Brief Report
Arthritis Care & Research DOI 10.1002/acr.22332
LOCAL-AREA CARTILAGE SEGMENTATION (LACS), A SEMI-AUTOMATED NOVEL METHOD OF MEASURING CARTILAGE LOSS IN KNEE OSTEOARTHRITIS
Jeffrey Duryea1, Tannaz Iranpour-Boroujeni1, Jamie E. Collins1,2, Case Vanwynngaarden3, Ali Guermazi4, Jeffrey N. Katz1, Elena Losina1,2, Ruby Russell, Charles Ratzlaff1
1
Brigham and Women's Hosp. / Harvard Medical School, Boston, MA,
2
Boston University School of Public Health, Boston, MA.
3
Northern Lights Regional Health Care Centre, Alberta, Canada,
4
Boston Univ. Sch. of Med., Boston, MA.
Keywords: Osteoarthritis, Cartilage, Knee, Magnetic Resonance Imaging, Computer Software
Address for correspondence: Jeffrey Duryea, Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115 [email protected]
Word count: 2487 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/acr.22332 © 2014 American College of Rheumatology Received: Aug 27, 2013; Revised: Feb 25, 2014; Accepted: Mar 18, 2014
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Abstract OBJECTIVE: To assess the responsiveness and reader time of a novel semi-automated tool to detect knee cartilage loss over two years in subjects with knee OA.
METHODS: 122 subjects from the OAI Progression Cohort were selected. A reader used the software method to segment cartilage on DESS scans in the medial compartment of the femur from the baseline and 24-month visits. Change in cartilage volume (∆V) was measured at a fixed weight-bearing (WB) location with respect to the three-dimensional coordinate system based on cylindrical coordinates. Change was measured for five regions of varying WB surface area centered on the fixed point. The average change (∆V), the standard deviation (SD) of ∆V and the standardized response mean (SRM) are reported.
RESULTS: The SRM value was –0.52 for the largest region and decreased in magnitude as smaller regions of cartilage were probed. The average evaluation time was less than 20 minutes per knee compartment, split approximately evenly between a technician and a trained reader.
CONCLUSION: The results establish that measurement of cartilage loss in a local region can be done efficiently and that the resultant measures are responsive to loss of cartilage over time. The coordinate system can potentially be used to objectively examine and establish a consistent location for all knees that is most responsive to change in cartilage volume. This technique can provide an objective quantitative measure of cartilage loss rapidly and could substantially reduce study costs for large trials and datasets.
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SIGNIFICANCE AND INNOVATION:
•
Tibiofemoral cartilage loss is an important risk factor for knee OA progression
•
Efficient methods are necessary to provide measures of cartilage loss for very large studies of knee OA such as the Osteoarthritis Initiative.
• We present and validate an innovative software method to measure knee cartilage loss based on a 3D coordinate system, which has a substantially reduced reader time compared to other published methods. The method is sufficiently efficient that it can be used to assess OA progression for very large numbers of subjects.
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Introduction Magnetic resonance imaging (MRI) allows for non-invasive high-resolution visualization of articular cartilage, and is a well-established modality for measuring cartilage volume and thickness. Cartilage status can be assessed with semi-quantitative scoring scales (1,2); however, these methods fundamentally use a qualitative assessment and provide ordinal rather than continuous measures of cartilage status. The cost can be high due largely to reading times and the radiology expertise required.
Quantitative measurement, in contrast, is based on the inherently digital nature of MRI images and provides a continuous score that has the potential to be more responsive than the qualitative methods for monitoring longitudinal change. Software techniques to manually (i.e., ‘tracing’ cartilage borders by hand) or semi-automatically (i.e., using computer algorithms, guided by a reader with anatomic knowledge) segment the cartilage on knee MRI data sets have been used for several years and numerous studies describe their performance (3,4,5,6,7,8,9,10). However, despite their common use, these methods can be laborious due to long reading times, and the attendant expense may limit sample size or power. Two quantitative imaging studies of OA subjects have reported average MRI reading times of 60 to 78 minutes for the tibia (7) and 43 minutes for the femur (8).
A potentially faster approach may be to use a software tool to measure a clinically important local region of the cartilage plate that omits large areas of unaffected cartilage, since readings for an entire cartilage plate or sub-region often include non-diseased locations. It would be of interest to determine if certain local weight-bearing regions of the femoral or tibial cartilage
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plates were more commonly affected in many or all subjects. In principle, measurement of cartilage in focused regions should be more responsive than in the total plates. In fact, there is evidence of improved responsiveness measuring cartilage loss in the central weight-bearing femoral and tibial sub-regions (9,11) as compared with an entire plate or half plate.
Our previous research using three-dimensional (3D) registration has shown that limiting cartilage measurement to localized regions in the knee increases responsiveness substantially (10). However our 3D registration method involved segmentation of the entire half plate and the selection of an indexed point for each knee to define a location of maximum thinning, which required considerable time and additional expertise.
The aim of this study is to determine whether use of a software tool that analyses cartilage morphometry only in localized regions of the knee provides a method that substantially reduces average reader time for analysis while maintaining or improving the responsiveness of the measurement of cartilage loss longitudinally. A method that can do this has the potential to address the enormous workload associated with quantifying cartilage loss in large studies of knee OA, such as the Osteoarthritis Initiative (OAI) ,(http://www.oai.ucsf.edu/).
Methods OAI study sample We used the data from the Osteoarthritis Initiative (OAI), a longitudinal multi-center cohort study of biomarkers and risk factors for the development and progression of knee OA. General exclusion criteria (for all OAI participants) included rheumatoid or inflammatory arthritis,
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bilateral end-stage knee OA and MRI contraindications. A full description of study protocol, design, data overview, and the datasets are available for public access at http://www.oai.ucsf.edu/. The study was HIPAA compliant, all subjects provided informed consent, and the study documentation was reviewed and approved by the local institutional review boards.
Study sample For this study, we selected knees from the 160 Progression Cohort subjects included in Data Set 0.1.1 and Image Releases 0.B.1 and 1.B.1. As described in a separate publication, ten subjects were excluded to provide a sample of subjects that were more likely to progress (12). A single indexed (most diseased) knee was chosen for each subject. After requiring that both the baseline and 24-month visit data were available, a total of 122 subjects remained for inclusion in the study.
MRI protocol MR images were acquired at four OAI clinical centers using dedicated Siemens Trio 3T scanners (Trio, Siemens, Erlangen, Germany). The sagittal 3D dual-echo steady state (DESS) (sagittal, 0.456mm × 0.365 mm, 0.7mm slice thickness, repetition time (TR) 16.5 ms, echo time (TE) 4.7 ms.) pulse sequence was used. Since the DESS protocol is sensitive to cartilage and uses near isotropic voxels, it is ideal for software cartilage segmentation.
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Semi-automated quantification of cartilage volume An initial step, which did not generally require a skilled reader, consisted of segmenting the margin between the bone and cartilage for the femur. A skilled reader made corrections where necessary. This procedure served two purposes: it established one half of the segmented region on each slice, and was used to define a 3D cylindrical coordinate system described below. It was only necessary to produce the bone margin in the medial compartment region at slices where cartilage segmentation occurred, and at a single slice in the center of the lateral compartment used to define the coordinate system.
Cylindrical Coordinate System The coordinate system is a 3D generalization of the two-dimensional Cartesian coordinate system previously used to assess location-specific joint space width (JSW) on digital knee radiographs (13). While, the Cartesian coordinate system defines image points by the two variables x and y, with the cylindrical coordinate system locations in 3D space are defined by three variables: r, θ, and z. (Figure 1a)
Two variables of the cylindrical coordinate system, z and θ, define a region of the femur cartilage (Figure 1a). The z variable relates to the location in the medial-lateral direction from a value of z = 0.0 at the outer edge of the lateral compartment to z = 1.0 at the most medial location of the femoral condyle. As shown in Figure 1b, the θ variable is defined on the sagittal slice at the midpoint between the most lateral and medial slices. The direction indicated by θ = 180° is generally located in the central weight-bearing region of the femur, and as θ increases above 180°, a more posterior region of the femur is probed. Since the r variable corresponds to a
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direction roughly perpendicular to the surface of the femur, it is not useful for establishing a specific location on the femur cartilage plate.
Measurement location All knees were evaluated at a fixed measurement location determined on a subset of 24 subjects. As described in a separate publication (10) a reader identified an “indexed” location of potential thinning on the 24 follow-up scans; this reader did not examine the baseline scan and was not involved in the segmentation step. Based on this, we selected a fixed location in the coordinate system, z0 = 0.8 (medial compartment of knee) and θ0 = 210° (central-posterior femur) to assess cartilage change. The single measurement location was selected by first determining the z and θ location of each index point. The approximate averages of the coordinate values for these 24 subject, z0 = 0.8 and θ0 = 210°, were used to evaluate all 122 subjects in our current study. We selected a maximum range in z of ∆z = 0.1 and in θ of ∆θ = 100°. Using custom software, the reader (CR) segmented the cartilage in the region specified by the coordinate system. In practice the software informed the reader of the slices required to evaluate the cartilage in the full zrange, and the region on each slice to ensure coverage in θ. Readings were performed viewing the baseline and 24 month scans together but blinded to the time point. On average 24.4 (SD = 2.4) slices were segmented per scan. Automated image analysis tools were also provided to increase speed and objectivity including edge detection algorithms that the reader could initiate in areas adjacent to the cartilage margins as well as a method for the reader to indicate areas of denuded cartilage. The automated steps minimized the need for manual segmentation by providing tools to allow the reader to guide the automated software when corrections were required.
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Statistical Analysis In the absence of disease-modifying therapies, we assumed that knee OA worsens structurally over time and that the extent of change in cartilage volume documented over time reflects the performance of imaging-based outcome measures. The change in cartilage volume was calculated between the 24 month and baseline visits for different values of ∆z and ∆θ, effectively probing a range of surface areas around the fixed point, z0 = 0.8 and θ0 = 210°. (Figure 1a) Responsiveness was measured by calculating the average and standard deviation of the volume change, and the standardized response mean (SRM) (average ∆V /standard deviation of ∆V). The inter and intra-reader reproducibility was measured using intraclass correlation coefficients (ICCs) on a random subset of 12 (3 each of KL0, KL1, KL2, and KL3) baseline scans, We also report the average reader time per scan.
Results The baseline characteristics are given in Table 1. The average assessment time, for the skilled reader, was 10.5 (SD 3.4 minutes) minutes per knee compartment. When combined with time necessary for the unskilled reader to perform initial segmentation, the total evaluation time was less than 20 minutes.
Responsiveness and reproducibility results are given in Table 2. In general, a greater surface area (larger values for ∆z and ∆θ) yielded improved responsiveness compared to a smaller area. The most responsive measurement (SRM = -0.52) was for ∆z = 0.10, ∆θ = 100°, while the least responsive region (SRM = -0.32) was for ∆z = 0.02, ∆θ = 20°. The reproducibility was
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uniformly good (ICC > 0.9) with some loss evident for the largest region (∆z = 0.10). In general, the responsiveness was relatively constant from the regions defined as (∆z = 0.08, ∆θ = 80°) to (∆z = 0.04, ∆θ = 40°) and dropped substantially for the smallest region tested (∆z = 0.02, ∆θ = 20°).
Discussion: The study shows that a responsive measure of cartilage loss in a single compartment can be made with approximately 10 minutes of expert reader time and less than 20 minutes total reader time. This method has two potential advantages. Segmentation in a localized fixed location has the potential to be more responsive if the region of known cartilage thinning is selected. Secondly, cartilage segmentation is substantially faster since reader attention is limited to a relatively small region.
A relatively high responsiveness is maintained even for a much smaller measurement region (∆z = 0.04, ∆θ = 40°). Since the expert reader time needed to segment the smaller area of cartilage would be substantially lower than 10 minutes, an even faster method is possible if some loss of performance is acceptable. The results demonstrate that probing a larger area can improve the responsiveness but the gain is marginal as the area is increased above the smallest region we defined. The trade-offs between reader time and responsiveness merit further study.
Recently, standardized subregions have been defined and used by some researchers in studies involving cartilage segmentation (14). Since these regions are also based on anatomical landmarks, it is possible to approximate the standard subregions using our cylindrical coordinate
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system approach. Our technique, however, offers some advantages over the conventionally defined subregions. Since it is not necessary to segment the entire cartilage plate, the method is fast. Additionally it offers substantially more flexibility for selecting regions to evaluate, which ultimately may lead to improvement in responsiveness over the standard subregions since it can target the exact location of cartilage loss for a given subject.
Comparison to other studies is difficult if the subjects and time points are not identical. Hunter et al (12) reported an SRM value of –0.34 for a quantitative MRI cartilage volume change assessed over 12 months at a similar anatomical location, the central medial femur cartilage region. These authors used the same subjects as our study. An exact comparison is not possible since our study has a longer follow-up time and excluded some subjects who were not able to participate at 24 months. Buck et al (15) have addressed the issue of large areas of unaffected cartilage loss using an ordered values approach, which prioritized regions by amount of volume change over time irrespective of the location of the sub-region in which it occurred. However, this method still required measurement of many areas of no or small change in cartilage volume.
One limitation of our study is that it probes only a single region of the femur and may miss additional areas of thinning. To address this, the method could be easily adapted so that multiple regions for each knee could be assessed in a manner similar to radiographic location-specific JSW, but generalized to 3D. While our method currently does not evaluate the full extent of articulate cartilage, its speed may allow for use in studies like the OAI involving very large numbers of subjects, where alternative methods may not be practical. For example, in the first five time points the OAI data include over 38,000 MRI images sets using the DESS protocol.
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A second limitation is that we have not addressed measuring cartilage loss in the tibia or patella. In theory these plates can be assessed with an analogous approach but with a different coordinate system that has yet to be defined and validated. Additionally, for this study we did not perform an analysis of the lateral compartment. A total reader time of 20 minutes marks a significant improvement over our previous approach (8) but will have to be further improved for use in very large studies. For this initial study, some additional quality assurance (QA) time was utilized for the reader to review selected cases with a radiologist (CV). The time required for QA is substantially reduced or eliminated as the reader is better trained. Providing measurements of the tibia, patella, and additional regions of the femur, as well as other structures such as BMLs, and osteophytes will add to the reader time. Ultimately we expect to further develop these methods so that a comprehensive and fully quantitative assessment of a single knee scan can be made in 10 minutes or less of skilled reader time.
Future Research
The coordinate system has the potential to be useful for mapping and quantifying other relevant structures on MRI data sets such as bone marrow lesions (BMLs) and osteophytes. Examining relationships between clinical symptoms and precise location of these structures on a robust and reproducible framework may yield improved understanding of their relevance to OA incidence and progression. With this method, any structure on the image can be described in terms of its volume, gray scale intensity, and location relative to the knee using the parameters of the
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coordinate system: z, θ, and r. The fact that all of these parameters are fully quantitative continuous variables should facilitate statistical analysis.
Conclusions: We have documented a novel and fast method to quantify cartilage loss on knee MRI scans that is based on a 3D cylindrical coordinate system. The method provides a responsive measure of change with less than 20 minutes of reader time per scan, however even faster times are possible if some loss of responsiveness is acceptable.
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Acknowledgements: Quinley Miao for her invaluable assistance. Author Contributions: Study conception and design - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi Acquisition of data – Duryea, Ratzlaff, Russell Analysis and interpretation of data – Ratzlaff, Duryea, Katz, Losina, Collins, VanWynaarden, Guermazi Drafting the article or revising it critically for important intellectual content - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi, VanWynaarden, Russell Final approval of the version of the article to be published - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi, VanWynaarden, Russell J Duryea ([email protected]) takes responsibility for the integrity of the work as a whole. Role of funding source: This study was supported by the NIH/NIAMS (R01AR056664). Additional support for Dr. Ratzlaff was provided by the Canadian Institutes of Health Research and Michael Smith Foundation for Health Research, and for Drs. Katz, Losina and Jamie Collins by NIH/NIAMS (T32AR055885, K24AR057827, P60AR047782).The OAI is a public–private partnership comprised of 5 contracts (N01-AR-2-2258, N01-AR-2-2259, N01-AR-2-2260, N01AR-2-2261, and N01-AR-2-2262) funded by the NIH, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Pfizer, Novartis Pharmaceuticals, Merck Research Laboratories, and GlaxoSmithKline. Private sector funding for the OAI is managed by the Foundation for the NIH.
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Conflict of interest statement: Ali Guermazi is President of BICL, LLC and is Consultant to AstraZeneca, Genzyme and MerckSerono. Other authors declare that they have no conflicting interests. ..
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References: 1. Hunter DJ, GH Lo, D Gale, AJ Grainger, A Guermazi and PG Conaghan. The reliability of a new scoring system for knee osteoarthritis mri and the validity of bone marrow lesion assessment: Bloks (boston leeds osteoarthritis knee score). Ann Rheum Dis 2008;67:206-11. 2. Hunter DJ, A Guermazi, GH Lo, AJ Grainger, PG Conaghan, RM Boudreau and FW Roemer. Evolution of semi-quantitative whole joint assessment of knee oa: Moaks (mri osteoarthritis knee score). Osteoarthritis Cartilage 2011;19:990-1002. 3. Piplani MA, DG Disler, TR McCauley, TJ Holmes and JP Cousins. Articular cartilage volume in the knee: Semiautomated determination from three-dimensional reformations of mr images. Radiology 1996;198:855-9. 4. Stammberger T, F Eckstein, KH Englmeier and M Reiser. Determination of 3d cartilage thickness data from mr imaging: Computational method and reproducibility in the living. Magn Reson Med 1999;41:529-36. 5. Eckstein F, HC Charles, RJ Buck, VB Kraus, AE Remmers, M Hudelmaier, W Wirth and JL Evelhoch. Accuracy and precision of quantitative assessment of cartilage morphology by magnetic resonance imaging at 3.0t. Arthritis Rheum 2005;52:3132-6. 6. Folkesson J, E Dam, OF Olsen, P Pettersen and C Christiansen. Automatic segmentation of the articular cartilage in knee mri using a hierarchical multi-class classification scheme. Med Image Comput Comput Assist Interv 2005;8:327-34. 7. McWalter EJ, W Wirth, M Siebert, RM von Eisenhart-Rothe, M Hudelmaier, DR Wilson and F Eckstein. Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images. Osteoarthritis Cartilage 2005;13:48-53.
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8. Duryea J, G Neumann, MH Brem, W Koh, F Noorbakhsh, RD Jackson, J Yu, CB Eaton and P Lang. Novel fast semi-automated software to segment cartilage for knee mr acquisitions. Osteoarthritis Cartilage 2007;15:487-92. 9. Pelletier JP, JP Raynauld, MJ Berthiaume, F Abram, D Choquette, B Haraoui, JF Beary, GA Cline, JM Meyer and J Martel-Pelletier. Risk factors associated with the loss of cartilage volume on weight-bearing areas in knee osteoarthritis patients assessed by quantitative magnetic resonance imaging: A longitudinal study. Arthritis Res Ther 2007;9:R74. 10. Iranpour-Boroujeni T, A Watanabe, R Bashtar, H Yoshioka and J Duryea. Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: Analysis of longitudinal data from the osteoarthritis initiative (oai). Osteoarthritis Cartilage 2011;19:309-14. 11. Wirth W, MP Hellio Le Graverand, BT Wyman, S Maschek, M Hudelmaier, W Hitzl, M Nevitt and F Eckstein. Regional analysis of femorotibial cartilage loss in a subsample from the osteoarthritis initiative progression subcohort. Osteoarthritis Cartilage 2009;17:291-7. 12. Hunter DJ, J Niu, Y Zhang, S Totterman, J Tamez, C Dabrowski, R Davies, MP Le Graverand, M Luchi, Y Tymofyeyev and CR Beals. Change in cartilage morphometry: A sample of the progression cohort of the osteoarthritis initiative. Ann Rheum Dis 2009;68:349-56. 13. Duryea J, G Neumann, J Niu, S Totterman, J Tamez, C Dabrowski, MP Le Graverand, M Luchi, CR Beals and DJ Hunter. Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: Analysis of longitudinal data from the osteoarthritis initiative. Arthritis Care Res (Hoboken) 2010;62:932-7. 14. Eckstein F, G Ateshian, R Burgkart, D Burstein, F Cicuttini, B Dardzinski, M Gray, TM Link, S Majumdar, T Mosher, C Peterfy, S Totterman, J Waterton, CS Winalski and D Felson.
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Proposal for a nomenclature for magnetic resonance imaging based measures of articular cartilage in osteoarthritis. Osteoarthritis Cartilage 2006;14:974-83. 15. Buck RJ, BT Wyman, MP Le Graverand, M Hudelmaier, W Wirth and F Eckstein. Does the use of ordered values of subregional change in cartilage thickness improve the detection of disease progression in longitudinal studies of osteoarthritis? Arthritis Rheum 2009;61:917-24.
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Figure Legends
Figure 1a. Cylindrical coordinate system used to assess 3D cartilage changes in the femur. Three variables, z, θ and r are used to define any point on the 3D image. The origin is located in the lateral compartment as shown and the z-axis is defined to pass through the center of the circular portion of the femoral condyles. The variable r represents the distance from the z-axis to any point. The θ variable, defined in Figure 1c, is the angle from a fixed direction perpendicular to the z-axis. Once the coordinate system is established, any point on the image can be defined in terms of quantities r, θ, and z. A region (lightly shaded) of cartilage for segmentation is defined by a center point (z0 = 0.8, θ0 = 210°) and a range in a θ and z given by ∆z = 0.10 and ∆θ = 100°.
Figure 1b. Definition of the θ = 0° direction on the center sagittal slice of the knee, defined as the slice at the midpoint between the most lateral and medial slices. The θ = 0° direction is perpendicular to the z-axis, which is approximately perpendicular to the plane of the image. On this image the variable r projects in a radial direction with the location of the z-axis as the center.
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FIGURES:
Figure 1a. Cylindrical coordinate system used to assess 3D cartilage changes in the femur. Three variables, z, θ and r are used to define any point on the 3D image. The origin is located in the lateral compartment as shown and the z-axis is defined to pass through the center of the circular portion of the femoral condyles. The variable r represents the distance from the z-axis to any point. The θ variable, defined in Figure 1c, is the angle from a fixed direction perpendicular to the z-axis. Once the coordinate system is established, any point on the image can be defined in terms of quantities r, θ, and z. A region (lightly shaded) of cartilage for segmentation is defined by a center point (z0 = 0.8, θ0 = 210°) and a range in a θ and z given by ∆z = 0.10 and ∆θ = 100°.
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Figure 1b. Definition of the θ = 0° direction on the center sagittal slice of the knee, defined as the slice at the midpoint between the most lateral and medial slices. The θ = 0° direction is perpendicular to the z-axis, which is approximately perpendicular to the plane of the image. On this image the variable r projects in a radial direction with the location of the z-axis as the center.
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Table 1 Characteristics of the study subjects for the study (N = 122). N = 122 Age (SD)
60.6 (10.2) years
Body-mass Index (SD)
30.2 (4.7)
Gender Male
62 (50.8%)
Female
60 (49.2)
Race White
101 (82.8%)
African American
18 (14.8%)
Other
3 (2.5%)
KL Grade 0
8 (6.6%)
1
10 (8.2%)
2
51 (41.8%)
3
42 (34.4%)
4
11 (9.0%)
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Table 2. Responsiveness results (N = 122). The change in cartilage volume ∆V and SRM values are provided as a function of ∆z and ∆θ. The center point is located at z0 = 0.8, θ0 = 210°. Reproducibility results are based on a subset of 12 subjects.
∆z
∆θ
∆V (mm3)
SD (∆V) (mm3)
SRM
Intra-reader ICC
Inter-reader ICC
0.10
100°
-46.2
89.5
-0.52
0.93
0.91
0.08
80°
-30.8
62.8
-0.49
0.99
0.98
0.06
60°
-17.9
40.6
-0.44
0.98
0.97
0.04
40°
-9.2
20.4
-0.45
0.98
0.98
0.02
20°
-2.3
7.2
-0.32
0.95
0.99
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Arthritis Care & Research DOI 10.1002/acr.22332
LOCAL-AREA CARTILAGE SEGMENTATION (LACS), A SEMI-AUTOMATED NOVEL METHOD OF MEASURING CARTILAGE LOSS IN KNEE OSTEOARTHRITIS
Jeffrey Duryea1, Tannaz Iranpour-Boroujeni1, Jamie E. Collins1,2, Case Vanwynngaarden3, Ali Guermazi4, Jeffrey N. Katz1, Elena Losina1,2, Ruby Russell, Charles Ratzlaff1
1
Brigham and Women's Hosp. / Harvard Medical School, Boston, MA,
2
Boston University School of Public Health, Boston, MA.
3
Northern Lights Regional Health Care Centre, Alberta, Canada,
4
Boston Univ. Sch. of Med., Boston, MA.
Keywords: Osteoarthritis, Cartilage, Knee, Magnetic Resonance Imaging, Computer Software
Address for correspondence: Jeffrey Duryea, Radiology, Brigham and Women’s Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115 [email protected]
Word count: 2487 This article has been accepted for publication and undergone full peer review but has not been through the copyediting, typesetting, pagination and proofreading process which may lead to differences between this version and the Version of Record. Please cite this article as an ‘Accepted Article’, doi: 10.1002/acr.22332 © 2014 American College of Rheumatology Received: Aug 27, 2013; Revised: Feb 25, 2014; Accepted: Mar 18, 2014
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Abstract OBJECTIVE: To assess the responsiveness and reader time of a novel semi-automated tool to detect knee cartilage loss over two years in subjects with knee OA.
METHODS: 122 subjects from the OAI Progression Cohort were selected. A reader used the software method to segment cartilage on DESS scans in the medial compartment of the femur from the baseline and 24-month visits. Change in cartilage volume (∆V) was measured at a fixed weight-bearing (WB) location with respect to the three-dimensional coordinate system based on cylindrical coordinates. Change was measured for five regions of varying WB surface area centered on the fixed point. The average change (∆V), the standard deviation (SD) of ∆V and the standardized response mean (SRM) are reported.
RESULTS: The SRM value was –0.52 for the largest region and decreased in magnitude as smaller regions of cartilage were probed. The average evaluation time was less than 20 minutes per knee compartment, split approximately evenly between a technician and a trained reader.
CONCLUSION: The results establish that measurement of cartilage loss in a local region can be done efficiently and that the resultant measures are responsive to loss of cartilage over time. The coordinate system can potentially be used to objectively examine and establish a consistent location for all knees that is most responsive to change in cartilage volume. This technique can provide an objective quantitative measure of cartilage loss rapidly and could substantially reduce study costs for large trials and datasets.
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SIGNIFICANCE AND INNOVATION:
•
Tibiofemoral cartilage loss is an important risk factor for knee OA progression
•
Efficient methods are necessary to provide measures of cartilage loss for very large studies of knee OA such as the Osteoarthritis Initiative.
• We present and validate an innovative software method to measure knee cartilage loss based on a 3D coordinate system, which has a substantially reduced reader time compared to other published methods. The method is sufficiently efficient that it can be used to assess OA progression for very large numbers of subjects.
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Introduction Magnetic resonance imaging (MRI) allows for non-invasive high-resolution visualization of articular cartilage, and is a well-established modality for measuring cartilage volume and thickness. Cartilage status can be assessed with semi-quantitative scoring scales (1,2); however, these methods fundamentally use a qualitative assessment and provide ordinal rather than continuous measures of cartilage status. The cost can be high due largely to reading times and the radiology expertise required.
Quantitative measurement, in contrast, is based on the inherently digital nature of MRI images and provides a continuous score that has the potential to be more responsive than the qualitative methods for monitoring longitudinal change. Software techniques to manually (i.e., ‘tracing’ cartilage borders by hand) or semi-automatically (i.e., using computer algorithms, guided by a reader with anatomic knowledge) segment the cartilage on knee MRI data sets have been used for several years and numerous studies describe their performance (3,4,5,6,7,8,9,10). However, despite their common use, these methods can be laborious due to long reading times, and the attendant expense may limit sample size or power. Two quantitative imaging studies of OA subjects have reported average MRI reading times of 60 to 78 minutes for the tibia (7) and 43 minutes for the femur (8).
A potentially faster approach may be to use a software tool to measure a clinically important local region of the cartilage plate that omits large areas of unaffected cartilage, since readings for an entire cartilage plate or sub-region often include non-diseased locations. It would be of interest to determine if certain local weight-bearing regions of the femoral or tibial cartilage
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plates were more commonly affected in many or all subjects. In principle, measurement of cartilage in focused regions should be more responsive than in the total plates. In fact, there is evidence of improved responsiveness measuring cartilage loss in the central weight-bearing femoral and tibial sub-regions (9,11) as compared with an entire plate or half plate.
Our previous research using three-dimensional (3D) registration has shown that limiting cartilage measurement to localized regions in the knee increases responsiveness substantially (10). However our 3D registration method involved segmentation of the entire half plate and the selection of an indexed point for each knee to define a location of maximum thinning, which required considerable time and additional expertise.
The aim of this study is to determine whether use of a software tool that analyses cartilage morphometry only in localized regions of the knee provides a method that substantially reduces average reader time for analysis while maintaining or improving the responsiveness of the measurement of cartilage loss longitudinally. A method that can do this has the potential to address the enormous workload associated with quantifying cartilage loss in large studies of knee OA, such as the Osteoarthritis Initiative (OAI) ,(http://www.oai.ucsf.edu/).
Methods OAI study sample We used the data from the Osteoarthritis Initiative (OAI), a longitudinal multi-center cohort study of biomarkers and risk factors for the development and progression of knee OA. General exclusion criteria (for all OAI participants) included rheumatoid or inflammatory arthritis,
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bilateral end-stage knee OA and MRI contraindications. A full description of study protocol, design, data overview, and the datasets are available for public access at http://www.oai.ucsf.edu/. The study was HIPAA compliant, all subjects provided informed consent, and the study documentation was reviewed and approved by the local institutional review boards.
Study sample For this study, we selected knees from the 160 Progression Cohort subjects included in Data Set 0.1.1 and Image Releases 0.B.1 and 1.B.1. As described in a separate publication, ten subjects were excluded to provide a sample of subjects that were more likely to progress (12). A single indexed (most diseased) knee was chosen for each subject. After requiring that both the baseline and 24-month visit data were available, a total of 122 subjects remained for inclusion in the study.
MRI protocol MR images were acquired at four OAI clinical centers using dedicated Siemens Trio 3T scanners (Trio, Siemens, Erlangen, Germany). The sagittal 3D dual-echo steady state (DESS) (sagittal, 0.456mm × 0.365 mm, 0.7mm slice thickness, repetition time (TR) 16.5 ms, echo time (TE) 4.7 ms.) pulse sequence was used. Since the DESS protocol is sensitive to cartilage and uses near isotropic voxels, it is ideal for software cartilage segmentation.
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Semi-automated quantification of cartilage volume An initial step, which did not generally require a skilled reader, consisted of segmenting the margin between the bone and cartilage for the femur. A skilled reader made corrections where necessary. This procedure served two purposes: it established one half of the segmented region on each slice, and was used to define a 3D cylindrical coordinate system described below. It was only necessary to produce the bone margin in the medial compartment region at slices where cartilage segmentation occurred, and at a single slice in the center of the lateral compartment used to define the coordinate system.
Cylindrical Coordinate System The coordinate system is a 3D generalization of the two-dimensional Cartesian coordinate system previously used to assess location-specific joint space width (JSW) on digital knee radiographs (13). While, the Cartesian coordinate system defines image points by the two variables x and y, with the cylindrical coordinate system locations in 3D space are defined by three variables: r, θ, and z. (Figure 1a)
Two variables of the cylindrical coordinate system, z and θ, define a region of the femur cartilage (Figure 1a). The z variable relates to the location in the medial-lateral direction from a value of z = 0.0 at the outer edge of the lateral compartment to z = 1.0 at the most medial location of the femoral condyle. As shown in Figure 1b, the θ variable is defined on the sagittal slice at the midpoint between the most lateral and medial slices. The direction indicated by θ = 180° is generally located in the central weight-bearing region of the femur, and as θ increases above 180°, a more posterior region of the femur is probed. Since the r variable corresponds to a
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direction roughly perpendicular to the surface of the femur, it is not useful for establishing a specific location on the femur cartilage plate.
Measurement location All knees were evaluated at a fixed measurement location determined on a subset of 24 subjects. As described in a separate publication (10) a reader identified an “indexed” location of potential thinning on the 24 follow-up scans; this reader did not examine the baseline scan and was not involved in the segmentation step. Based on this, we selected a fixed location in the coordinate system, z0 = 0.8 (medial compartment of knee) and θ0 = 210° (central-posterior femur) to assess cartilage change. The single measurement location was selected by first determining the z and θ location of each index point. The approximate averages of the coordinate values for these 24 subject, z0 = 0.8 and θ0 = 210°, were used to evaluate all 122 subjects in our current study. We selected a maximum range in z of ∆z = 0.1 and in θ of ∆θ = 100°. Using custom software, the reader (CR) segmented the cartilage in the region specified by the coordinate system. In practice the software informed the reader of the slices required to evaluate the cartilage in the full zrange, and the region on each slice to ensure coverage in θ. Readings were performed viewing the baseline and 24 month scans together but blinded to the time point. On average 24.4 (SD = 2.4) slices were segmented per scan. Automated image analysis tools were also provided to increase speed and objectivity including edge detection algorithms that the reader could initiate in areas adjacent to the cartilage margins as well as a method for the reader to indicate areas of denuded cartilage. The automated steps minimized the need for manual segmentation by providing tools to allow the reader to guide the automated software when corrections were required.
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Statistical Analysis In the absence of disease-modifying therapies, we assumed that knee OA worsens structurally over time and that the extent of change in cartilage volume documented over time reflects the performance of imaging-based outcome measures. The change in cartilage volume was calculated between the 24 month and baseline visits for different values of ∆z and ∆θ, effectively probing a range of surface areas around the fixed point, z0 = 0.8 and θ0 = 210°. (Figure 1a) Responsiveness was measured by calculating the average and standard deviation of the volume change, and the standardized response mean (SRM) (average ∆V /standard deviation of ∆V). The inter and intra-reader reproducibility was measured using intraclass correlation coefficients (ICCs) on a random subset of 12 (3 each of KL0, KL1, KL2, and KL3) baseline scans, We also report the average reader time per scan.
Results The baseline characteristics are given in Table 1. The average assessment time, for the skilled reader, was 10.5 (SD 3.4 minutes) minutes per knee compartment. When combined with time necessary for the unskilled reader to perform initial segmentation, the total evaluation time was less than 20 minutes.
Responsiveness and reproducibility results are given in Table 2. In general, a greater surface area (larger values for ∆z and ∆θ) yielded improved responsiveness compared to a smaller area. The most responsive measurement (SRM = -0.52) was for ∆z = 0.10, ∆θ = 100°, while the least responsive region (SRM = -0.32) was for ∆z = 0.02, ∆θ = 20°. The reproducibility was
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uniformly good (ICC > 0.9) with some loss evident for the largest region (∆z = 0.10). In general, the responsiveness was relatively constant from the regions defined as (∆z = 0.08, ∆θ = 80°) to (∆z = 0.04, ∆θ = 40°) and dropped substantially for the smallest region tested (∆z = 0.02, ∆θ = 20°).
Discussion: The study shows that a responsive measure of cartilage loss in a single compartment can be made with approximately 10 minutes of expert reader time and less than 20 minutes total reader time. This method has two potential advantages. Segmentation in a localized fixed location has the potential to be more responsive if the region of known cartilage thinning is selected. Secondly, cartilage segmentation is substantially faster since reader attention is limited to a relatively small region.
A relatively high responsiveness is maintained even for a much smaller measurement region (∆z = 0.04, ∆θ = 40°). Since the expert reader time needed to segment the smaller area of cartilage would be substantially lower than 10 minutes, an even faster method is possible if some loss of performance is acceptable. The results demonstrate that probing a larger area can improve the responsiveness but the gain is marginal as the area is increased above the smallest region we defined. The trade-offs between reader time and responsiveness merit further study.
Recently, standardized subregions have been defined and used by some researchers in studies involving cartilage segmentation (14). Since these regions are also based on anatomical landmarks, it is possible to approximate the standard subregions using our cylindrical coordinate
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system approach. Our technique, however, offers some advantages over the conventionally defined subregions. Since it is not necessary to segment the entire cartilage plate, the method is fast. Additionally it offers substantially more flexibility for selecting regions to evaluate, which ultimately may lead to improvement in responsiveness over the standard subregions since it can target the exact location of cartilage loss for a given subject.
Comparison to other studies is difficult if the subjects and time points are not identical. Hunter et al (12) reported an SRM value of –0.34 for a quantitative MRI cartilage volume change assessed over 12 months at a similar anatomical location, the central medial femur cartilage region. These authors used the same subjects as our study. An exact comparison is not possible since our study has a longer follow-up time and excluded some subjects who were not able to participate at 24 months. Buck et al (15) have addressed the issue of large areas of unaffected cartilage loss using an ordered values approach, which prioritized regions by amount of volume change over time irrespective of the location of the sub-region in which it occurred. However, this method still required measurement of many areas of no or small change in cartilage volume.
One limitation of our study is that it probes only a single region of the femur and may miss additional areas of thinning. To address this, the method could be easily adapted so that multiple regions for each knee could be assessed in a manner similar to radiographic location-specific JSW, but generalized to 3D. While our method currently does not evaluate the full extent of articulate cartilage, its speed may allow for use in studies like the OAI involving very large numbers of subjects, where alternative methods may not be practical. For example, in the first five time points the OAI data include over 38,000 MRI images sets using the DESS protocol.
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A second limitation is that we have not addressed measuring cartilage loss in the tibia or patella. In theory these plates can be assessed with an analogous approach but with a different coordinate system that has yet to be defined and validated. Additionally, for this study we did not perform an analysis of the lateral compartment. A total reader time of 20 minutes marks a significant improvement over our previous approach (8) but will have to be further improved for use in very large studies. For this initial study, some additional quality assurance (QA) time was utilized for the reader to review selected cases with a radiologist (CV). The time required for QA is substantially reduced or eliminated as the reader is better trained. Providing measurements of the tibia, patella, and additional regions of the femur, as well as other structures such as BMLs, and osteophytes will add to the reader time. Ultimately we expect to further develop these methods so that a comprehensive and fully quantitative assessment of a single knee scan can be made in 10 minutes or less of skilled reader time.
Future Research
The coordinate system has the potential to be useful for mapping and quantifying other relevant structures on MRI data sets such as bone marrow lesions (BMLs) and osteophytes. Examining relationships between clinical symptoms and precise location of these structures on a robust and reproducible framework may yield improved understanding of their relevance to OA incidence and progression. With this method, any structure on the image can be described in terms of its volume, gray scale intensity, and location relative to the knee using the parameters of the
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coordinate system: z, θ, and r. The fact that all of these parameters are fully quantitative continuous variables should facilitate statistical analysis.
Conclusions: We have documented a novel and fast method to quantify cartilage loss on knee MRI scans that is based on a 3D cylindrical coordinate system. The method provides a responsive measure of change with less than 20 minutes of reader time per scan, however even faster times are possible if some loss of responsiveness is acceptable.
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Acknowledgements: Quinley Miao for her invaluable assistance. Author Contributions: Study conception and design - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi Acquisition of data – Duryea, Ratzlaff, Russell Analysis and interpretation of data – Ratzlaff, Duryea, Katz, Losina, Collins, VanWynaarden, Guermazi Drafting the article or revising it critically for important intellectual content - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi, VanWynaarden, Russell Final approval of the version of the article to be published - Ratzlaff, Duryea, Katz, Losina, Collins, Guermazi, VanWynaarden, Russell J Duryea ([email protected]) takes responsibility for the integrity of the work as a whole. Role of funding source: This study was supported by the NIH/NIAMS (R01AR056664). Additional support for Dr. Ratzlaff was provided by the Canadian Institutes of Health Research and Michael Smith Foundation for Health Research, and for Drs. Katz, Losina and Jamie Collins by NIH/NIAMS (T32AR055885, K24AR057827, P60AR047782).The OAI is a public–private partnership comprised of 5 contracts (N01-AR-2-2258, N01-AR-2-2259, N01-AR-2-2260, N01AR-2-2261, and N01-AR-2-2262) funded by the NIH, a branch of the Department of Health and Human Services, and conducted by the OAI Study Investigators. Private funding partners include Pfizer, Novartis Pharmaceuticals, Merck Research Laboratories, and GlaxoSmithKline. Private sector funding for the OAI is managed by the Foundation for the NIH.
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Conflict of interest statement: Ali Guermazi is President of BICL, LLC and is Consultant to AstraZeneca, Genzyme and MerckSerono. Other authors declare that they have no conflicting interests. ..
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References: 1. Hunter DJ, GH Lo, D Gale, AJ Grainger, A Guermazi and PG Conaghan. The reliability of a new scoring system for knee osteoarthritis mri and the validity of bone marrow lesion assessment: Bloks (boston leeds osteoarthritis knee score). Ann Rheum Dis 2008;67:206-11. 2. Hunter DJ, A Guermazi, GH Lo, AJ Grainger, PG Conaghan, RM Boudreau and FW Roemer. Evolution of semi-quantitative whole joint assessment of knee oa: Moaks (mri osteoarthritis knee score). Osteoarthritis Cartilage 2011;19:990-1002. 3. Piplani MA, DG Disler, TR McCauley, TJ Holmes and JP Cousins. Articular cartilage volume in the knee: Semiautomated determination from three-dimensional reformations of mr images. Radiology 1996;198:855-9. 4. Stammberger T, F Eckstein, KH Englmeier and M Reiser. Determination of 3d cartilage thickness data from mr imaging: Computational method and reproducibility in the living. Magn Reson Med 1999;41:529-36. 5. Eckstein F, HC Charles, RJ Buck, VB Kraus, AE Remmers, M Hudelmaier, W Wirth and JL Evelhoch. Accuracy and precision of quantitative assessment of cartilage morphology by magnetic resonance imaging at 3.0t. Arthritis Rheum 2005;52:3132-6. 6. Folkesson J, E Dam, OF Olsen, P Pettersen and C Christiansen. Automatic segmentation of the articular cartilage in knee mri using a hierarchical multi-class classification scheme. Med Image Comput Comput Assist Interv 2005;8:327-34. 7. McWalter EJ, W Wirth, M Siebert, RM von Eisenhart-Rothe, M Hudelmaier, DR Wilson and F Eckstein. Use of novel interactive input devices for segmentation of articular cartilage from magnetic resonance images. Osteoarthritis Cartilage 2005;13:48-53.
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8. Duryea J, G Neumann, MH Brem, W Koh, F Noorbakhsh, RD Jackson, J Yu, CB Eaton and P Lang. Novel fast semi-automated software to segment cartilage for knee mr acquisitions. Osteoarthritis Cartilage 2007;15:487-92. 9. Pelletier JP, JP Raynauld, MJ Berthiaume, F Abram, D Choquette, B Haraoui, JF Beary, GA Cline, JM Meyer and J Martel-Pelletier. Risk factors associated with the loss of cartilage volume on weight-bearing areas in knee osteoarthritis patients assessed by quantitative magnetic resonance imaging: A longitudinal study. Arthritis Res Ther 2007;9:R74. 10. Iranpour-Boroujeni T, A Watanabe, R Bashtar, H Yoshioka and J Duryea. Quantification of cartilage loss in local regions of knee joints using semi-automated segmentation software: Analysis of longitudinal data from the osteoarthritis initiative (oai). Osteoarthritis Cartilage 2011;19:309-14. 11. Wirth W, MP Hellio Le Graverand, BT Wyman, S Maschek, M Hudelmaier, W Hitzl, M Nevitt and F Eckstein. Regional analysis of femorotibial cartilage loss in a subsample from the osteoarthritis initiative progression subcohort. Osteoarthritis Cartilage 2009;17:291-7. 12. Hunter DJ, J Niu, Y Zhang, S Totterman, J Tamez, C Dabrowski, R Davies, MP Le Graverand, M Luchi, Y Tymofyeyev and CR Beals. Change in cartilage morphometry: A sample of the progression cohort of the osteoarthritis initiative. Ann Rheum Dis 2009;68:349-56. 13. Duryea J, G Neumann, J Niu, S Totterman, J Tamez, C Dabrowski, MP Le Graverand, M Luchi, CR Beals and DJ Hunter. Comparison of radiographic joint space width with magnetic resonance imaging cartilage morphometry: Analysis of longitudinal data from the osteoarthritis initiative. Arthritis Care Res (Hoboken) 2010;62:932-7. 14. Eckstein F, G Ateshian, R Burgkart, D Burstein, F Cicuttini, B Dardzinski, M Gray, TM Link, S Majumdar, T Mosher, C Peterfy, S Totterman, J Waterton, CS Winalski and D Felson.
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Proposal for a nomenclature for magnetic resonance imaging based measures of articular cartilage in osteoarthritis. Osteoarthritis Cartilage 2006;14:974-83. 15. Buck RJ, BT Wyman, MP Le Graverand, M Hudelmaier, W Wirth and F Eckstein. Does the use of ordered values of subregional change in cartilage thickness improve the detection of disease progression in longitudinal studies of osteoarthritis? Arthritis Rheum 2009;61:917-24.
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Figure Legends
Figure 1a. Cylindrical coordinate system used to assess 3D cartilage changes in the femur. Three variables, z, θ and r are used to define any point on the 3D image. The origin is located in the lateral compartment as shown and the z-axis is defined to pass through the center of the circular portion of the femoral condyles. The variable r represents the distance from the z-axis to any point. The θ variable, defined in Figure 1c, is the angle from a fixed direction perpendicular to the z-axis. Once the coordinate system is established, any point on the image can be defined in terms of quantities r, θ, and z. A region (lightly shaded) of cartilage for segmentation is defined by a center point (z0 = 0.8, θ0 = 210°) and a range in a θ and z given by ∆z = 0.10 and ∆θ = 100°.
Figure 1b. Definition of the θ = 0° direction on the center sagittal slice of the knee, defined as the slice at the midpoint between the most lateral and medial slices. The θ = 0° direction is perpendicular to the z-axis, which is approximately perpendicular to the plane of the image. On this image the variable r projects in a radial direction with the location of the z-axis as the center.
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FIGURES:
Figure 1a. Cylindrical coordinate system used to assess 3D cartilage changes in the femur. Three variables, z, θ and r are used to define any point on the 3D image. The origin is located in the lateral compartment as shown and the z-axis is defined to pass through the center of the circular portion of the femoral condyles. The variable r represents the distance from the z-axis to any point. The θ variable, defined in Figure 1c, is the angle from a fixed direction perpendicular to the z-axis. Once the coordinate system is established, any point on the image can be defined in terms of quantities r, θ, and z. A region (lightly shaded) of cartilage for segmentation is defined by a center point (z0 = 0.8, θ0 = 210°) and a range in a θ and z given by ∆z = 0.10 and ∆θ = 100°.
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Figure 1b. Definition of the θ = 0° direction on the center sagittal slice of the knee, defined as the slice at the midpoint between the most lateral and medial slices. The θ = 0° direction is perpendicular to the z-axis, which is approximately perpendicular to the plane of the image. On this image the variable r projects in a radial direction with the location of the z-axis as the center.
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Table 1 Characteristics of the study subjects for the study (N = 122). N = 122 Age (SD)
60.6 (10.2) years
Body-mass Index (SD)
30.2 (4.7)
Gender Male
62 (50.8%)
Female
60 (49.2)
Race White
101 (82.8%)
African American
18 (14.8%)
Other
3 (2.5%)
KL Grade 0
8 (6.6%)
1
10 (8.2%)
2
51 (41.8%)
3
42 (34.4%)
4
11 (9.0%)
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Table 2. Responsiveness results (N = 122). The change in cartilage volume ∆V and SRM values are provided as a function of ∆z and ∆θ. The center point is located at z0 = 0.8, θ0 = 210°. Reproducibility results are based on a subset of 12 subjects.
∆z
∆θ
∆V (mm3)
SD (∆V) (mm3)
SRM
Intra-reader ICC
Inter-reader ICC
0.10
100°
-46.2
89.5
-0.52
0.93
0.91
0.08
80°
-30.8
62.8
-0.49
0.99
0.98
0.06
60°
-17.9
40.6
-0.44
0.98
0.97
0.04
40°
-9.2
20.4
-0.45
0.98
0.98
0.02
20°
-2.3
7.2
-0.32
0.95
0.99
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