Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, California, USA.
Department of Radiology, University Hospital, LMU Munich, Munich, Germany.
J Magn Reson Imaging. 2024 Jul;60(1):186-202. doi: 10.1002/jmri.29009. Epub 2023 Sep 13.
The polyarticular nature of Osteoarthritis (OA) tends to manifest in multi-joints. Associations between cartilage health in connected joints can help identify early degeneration and offer the potential for biomechanical intervention. Such associations between hip and knee cartilages remain understudied.
To investigate T associations between hip-femoral and acetabular-cartilage subregions with Intra-limb and Inter-limb patellar cartilage; whole and deep-medial (DM), deep-lateral (DL), superficial-medial (SM), superficial-lateral (SL) subregions.
Prospective.
Twenty-eight subjects (age 55.1 ± 12.8 years, 15 females) with none-to-moderate hip-OA while no radiographic knee-OA.
FIELD STRENGTH/SEQUENCE: 3-T, bilateral hip, and knee: 3D-proton-density-fat-saturated (PDFS) Cube and Magnetization-Prepared-Angle-Modulated-Partitioned-k-Space-Spoiled-Gradient-Echo-Snapshots (MAPSS).
Ages of subjects were categorized into Group-1 (≤40), Group-2 (41-50), Group-3 (51-60), Group-4 (61-70), Group-5 (71-80), and Group-6 (≥81). Hip T maps, co-registered to Cube, underwent an atlas-based algorithm to quantify femoral and acetabular subregional (R-R) cartilage T. For knee Cube, a combination of V-Net architectures was used to segment the patellar cartilage and subregions (DM, DL, SM, SL). T values were computed from co-registered MAPSS.
For Intra-and-Inter-limb, 5 optimum predictors out of 13 (Hip subregional T, age group, gender) were selected by univariate linear-regression, to predict outcome (patellar T). The top five predictors were stepwise added to six linear mixed-effect (LME) models. In all LME models, we assume the data come from the same subject sharing the same random effect. The best-performing models (LME-model) selected via ANOVA, were tested with DM, SM, SL, and DL subregional-mean T. LME assumptions were verified (normality of residuals, random-effects, and posterior-predictive-checks).
LME-model (Intra-limb) had significant negative and positive fixed-effects of femoral-R and acetabular-R T, respectively (conditional-R = 0.581). LME-model (Inter-limb) had significant positive fixed-effects of femoral-R T (conditional-R = 0.26).
Significant positive and negative T associations were identified between load-bearing hip cartilage-subregions vs. ipsilateral and contralateral patellar cartilages respectively. The effects were localized on medial subregions of Inter-limb, in particular.
1 TECHNICAL EFFICACY: Stage 1.
骨关节炎(OA)的多关节性质往往表现为多关节受累。连接关节之间的软骨健康关联有助于识别早期退变,并提供生物力学干预的潜力。髋关节和膝关节软骨之间的这种关联仍有待研究。
研究髋关节股骨和髋臼软骨亚区与肢体间和肢体内髌软骨的 T 关联;全层和深层内侧(DM)、深层外侧(DL)、浅层内侧(SM)、浅层外侧(SL)亚区。
前瞻性。
28 名受试者(年龄 55.1±12.8 岁,15 名女性)患有非至中度髋关节 OA,但无放射学膝关节 OA。
场强/序列:3-T,双侧髋关节和膝关节:3D 质子密度脂肪饱和(PDFS)立方体和磁化准备角度调制分区空间扰相梯度回波快照(MAPSS)。
受试者年龄分为组 1(≤40 岁)、组 2(41-50 岁)、组 3(51-60 岁)、组 4(61-70 岁)、组 5(71-80 岁)和组 6(≥81 岁)。髋关节 T 图谱与立方体配准,采用基于图谱的算法对股骨和髋臼亚区(R-R)软骨 T 进行定量。对于膝关节立方体,使用 V-Net 架构的组合对髌软骨和亚区(DM、DL、SM、SL)进行分割。从配准的 MAPSS 计算 T 值。
对于肢体间和肢体内,通过单变量线性回归从 13 个(髋关节亚区 T、年龄组、性别)中选择 5 个最佳预测因子,以预测结果(髌软骨 T)。前五个预测因子被逐步添加到六个线性混合效应(LME)模型中。在所有 LME 模型中,我们假设数据来自同一个共享相同随机效应的受试者。通过方差分析选择表现最佳的模型(LME 模型),并与 DM、SM、SL 和 DL 亚区平均 T 进行测试。验证了 LME 模型的假设(残差、随机效应和后验预测检查的正态性)。
LME 模型(肢体间)具有股骨 R 和髋臼 R T 的显著负固定效应和正固定效应,分别为(条件 R=0.581)。LME 模型(肢体内)具有股骨 R T 的显著正固定效应(条件 R=0.26)。
在负荷承重髋关节软骨亚区与同侧和对侧髌软骨之间分别确定了显著的正和负 T 关联。在肢体间,特别是内侧亚区,这种影响是局部的。
1 技术功效:第 1 阶段。