Intra-operative shape acquisition of tibio-femoral joints using 3D laser scanning fro computer assisted orthopaedic surgery, a proof of concept

  • Shailesh Joshi

Student thesis: Doctoral Thesis


Image registration is an important aspect in all computer assisted surgeries including Neurosurgery, Cranio-maxillofacial surgery and Orthopaedics. It is a process of developing a spatial relationship between pre-operative data, such as Computed Tomography (CT) scans or Magnetic Resonance Imaging (MRI) scans and the physical patient in the operating theatre. Current image registration techniques for Computer Assisted Orthopaedic Surgery (CAOS) in minimally invasive Unicompartmental Knee Arthroplasty (UKA) surgery are invasive, time consuming and often take 14-20 minutes and are therefore costly. The rationale for this study was to develop a new operating theatre compliant, quick, cost effective, contactless, automated technique for image registration during CAOS based on an accurate rigid body model of the ends of the exposed knee joint, produced using 3D laser scans taken intra-operatively by a Laser Displacement Sensor. Bespoke automated 3D laser scanning techniques based on the DAVID Laserscanner method were developed and were used to scan surface geometry of the knee joints in cadaveric legs. The laser scanned knee joint models were registered with the pre-operative (MRI/CT) models and the deviations were evaluated. Furthermore, trends in the deviations were studied along with a supportive validity study. Results indicated that the laser scanner can repeatedly produce accurate 3D models of the human tibio-femoral joint in the operating theatre. This study has provided a proof of concept for a new in situ automated shape acquisition and registration technique for CAOS with the potential for providing a quantitative assessment of the articular cartilage integrity during lower limb arthroplasty.
Date of Award29 Apr 2015
Original languageEnglish
Awarding Institution
  • University Of Strathclyde
SupervisorPhilip Rowe (Supervisor) & Phil Riches (Supervisor)

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