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Acad Radiol ; 17(2): 244-50, 2010 Feb.
Article in English | MEDLINE | ID: mdl-19962916

ABSTRACT

RATIONALE AND OBJECTIVES: This study was designed to demonstrate the feasibility of the use of phase-contrast computed tomographic (CT) imaging for the identification of articular cartilage abnormalities of the knees in a mouse model of collagen-induced arthritis. MATERIALS AND METHODS: Arthritis was induced in nine male DBA/1 J mice by the intradermal injection of collagen. After 50 days, the nine mice were sacrificed, along with four mice that did not receive intradermal injections of collagen. Phase-contrast CT imaging using a microfocus x-ray source of the entire knee was performed. The images were evaluated by two blinded readers, and histopathologic grades were considered the reference standard. The phase-contrast CT images of cartilage were graded 0, I, or II. Evaluation of the grading agreement between the phase-contrast CT images and histopathologic findings was performed using correlation analysis. RESULTS: Phase-contrast CT images highly reflected the subchondral bone status in the assessment of articular cartilage abnormalities in the mouse model of collagen-induced arthritis. Three-dimensional reformed images showed the articular surface and subchondral bony status of the knee joints. On the basis of the histopathology of the 26 knee joints, 12 joints were grade 0, six joints were grade I, and eight joints were grade II. Grading agreement between the use of the phase-contrast CT images and histopathologic results was high (r = 0.76). CONCLUSIONS: Phase-contrast CT imaging using a microfocus x-ray source offers a promising tool for the assessment of articular cartilage abnormalities of the knees in a mouse model.


Subject(s)
Arthritis/chemically induced , Arthritis/pathology , Cartilage, Articular/pathology , Collagen Type II , Disease Models, Animal , Tomography, X-Ray Computed/methods , Animals , Cartilage, Articular/drug effects , Humans , Male , Mice , Mice, Inbred DBA , Reproducibility of Results , Sensitivity and Specificity
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