Brem M H, Lang P K, Neumann G, Schlechtweg P M, Schneider E, Jackson R, Yu J, Eaton C B, Hennig F F, Yoshioka H, Pappas G, Duryea J
Department of Radiology, Brigham and Women's Hospital, 75 Francis St., Boston, MA, 02115, USA.
Skeletal Radiol. 2009 May;38(5):505-11. doi: 10.1007/s00256-009-0658-1. Epub 2009 Feb 28.
Software-based image analysis is important for studies of cartilage changes in knee osteoarthritis (OA). This study describes an evaluation of a semi-automated cartilage segmentation software tool capable of quantifying paired images for potential use in longitudinal studies of knee OA. We describe the methodology behind the analysis and demonstrate its use by determination of test-retest analysis precision of duplicate knee magnetic resonance imaging (MRI) data sets.
Test-retest knee MR images of 12 subjects with a range of knee health were evaluated from the Osteoarthritis Initiative (OAI) pilot MR study. Each subject was removed from the magnet between the two scans. The 3D DESS (sagittal, 0.456 mm x 0.365 mm, 0.7 mm slice thickness, TR 16.5 ms, TE 4.7 ms) images were obtained on a 3-T Siemens Trio MR system with a USA Instruments quadrature transmit-receive extremity coil. Segmentation of one 3D-image series was first performed and then the corresponding retest series was segmented by viewing both image series concurrently in two adjacent windows. After manual registration of the series, the first segmentation cartilage outline served as an initial estimate for the second segmentation. We evaluated morphometric measures of the bone and cartilage surface area (tAB and AC), cartilage volume (VC), and mean thickness (ThC.me) for medial/lateral tibia (MT/LT), total femur (F) and patella (P). Test-retest reproducibility was assessed using the root-mean square coefficient of variation (RMS CV%).
For the paired analyses, RMS CV % ranged from 0.9% to 1.2% for VC, from 0.3% to 0.7% for AC, from 0.6% to 2.7% for tAB and 0.8% to 1.5% for ThC.me.
Paired image analysis improved the measurement precision of cartilage segmentation. Our results are in agreement with other publications supporting the use of paired analysis for longitudinal studies of knee OA.
基于软件的图像分析对于膝关节骨关节炎(OA)软骨变化的研究很重要。本研究描述了一种半自动软骨分割软件工具的评估,该工具能够对配对图像进行量化,可用于膝关节OA的纵向研究。我们描述了分析背后的方法,并通过确定重复膝关节磁共振成像(MRI)数据集的重测分析精度来证明其用途。
从骨关节炎倡议(OAI)试点MR研究中评估了12名膝关节健康状况各异的受试者的重测膝关节MR图像。在两次扫描之间将每个受试者从磁体中移出。在配备美国仪器正交发射接收肢体线圈的3-T西门子Trio MR系统上获取3D DESS(矢状面,0.456 mm×0.365 mm,层厚0.7 mm,TR 16.5 ms,TE 4.7 ms)图像。首先对一个3D图像系列进行分割,然后通过在两个相邻窗口中同时查看两个图像系列来分割相应的重测系列。在对系列进行手动配准后,第一次分割的软骨轮廓用作第二次分割的初始估计。我们评估了内侧/外侧胫骨(MT/LT)、全股骨(F)和髌骨(P)的骨和软骨表面积(tAB和AC)、软骨体积(VC)和平均厚度(ThC.me)的形态测量指标。使用均方根变异系数(RMS CV%)评估重测再现性。
对于配对分析,VC的RMS CV%范围为0.9%至1.2%,AC为0.3%至0.7%,tAB为0.6%至2.7%,ThC.me为0.8%至1.5%。
配对图像分析提高了软骨分割的测量精度。我们的结果与其他支持在膝关节OA纵向研究中使用配对分析的出版物一致。