Literature DB >> 10204876

Determination of 3D cartilage thickness data from MR imaging: computational method and reproducibility in the living.

T Stammberger1, F Eckstein, K H Englmeier, M Reiser.   

Abstract

The objective of this work was to develop a computational approach for quantifying the three-dimensional (3D) thickness distribution of articular cartilage with magnetic resonance (MR) imaging, independent of the imaging plane, and to test the reproducibility of the method in the living. An algorithm was implemented, based on a 3D Euclidean distance transformation, and its accuracy was assessed in geometric test objects, for which an analytic solution was available. The precision of the method was evaluated in six replicated MR data sets of the knee joint cartilage of eight volunteers. The algorithm produced 3D thickness values identical to those of the analytic solutions in the test objects. The reproducibility of the mean cartilage thickness in the patellar and tibial cartilages was 1.5-3.4% (root-mean-square average of the individual coefficient of variation percent), that of the maximal thickness 2.1-7.9%, and that of the thickness distribution 2.3-6.1%. The method presented allows for noninvasive analysis of 3D cartilage thickness from MR images in biomechanical and clinical investigations.

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Year:  1999        PMID: 10204876     DOI: 10.1002/(sici)1522-2594(199903)41:3<529::aid-mrm15>3.0.co;2-z

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  31 in total

1.  Comparison between different implementations of the 3D FLASH sequence for knee cartilage quantification.

Authors:  Martin Hudelmaier; Christian Glaser; Christian Pfau; Felix Eckstein
Journal:  MAGMA       Date:  2011-12-14       Impact factor: 2.310

Review 2.  The effects of exercise on human articular cartilage.

Authors:  F Eckstein; M Hudelmaier; R Putz
Journal:  J Anat       Date:  2006-04       Impact factor: 2.610

Review 3.  Therapeutic targets in osteoarthritis: from today to tomorrow with new imaging technology.

Authors:  J-P Pelletier; J Martel-Pelletier
Journal:  Ann Rheum Dis       Date:  2003-11       Impact factor: 19.103

4.  Accuracy of 3D cartilage models generated from MR images is dependent on cartilage thickness: laser scanner based validation of in vivo cartilage.

Authors:  Seungbum Koo; Nicholas J Giori; Garry E Gold; Chris O Dyrby; Thomas P Andriacchi
Journal:  J Biomech Eng       Date:  2009-12       Impact factor: 2.097

5.  Variation in the Thickness of Knee Cartilage. The Use of a Novel Machine Learning Algorithm for Cartilage Segmentation of Magnetic Resonance Images.

Authors:  Romil F Shah; Alejandro M Martinez; Valentina Pedoia; Sharmila Majumdar; Thomas P Vail; Stefano A Bini
Journal:  J Arthroplasty       Date:  2019-07-24       Impact factor: 4.757

6.  Quantitative versus semiquantitative MR imaging of cartilage in blood-induced arthritic ankles: preliminary findings.

Authors:  Andrea S Doria; Ningning Zhang; Bjorn Lundin; Pamela Hilliard; Carina Man; Ruth Weiss; Gary Detzler; Victor Blanchette; Rahim Moineddin; Felix Eckstein; Marshall S Sussman
Journal:  Pediatr Radiol       Date:  2014-02-13

7.  The effect of a six-month training program followed by a marathon run on knee joint cartilage volume and thickness in marathon beginners.

Authors:  Stefan Hinterwimmer; Matthias J Feucht; Corinna Steinbrech; Heiko Graichen; Rüdiger von Eisenhart-Rothe
Journal:  Knee Surg Sports Traumatol Arthrosc       Date:  2013-09-18       Impact factor: 4.342

8.  Identification of progressors in osteoarthritis by combining biochemical and MRI-based markers.

Authors:  Erik B Dam; Marco Loog; Claus Christiansen; Inger Byrjalsen; Jenny Folkesson; Mads Nielsen; Arish A Qazi; Paola C Pettersen; Patrick Garnero; Morten A Karsdal
Journal:  Arthritis Res Ther       Date:  2009-07-24       Impact factor: 5.156

9.  Knee cartilage MRI with in situ mechanical loading using prospective motion correction.

Authors:  Thomas Lange; Julian Maclaren; Michael Herbst; Cris Lovell-Smith; Kaywan Izadpanah; Maxim Zaitsev
Journal:  Magn Reson Med       Date:  2014-02       Impact factor: 4.668

10.  Cartilage imaging at 3.0T with gradient refocused acquisition in the steady-state (GRASS) and IDEAL fat-water separation.

Authors:  Richard Kijowski; Michael Tuite; Leo Passov; Ann Shimakawa; Huanzhou Yu; Huanzhou Hu; Scott B Reeder
Journal:  J Magn Reson Imaging       Date:  2008-07       Impact factor: 4.813

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