which often takes 10–15 minutes to complete and therefore costly. Thus the rationale for this study was to develop a new, cost effective, contactless, automated registration method which would entail much lesser time to produce the rigid body model in theatre from the ends of the exposed bones. This can be achieved by taking 3D scans intra-operatively using a Laser Displacement Sensor.

A number of techniques using hand held and automated 3D Laser scanners for acquiring geometry of non-reflective objects have been developed and used to scan the surface geometry of a porcine femur with four holes drilled in it. The distances between the holes and the geometry of the bone were measured using digital vernier callipers as well as measurements acquired from the 3D scans. These distances were measured in an open source package MESHLAB version 1.3.2 used for the interpretation, post-processing and analysis of the 3D meshes. Absolute errors ranging from of 0.1 mm to 0.4 mm and the absolute percentage errors ranging from 0.48% to 0.75% were found. Additionally, a pre-calibrated dental model was scanned using a 650 nm FARO™ Laser arm using the global surface registration approach in Geomagic Qualify package and our 3D Laser scanner. Results indicate an average measurement error of 0.16 mm, with deviations ranging from 0.12mm to −0.13 mm and a standard deviation of 0.2 mm. We demonstrated that by acquiring multiple scans of the targets, complete 3D models along with their surface texture can be developed. The overall scanning process, including time required for the post-processing of the data requires less than 20 minutes and is a cost-efficient approach. Moreover, the majority of that time was used in post processing the acquired data which could be potentially reduced through the use of bespoke application software. This project has provided proof of concept for a new automated, non-invasive and cost efficient registration technique with the potential of providing a quantitative assessment of the articular cartilage integrity during lower limb arthroplasty.

Objectives

All Polyethylene Tibial components in Total Knee Arthroplasty have been in use for some years, studies showing equivalent results to Total Knee Arthroplasty (TKA) with metal-backed Tibial components at 10 years have shown no significant difference between the two on radiostereometric analysis and revision rates[1].

Post operative patient outcome data using standard metal-backed Tibial components is widely reported in the literature. This study is looking at patient outcomes following All-polyethylene tibial component TKA. We hypothesize that using standard patient outcome measures, an improvement comparable with that expected for metal-backed tibial component TKA will be shown with All-polyethylene tibial component TKA.