Department of Radiology and Biomedical Imaging, University of California, San Francisco (UCSF), San Francisco, California 94107, USA.
J Magn Reson Imaging. 2012 Jan;35(1):211-7. doi: 10.1002/jmri.22803. Epub 2011 Oct 10.
To develop imaging techniques that provide quantitative characterization of bone marrow edema pattern (BME) in wrist joints of patients with rheumatoid arthritis (RA), including volume, signal intensity changes, and perfusion properties.
Fourteen RA patients and three controls were scanned using 3 Tesla MR. BME was semi-automatically segmented in water images obtained from iterative decomposition of water and fat with echo asymmetry and least-squares estimation (IDEAL) sequences. BME perfusion parameters (enhancement and slope) were evaluated using three-dimensional (3D) dynamic enhanced MRI (DCE-MRI). Experimental reproducibility, inter- and intra-observer reproducibility of BME quantification were evaluated using root mean square coefficients of variation (RMS-CV) and intraclass correlation (ICC).
The RMS-CV for BME volume quantification with repeated scans were 6.9%. The inter-observer ICC was 0.993 and RMS CV was 5.2%. The intra-observer ICC was 0.998 and RMS CV was 2.3%. Both maximum enhancement and slope during DCE-MRI were significantly higher in BME than in normal bone marrow (P < 0.001). No significant correlation was found between BME quantification and clinical evaluations.
A highly reproducible semi-automatic method for quantifying BME lesion burden in RA was developed, which may enhance our capability of predicting disease progression and monitoring treatment response.
开发成像技术,对类风湿关节炎(RA)患者腕关节骨髓水肿模式(BME)进行定量特征分析,包括容积、信号强度变化和灌注特性。
使用 3T MR 对 14 名 RA 患者和 3 名对照者进行扫描。在通过水脂分解迭代估计(IDEAL)序列获得的水图像中半自动分割 BME。使用三维(3D)动态增强磁共振成像(DCE-MRI)评估 BME 灌注参数(增强和斜率)。使用均方根变异系数(RMS-CV)和组内相关系数(ICC)评估 BME 定量的实验重复性、观察者间和观察者内重复性。
重复扫描的 BME 容积定量的 RMS-CV 为 6.9%。观察者间 ICC 为 0.993,RMS-CV 为 5.2%。观察者内 ICC 为 0.998,RMS-CV 为 2.3%。DCE-MRI 中最大增强和斜率在 BME 中均显著高于正常骨髓(P < 0.001)。BME 定量与临床评估之间未发现显著相关性。
开发了一种高度可重复的定量 RA 中 BME 病变负担的半自动方法,这可能增强我们预测疾病进展和监测治疗反应的能力。