Literature DB >> 17923429

Inter-subject comparison of MRI knee cartilage thickness.

Julio Carballido-Gamio1, Jan S Bauer, Robert Stahl, Keh-Yang Lee, Stefanie Krause, Thomas M Link, Sharmila Majumdar.   

Abstract

In this paper, we present the development and application of current image processing techniques to perform MRI inter-subject comparison of knee cartilage thickness based on the registration of bone structures. Each point in the bone surface which is part of the bone-cartilage interface is assigned a cartilage thickness value. Cartilage and corresponding bone structures are segmented and their shapes interpolated to create isotropic voxels. Cartilage thicknesses are computed for each point in the bone-cartilage interfaces and transferred to the bone surfaces. Corresponding anatomic points are then computed for bone surfaces based on shape matching using 3D shape descriptors called shape contexts to register bones with affine and elastic transformations, and then perform a point to point comparison of cartilage thickness values. An alternative technique for cartilage shape interpolation using a morphing technique is also presented. The cartilage segmentation and morphing were validated visually, based on volumetric measurements of porcine knee images which cartilage volumes were measured using a water displacement method, and based on digital thickness values computed with an established technique. Shape matching using 3D shape contexts was validated visually and against manual shape matching performed by a radiologist. The reproducibility of intra- and inter-subject cartilage thickness comparisons was established, as well as the feasibility of using the proposed technique to build a mean femoral shape, cartilage thickness map, and cartilage coverage map. Results showed that the proposed technique is robust, accurate, and reproducible to perform point to point inter-subject comparison of knee cartilage thickness values.

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Year:  2007        PMID: 17923429      PMCID: PMC2838773          DOI: 10.1016/j.media.2007.08.002

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  29 in total

1.  An adaptive-focus statistical shape model for segmentation and shape modeling of 3-D brain structures.

Authors:  D Shen; E H Herskovits; C Davatzikos
Journal:  IEEE Trans Med Imaging       Date:  2001-04       Impact factor: 10.048

2.  Elastic registration of 3D cartilage surfaces from MR image data for detecting local changes in cartilage thickness.

Authors:  T Stammberger; J Hohe; K H Englmeier; M Reiser; F Eckstein
Journal:  Magn Reson Med       Date:  2000-10       Impact factor: 4.668

3.  Shape-based interpolation of multidimensional objects.

Authors:  S P Raya; J K Udupa
Journal:  IEEE Trans Med Imaging       Date:  1990       Impact factor: 10.048

4.  Morphology-based interpolation for 3D medical image reconstruction.

Authors:  J F Guo; Y L Cai; Y P Wang
Journal:  Comput Med Imaging Graph       Date:  1995 May-Jun       Impact factor: 4.790

5.  A non-invasive technique for 3-dimensional assessment of articular cartilage thickness based on MRI. Part 1: Development of a computational method.

Authors:  A Lösch; F Eckstein; M Haubner; K H Englmeier
Journal:  Magn Reson Imaging       Date:  1997       Impact factor: 2.546

6.  Measurement of localized cartilage volume and thickness of human knee joints by computer analysis of three-dimensional magnetic resonance images.

Authors:  A A Kshirsagar; P J Watson; J A Tyler; L D Hall
Journal:  Invest Radiol       Date:  1998-05       Impact factor: 6.016

7.  Segmenting articular cartilage automatically using a voxel classification approach.

Authors:  Jenny Folkesson; Erik B Dam; Ole F Olsen; Paola C Pettersen; Claus Christiansen
Journal:  IEEE Trans Med Imaging       Date:  2007-01       Impact factor: 10.048

8.  Interobserver reproducibility of quantitative cartilage measurements: comparison of B-spline snakes and manual segmentation.

Authors:  T Stammberger; F Eckstein; M Michaelis; K H Englmeier; M Reiser
Journal:  Magn Reson Imaging       Date:  1999-09       Impact factor: 2.546

9.  Corresponding articular cartilage thickness measurements in the knee joint by modelling the underlying bone (commercial in confidence).

Authors:  Tomos G Williams; Christopher J Taylor; ZaiXiang Gao; John C Waterton
Journal:  Inf Process Med Imaging       Date:  2003-07

10.  Quantification of articular cartilage in the knee with pulsed saturation transfer subtraction and fat-suppressed MR imaging: optimization and validation.

Authors:  C G Peterfy; C F van Dijke; D L Janzen; C C Glüer; R Namba; S Majumdar; P Lang; H K Genant
Journal:  Radiology       Date:  1994-08       Impact factor: 11.105

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  68 in total

1.  Association of MR relaxation and cartilage deformation in knee osteoarthritis.

Authors:  K Subburaj; R B Souza; C Stehling; B T Wyman; M-P Le Graverand-Gastineau; T M Link; X Li; S Majumdar
Journal:  J Orthop Res       Date:  2011-12-07       Impact factor: 3.494

2.  Effects of unloading on knee articular cartilage T1rho and T2 magnetic resonance imaging relaxation times: a case series.

Authors:  Richard B Souza; Thomas Baum; Samuel Wu; Brian T Feeley; Nancy Kadel; Xiaojuan Li; Thomas M Link; Sharmila Majumdar
Journal:  J Orthop Sports Phys Ther       Date:  2012-03-08       Impact factor: 4.751

Review 3.  MRI of hip cartilage: joint morphology, structure, and composition.

Authors:  Stephanie L Gold; Alissa J Burge; Hollis G Potter
Journal:  Clin Orthop Relat Res       Date:  2012-12       Impact factor: 4.176

4.  LOGISMOS--layered optimal graph image segmentation of multiple objects and surfaces: cartilage segmentation in the knee joint.

Authors:  Yin Yin; Xiangmin Zhang; Rachel Williams; Xiaodong Wu; Donald D Anderson; Milan Sonka
Journal:  IEEE Trans Med Imaging       Date:  2010-07-19       Impact factor: 10.048

5.  Using multidimensional topological data analysis to identify traits of hip osteoarthritis.

Authors:  Jasmine Rossi-deVries; Valentina Pedoia; Michael A Samaan; Adam R Ferguson; Richard B Souza; Sharmila Majumdar
Journal:  J Magn Reson Imaging       Date:  2018-05-07       Impact factor: 4.813

6.  3D bone-shape changes and their correlations with cartilage T1ρ and T2 relaxation times and patient-reported outcomes over 3-years after ACL reconstruction.

Authors:  Q Zhong; V Pedoia; M Tanaka; J Neumann; T M Link; B Ma; J Lin; X Li
Journal:  Osteoarthritis Cartilage       Date:  2019-02-23       Impact factor: 6.576

7.  Spatial distribution and temporal progression of T2 relaxation time values in knee cartilage prior to the onset of cartilage lesions - data from the Osteoarthritis Initiative (OAI).

Authors:  M Kretzschmar; M C Nevitt; B J Schwaiger; G B Joseph; C E McCulloch; T M Link
Journal:  Osteoarthritis Cartilage       Date:  2019-02-23       Impact factor: 6.576

8.  A review of imaging modalities for the hip.

Authors:  Alexander E Weber; Jon A Jacobson; Asheesh Bedi
Journal:  Curr Rev Musculoskelet Med       Date:  2013-09

Review 9.  Segmentation of joint and musculoskeletal tissue in the study of arthritis.

Authors:  Valentina Pedoia; Sharmila Majumdar; Thomas M Link
Journal:  MAGMA       Date:  2016-02-25       Impact factor: 2.310

10.  Composite metric R2  - R (1/T2  - 1/T ) as a potential MR imaging biomarker associated with changes in pain after ACL reconstruction: A six-month follow-up.

Authors:  Colin Russell; Valentina Pedoia; Sharmila Majumdar
Journal:  J Orthop Res       Date:  2016-09-16       Impact factor: 3.494

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