Magnetic resonance imaging (MRI) offers direct three dimensional (3D) visualization of the articular cartilage in the knee joint. New high-field three-Tesla (3T) MRI scanners and specialized pulse sequences can provide images with excellent spatial resolution and high contrast, which show the cartilage and other structures of the knee in great detail. These new techniques are ideal for the application of image processing software to segment (identify and outline) cartilage plates on MRI slices for the quantitative assessment of osteoarthritis (OA) progression. Several academic and industry affiliated research groups have developed software tools to segment the cartilage plates on knee MRI scans. However measures derived from the full cartilage plate or even specified subregions can be relatively insensitive to change. We propose to test the hypothesis that an analysis based on comparing local regions of the knee cartilage will be more responsive than global measures of cartilage morphometry for detecting knee OA changes. We will use a software tool developed in our laboratory to segment the articular cartilage on knee MRI image sets. 3D registration software will be used to align baseline and follow-up segmented scans. Once the baseline and follow-up segmented images are registered (aligned) the technique will measure volume change in a region in close proximity to a known cartilage area of thinning. We will also develop and validate new quantitative metrics based on measurements of bone marrow edema (BME), bone cysts, and osteophyte volume. Our project will use data and images from the osteoarthritis Initiative (OAI), a large National Institutes of Health (NIH) study with approximately 4,800 enrolled subjects subdivided into three groups: the Incidence Cohort, the Progression Cohort, and the Control Cohort. A subset of 400 subjects from the Progression Cohort will be used to test our hypotheses. Our existing analysis software will be further developed to facilitate the aims of the study.

Public Health Relevance

This project will address public health concerns by validating an improved technique for evaluating osteoarthritis of the knee. The work will contribute to the public health indirectly by assisting others in developing and evaluating OA therapies.

Agency
National Institute of Health (NIH)
Institute
National Institute of Arthritis and Musculoskeletal and Skin Diseases (NIAMS)
Type
Research Project (R01)
Project #
1R01AR056664-01A1
Application #
7784808
Study Section
Skeletal Biology Structure and Regeneration Study Section (SBSR)
Program Officer
Lester, Gayle E
Project Start
2010-02-01
Project End
2013-01-31
Budget Start
2010-02-01
Budget End
2011-01-31
Support Year
1
Fiscal Year
2010
Total Cost
$400,500
Indirect Cost
Name
Brigham and Women's Hospital
Department
Type
DUNS #
030811269
City
Boston
State
MA
Country
United States
Zip Code
02115
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Schaefer, L F; Sury, M; Yin, M et al. (2017) Quantitative measurement of medial femoral knee cartilage volume - analysis of the OA Biomarkers Consortium FNIH Study cohort. Osteoarthritis Cartilage 25:1107-1113
Hakky, M; Jarraya, M; Ratzlaff, C et al. (2015) Validity and responsiveness of a new measure of knee osteophytes for osteoarthritis studies: data from the osteoarthritis initiative. Osteoarthritis Cartilage 23:2199-2205
Ratzlaff, C; Van Wyngaarden, C; Duryea, J (2014) Location-specific hip joint space width for progression of hip osteoarthritis--data from the osteoarthritis initiative. Osteoarthritis Cartilage 22:1481-7
Duryea, Jeffrey; Iranpour-Boroujeni, Tannaz; Collins, Jamie E et al. (2014) Local area cartilage segmentation: a semiautomated novel method of measuring cartilage loss in knee osteoarthritis. Arthritis Care Res (Hoboken) 66:1560-5
Ratzlaff, C; Guermazi, A; Collins, J et al. (2013) A rapid, novel method of volumetric assessment of MRI-detected subchondral bone marrow lesions in knee osteoarthritis. Osteoarthritis Cartilage 21:806-14
Iranpour-Boroujeni, T; Watanabe, A; Bashtar, R et al. (2011) 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 19:309-14