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Research Papers

Patterns of Femoral Cartilage Thickness are Different in Asymptomatic and Osteoarthritic Knees and Can be Used to Detect Disease-Related Differences Between Samples

[+] Author and Article Information
Julien Favre

e-mail: jfavre@stanford.edu

Sean F. Scanlan

e-mail: scanlansean@gmail.com
Department of Mechanical Engineering,
Stanford University,
Durand Building 061,
496 Lomita Mall,
Stanford, CA 94305-4308

Jenifer C. Erhart-Hledik

e-mail: jerhart@stanford.edu

Katerina Blazek

e-mail: kblazek@stanford.edu
Department of Mechanical Engineering,
Stanford University,
Durand Building 061,
496 Lomita Mall,
Stanford, CA 94305-4308;
Center for Tissue Regeneration,
Repair, and Restoration,
Veterans Administration Hospital,
3801 Miranda Avenue,
Palo Alto, CA 94304-1207

Thomas P. Andriacchi

Department of Mechanical Engineering,
Stanford University,
Durand Building 227,
496 Lomita Mall,
Stanford, CA 94305-4308;
Center for Tissue Regeneration,
Repair, and Restoration,
Veterans Administration Hospital,
3801 Miranda Avenue,
Palo Alto, CA 94304-1207;
Department of Orthopedic Surgery,
Stanford University,
Durand Building 227,
496 Lomita Mall,
Stanford, CA 94305-4308,
e-mail: tandriac@stanford.edu

Contributed by the Bioengineering Division of ASME for publication in the Journal of Biomechanical Engineering. Manuscript received December 7, 2012; final manuscript received May 16, 2013; accepted manuscript posted May 22, 2013; published online September 13, 2013. Assoc. Editor: Tammy Haut Donahue.

J Biomech Eng 135(10), 101002 (Sep 13, 2013) (10 pages) Paper No: BIO-12-1601; doi: 10.1115/1.4024629 History: Received December 07, 2012; Revised May 16, 2013; Accepted May 22, 2013

Measures of mean cartilage thickness over predefined regions in the femoral plate using magnetic resonance imaging have provided important insights into the characteristics of knee osteoarthritis (OA), however, this quantification method suffers from the limited ability to detect OA-related differences between knees and loses potentially important information regarding spatial variations in cartilage thickness. The objectives of this study were to develop a new method for analyzing patterns of femoral cartilage thickness and to test the following hypotheses: (1) asymptomatic knees have similar thickness patterns, (2) thickness patterns differ with knee OA, and (3) thickness patterns are more sensitive than mean thicknesses to differences between OA conditions. Bi-orthogonal thickness patterns were extracted from thickness maps of segmented magnetic resonance images in the medial, lateral, and trochlea compartments. Fifty asymptomatic knees were used to develop the method and establish reference asymptomatic patterns. Another subgroup of 20 asymptomatic knees and three subgroups of 20 OA knees each with a Kellgren/Lawrence grade (KLG) of 1, 2, and 3, respectively, were selected for hypotheses testing. The thickness patterns were similar between asymptomatic knees (coefficient of multiple determination between 0.8 and 0.9). The thickness pattern alterations, i.e., the differences between the thickness patterns of an individual knee and reference asymptomatic thickness patterns, increased with increasing OA severity (Kendall correlation between 0.23 and 0.47) and KLG 2 and 3 knees had significantly larger thickness pattern alterations than asymptomatic knees in the three compartments. On average, the number of significant differences detected between the four subgroups was 4.5 times greater with thickness pattern alterations than mean thicknesses. The increase was particularly marked in the medial compartment, where the number of significant differences between subgroups was 10 times greater with thickness pattern alterations than mean thickness measurements. Asymptomatic knees had characteristic regional thickness patterns and these patterns were different in medial OA knees. Assessing the thickness patterns, which account for the spatial variations in cartilage thickness and capture both cartilage thinning and swelling, could enhance the capacity to detect OA-related differences between knees.

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References

Figures

Grahic Jump Location
Fig. 4

Description of the sizes of the thickness cuts and the search regions as determined after the training phase. It is important to note that the best-matching points are located at the center of the search regions in this figure, but that these points can actually be anywhere in the search regions, depending on the individual thickness shape of the knee under analysis. It should also be noted that the search regions agree with the regions of thicker cartilage described in prior studies [20-22].

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Fig. 3

Illustration of the thickness cut extraction method. (a) The algorithm searches for the point within the search regions which provides thickness cuts that best match reference thickness cuts. (b) Thickness cuts for the best-matching points in the thickness map displayed in (a) and the reference asymptomatic thickness cuts (obtained with the training dataset) used for the searches.

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Fig. 2

(a) Thickness map with superposition of the cylinder fit used to define the coordinate system. (b) Example of thickness cut extraction around a point located 60 deg posterior to the notch and at 25% of the medial-lateral width.

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Fig. 1

Illustration of the method used to build the thickness map based MR images. (a) Segmentation of one MR image (solid line) and results from the segmentation of more medial images (dashed lines; 1 slice out of 6 is displayed). (b) Reconstruction of the three-dimensional cartilage model. The plane corresponding to the image segmented in (a) is indicated by a solid line. (c) Thickness map for the bone-cartilage layer. Again, the plane corresponding to the image segmented in (a) is indicated by a solid line.

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Fig. 8

Thickness patterns for the four subgroups in the analysis dataset (n = 20 knees per subgroup). Each graph displays the average (black line) ± one standard deviation (gray area) of a subgroup along with the coefficients of multiple determination (CMD) for the subgroup. For comparison, the reference asymptomatic thickness patterns (obtained with the training dataset) are presented using a blue dashed line.

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Fig. 5

Decomposition of a thickness cut into its relative (zero-mean) pattern and its mean value

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Fig. 6

Illustration of the “thickness pattern alteration” (Δtp) metric used to evaluate the similarity between the thickness pattern of an individual knee and the corresponding asymptomatic reference thickness pattern. As depicted in the right plot, Δtp is sensitive to local cartilage thinning and/or thickening that modify the form of the thickness pattern. Note that global cartilage thinning or thickening (i.e., changes in the mean value of the thickness cut) does not affect Δtp.

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Fig. 7

Illustration of the regions used for the secondary mean thickness measurements. (a) Ovoid regions defined by the size of the bi-orthogonal cuts and centered on the individual best-matching points. (b) A set of eight standard regions of interest [9].

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Fig. 9

Box plots of the thickness pattern alterations (Δtp) for the four subgroups in the analysis dataset. The bars at the top of the boxes indicate significant differences between the subgroups (p < 0.008). The values at the top of the graphs correspond to the Kendall correlation coefficients (τ); the following symbols indicate significant correlations (: p < 0.01, ¶¶: p < 0.001, ¶¶¶: p < 0.0001).

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