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Proceedings Paper

A system for the registration of arthroscopic images to magnetic resonance images of the knee: for improved virtual knee arthroscopy
Author(s): Chengliang Hu; Giancarlo Amati; Nicola Gullick; Stephen Oakley; Vassilios Hurmusiadis; Tobias Schaeffter; Graeme Penney; Kawal Rhode
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Paper Abstract

Knee arthroscopy is a minimally invasive procedure that is routinely carried out for the diagnosis and treatment of pathologies of the knee joint. A high level of expertise is required to carry out this procedure and therefore the clinical training is extensive. There are several reasons for this that include the small field of view seen by the arthroscope and the high degree of distortion in the video images. Several virtual arthroscopy simulators have been proposed to augment the learning process. One of the limitations of these simulators is the generic models that are used. We propose to develop a new virtual arthroscopy simulator that will allow the use of pathology-specific models with an increased level of photo-realism. In order to generate these models we propose to use registered magnetic resonance images (MRI) and arthroscopic video images collected from patients with a variety of knee pathologies. We present a method to perform this registration based on the use of a combined X-ray and MR imaging system (XMR). In order to validate our technique we carried out MR imaging and arthroscopy of a custom-made acrylic phantom in the XMR environment. The registration between the two modalities was computed using a combination of XMR and camera calibration, and optical tracking. Both two-dimensional (2D) and three-dimensional (3D) registration errors were computed and shown to be approximately 0.8 and 3 mm, respectively. Further to this, we qualitatively tested our approach using a more realistic plastic knee model that is used for the arthroscopy training.

Paper Details

Date Published: 13 March 2009
PDF: 9 pages
Proc. SPIE 7261, Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling, 726119 (13 March 2009); doi: 10.1117/12.813775
Show Author Affiliations
Chengliang Hu, King's College London (United Kingdom)
Giancarlo Amati, King's College London (United Kingdom)
Nicola Gullick, Guy's Hospital (United Kingdom)
National Institute for Health Research, Biomedical Research Ctr. (United Kingdom)
Stephen Oakley, The Royal Newcastle Ctr. (Australia)
Vassilios Hurmusiadis, Primal Pictures Ltd. (United Kingdom)
Tobias Schaeffter, King's College London (United Kingdom)
Graeme Penney, King's College London (United Kingdom)
Kawal Rhode, King's College London (United Kingdom)

Published in SPIE Proceedings Vol. 7261:
Medical Imaging 2009: Visualization, Image-Guided Procedures, and Modeling
Michael I. Miga; Kenneth H. Wong, Editor(s)

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