Literature DB >> 16689221

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.

Peter M M Cashman1, Richard I Kitney, Munir A Gariba, Mary E Carter.   

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.

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Year:  2002        PMID: 16689221     DOI: 10.1109/tnb.2002.806916

Source DB:  PubMed          Journal:  IEEE Trans Nanobioscience        ISSN: 1536-1241            Impact factor:   2.935


  6 in total

Review 1.  Systematic review of the concurrent and predictive validity of MRI biomarkers in OA.

Authors:  D J Hunter; W Zhang; Philip G Conaghan; K Hirko; L Menashe; L Li; W M Reichmann; E Losina
Journal:  Osteoarthritis Cartilage       Date:  2011-03-23       Impact factor: 6.576

2.  Quantitative cartilage imaging in knee osteoarthritis.

Authors:  Felix Eckstein; Wolfgang Wirth
Journal:  Arthritis       Date:  2010-12-08

3.  A Coarse-to-Fine Framework for Automated Knee Bone and Cartilage Segmentation Data from the Osteoarthritis Initiative.

Authors:  Yang Deng; Lei You; Yanfei Wang; Xiaobo Zhou
Journal:  J Digit Imaging       Date:  2021-05-24       Impact factor: 4.903

4.  Development of a rapid knee cartilage damage quantification method using magnetic resonance images.

Authors:  Ming Zhang; Jeffrey B Driban; Lori Lyn Price; Daniel Harper; Grace H Lo; Eric Miller; Robert J Ward; Timothy E McAlindon
Journal:  BMC Musculoskelet Disord       Date:  2014-08-06       Impact factor: 2.362

5.  Fully Automatic Knee Bone Detection and Segmentation on Three-Dimensional MRI.

Authors:  Rania Almajalid; Ming Zhang; Juan Shan
Journal:  Diagnostics (Basel)       Date:  2022-01-06

6.  Reproducibility of an Automated Quantitative MRI Assessment of Low-Grade Knee Articular Cartilage Lesions.

Authors:  Vladimir Juras; Pavol Szomolanyi; Markus M Schreiner; Karin Unterberger; Andrea Kurekova; Benedikt Hager; Didier Laurent; Esther Raithel; Heiko Meyer; Siegfried Trattnig
Journal:  Cartilage       Date:  2020-09-29       Impact factor: 4.634

  6 in total

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