From the Department of Radiology, Norfolk and Norwich University Hospitals NHS Foundation Trust, Colney Lane, Norwich NR4 7UY, England (T.D.T., J.W.M.); Norwich Medical School, University of East Anglia, Norwich, England (T.D.T., J.W.M.); Royal Liverpool University Hospital, Liverpool, England (S.B.L.); Department of Radiology, University Hospitals Bristol and Weston NHS Foundation Trust, Bristol, England (S.R.); Departments of Engineering (G.M.T., A.H.G.) and Medicine (K.E.S.P.), University of Cambridge, Cambridge, England; Department of Epidemiology and Biostatistics, University of California-San Francisco, San Francisco, Calif (J.A.L.); and Department of Physical Medicine and Rehabilitation, Kansas University Medical Center, Kansas City, Kan (N.A.S.).
Radiology. 2021 Jun;299(3):649-659. doi: 10.1148/radiol.2021203928. Epub 2021 Apr 13.
Background Imaging of structural disease in osteoarthritis has traditionally relied on MRI and radiography. Joint space mapping (JSM) can be used to quantitatively map joint space width (JSW) in three dimensions from CT images. Purpose To demonstrate the reproducibility, repeatability, and feasibility of JSM of the knee using weight-bearing CT images. Materials and Methods Two convenience samples of weight-bearing CT images of left and right knees with radiographic Kellgren-Lawrence grades (KLGs) less than or equal to 2 were acquired from 2014 to 2018 and were analyzed retrospectively with JSM to deliver three-dimensional JSW maps. For reproducibility, images of three sets of knees were used for novice training, and then the JSM output was compared against an expert's assessment. JSM was also performed on 2-week follow-up images in the second cohort, yielding three-dimensional JSW difference maps for repeatability. Statistical parametric mapping was performed on all knee imaging data (KLG, 0-4) to show the feasibility of a surface-based analysis in three dimensions. Results Reproducibility (in 20 individuals; mean age, 58 years ± 7 [standard deviation]; mean body mass index, 28 kg/m ± 6; 14 women) and repeatability (in nine individuals; mean age, 53 years ± 6; mean body mass index, 26 kg/m ± 4; seven women) reached their lowest performance at a smallest detectable difference less than ±0.1 mm in the central medial tibiofemoral joint space for individuals without radiographically demonstrated disease. The average root mean square coefficient of variation was less than 5% across all groups. Statistical parametric mapping (33 individuals; mean age, 57 years ± 7; mean body mass index, 27 kg/m ± 6; 23 women) showed that the central-to-posterior medial joint space was significantly narrower by 0.5 mm for each incremental increase in the KLG (threshold < .05). One knee (KLG, 2) demonstrated a baseline versus 24-month change in its three-dimensional JSW distribution that was beyond the smallest detectable difference across the lateral joint space. Conclusion Joint space mapping of the knee using weight-bearing CT images is feasible, demonstrating a relationship between the three-dimensional joint space width distribution and structural joint disease. It is reliably learned by novice users, can be personalized for disease phenotypes, and can be used to achieve a smallest detectable difference that is at least 50% smaller than that reported to be achieved at the highest performance level in radiography. © RSNA, 2021 . See also the editorial by Roemer in this issue.
背景 骨关节炎结构疾病的影像学检查传统上依赖于 MRI 和 X 线摄影。关节间隙测绘（JSM）可用于从 CT 图像定量测绘三维关节间隙宽度（JSW）。目的 演示使用负重 CT 图像进行膝关节 JSM 的可重复性、可再现性和可行性。材料与方法 2014 年至 2018 年，连续采集 2014 例负重左膝和右膝 CT 图像，对其进行回顾性分析，采用 JSM 提供三维 JSW 图。为了评估可重复性，在第二组中对 2 周的随访图像进行 JSM，得到三维 JSW 差值图。对所有膝关节成像数据（KLG0-4）进行统计参数映射，以显示三维基于表面的分析的可行性。结果 可重复性（20 例，平均年龄 58 岁±7[标准差]；平均体重指数 28 kg/m±6；14 例女性）和可再现性（9 例，平均年龄 53 岁±6；平均体重指数 26 kg/m±4；7 例女性）在未显示放射学疾病的个体中，最小可检测差异小于±0.1mm 时，表现最低。所有组的平均均方根变异系数均小于 5%。统计参数映射（33 例，平均年龄 57 岁±7；平均体重指数 27 kg/m±6；23 例女性）显示，KLG 每增加 1 级，中央至后内侧关节间隙变窄 0.5mm（阈值<0.05）。1 例（KLG2）的三维 JSW 分布在基线和 24 个月时的变化超出了外侧关节间隙的最小可检测差异。结论 使用负重 CT 图像对膝关节进行 JSM 是可行的，表明三维关节间隙宽度分布与结构性关节疾病之间存在关联。新手用户可可靠地学习 JSM，可针对疾病表型进行个性化定制，并可达到至少 50%的最小可检测差异，优于放射学的最高性能水平。©2021RSNA。本期还刊登了 Roemer 的社论。
