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IEEE Trans Nanobioscience. 2002 Mar;1(1):42-51.

Automated techniques for visualization and mapping of articular cartilage in MR images of the osteoarthritic knee: a base technique for the assessment of microdamage and submicro damage.

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  • 1Department of Bioengineering, Imperial College, London, UK.

Abstract

The purpose of this paper is to describe automated techniques for the visualization and mapping of articular cartilage in magnetic resonance (MR) images of the osteoarthritic knee. The MR sequences and analysis software which will be described allow the assessment of cartilage damage using a range of standard scanners. With high field strength systems it would be possible, using these techniques, to assess micro-damage. The specific aim of the paper is to develop and validate software for automated segmentation and thickness mapping of articular cartilage from three-dimensional (3-D) gradient-echo MR images of the knee. The method can also be used for MR-based assessment of tissue engineered grafts. Typical values of cartilage thickness over seven defined regions can be obtained in patients with osteoarthritis (OA) and control subjects without OA. Three groups of patients were studied. The first group comprised patients with moderate OA in the age range 45-73 years. The second group comprised asymptomatic volunteers of 50-65 years; the third group, younger volunteers selected by clinical interview, history and X-ray. In this paper, sagittal 3-D spoiled-gradient steady-state acquisition images were obtained using a 1.5-T GE whole-body scanner with a specialist knee coil. For validation bovine and porcine cadaveric knees were given artificial cartilage lesions and then imaged. The animal validations showed close agreement between direct lesion measurements and those obtained from the MR images. The feasibility of semi-automated segmentation is demonstrated. Regional cartilage thickness values are seen as having practical application for fully automated detection of OA lesions even down to the submicrometer level.

PMID:
16689221
[PubMed - indexed for MEDLINE]
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