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

被引:74
作者
Kshirsagar, AA
Watson, PJ
Tyler, JA
Hall, LD
机构
[1] Univ Cambridge, Sch Clin Med, Herchel Smith Lab Med Chem, Cambridge CB2 2PZ, England
[2] Strangeways Res Lab, Cambridge CB1 4RN, England
关键词
magnetic resonance imaging (MRT); knee joint; computer analysis; edge detection; image registration; cartilage thickness;
D O I
10.1097/00004424-199805000-00006
中图分类号
R8 [特种医学]; R445 [影像诊断学];
学科分类号
1002 [临床医学]; 100207 [影像医学与核医学]; 1009 [特种医学];
摘要
RATIONALE AND OBJECTIVES. This work demonstrates a new method far computerized measurement of the dimensions (thickness and volume) of articular cartilage for any specified region of the human knee joint. Three-dimensional magnetic resonance (MR) images optimized for cartilage contrast have been analyzed using computerized edge-detection techniques, and the reproducibility of articular cartilage thickness and volume measurements is assessed. METHODS. A fat-suppressed, three-dimensional SPoiled GRass MR sequence (45/7.5/30 degrees) with total scan time of approximately 12 minutes was used to acquire volume images of human knee joints at spatial resolution of 0.6 x 1.2 x 1.2 mm, Measurements were made using six repeated scans for three healthy volunteers over a period of 2 months. The subsequent semi-automated image processing to establish total cartilage volume and cartilage thickness maps for the femur required approximately 60 minutes of operator time. RESULTS. The mean coefficient of variation for total cartilage volume for the six repeated scans for the three volunteers was 3.8%, and the average coefficient of variation for the user-selected cartilage plugs was 2.0%, The cartilage thickness maps from the repeated scans of the same knee were similar. CONCLUSIONS. Standard resolution MR images with fat-suppressed contrast lead to an objective and reproducible measurement of spatial dimensions of articular cartilage when analyzed semi-automatically using computerized edge-detection methods.
引用
收藏
页码:289 / 299
页数:11
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