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髋关节骨关节炎中髋关节外展肌质量和大小的自动评估:局部肌肉区域与整体肌肉质量密切相关。

Automated evaluation of hip abductor muscle quality and size in hip osteoarthritis: Localized muscle regions are strongly associated with overall muscle quality.

机构信息

Department of Radiology and Biomedical Imaging, University of California - San Francisco, 185 Berry Street, Suite 190, Lobby 6, San Francisco, CA 94107, USA; Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, 2500 University Dr NW, Calgary, AB T2N 1N4, Canada; Department of Radiology, Cumming School of Medicine, University of Calgary, Foothills Medical Centre, 1403-29th Street NW, Calgary, AB T2N 2T9, Canada.

Department of Physical Therapy and Rehabilitation Science, University of California - San Francisco, 1500 Owens Street, Suite 400, San Francisco, CA 94158, USA.

出版信息

Magn Reson Imaging. 2024 Sep;111:237-245. doi: 10.1016/j.mri.2024.04.025. Epub 2024 Apr 16.

Abstract

Limited information exists regarding abductor muscle quality variation across its length and which locations are most representative of overall muscle quality. This is exacerbated by time-intensive processes for manual muscle segmentation, which limits feasibility of large cohort analyses. The purpose of this study was to develop an automated and localized analysis pipeline that accurately estimates hip abductor muscle quality and size in individuals with mild-to-moderate hip osteoarthritis (OA) and identifies regions of each muscle which provide best estimates of overall muscle quality. Forty-four participants (age 52.7 ± 16.1 years, BMI 23.7 ± 3.4 kg/m, 14 males) with and without mild-to-moderate radiographic hip OA were recruited for this study. Unilateral hip magnetic resonance (MR) images were acquired on a 3.0 T MR scanner and included axial T-weighted fast spin echo and 3D axial Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL-IQ) spoiled gradient-recalled echo (SPGR) with multi-peak fat spectrum modeling and single T* correction. A three dimensional (3D) V-Net convolutional neural network was trained to automatically segment the gluteus medius (GMED), gluteus minimus (GMIN), and tensor fascia lata (TFL) on axial IDEAL-IQ. Agreement between manual and automatic segmentation and associations between axial fat fraction (FF) estimated from IDEAL-IQ and overall muscle FF were evaluated. Dice scores for automatic segmentation were 0.94, 0.87, and 0.91 for GMED, GMIN, and TFL, respectively. GMED, GMIN, and TFL volumetric and FF measures were strongly correlated (r: 0.92-0.99) between automatic and manual segmentations, where all values fell within the 95% limits of agreement of [-9.79 cm, 17.43 cm] and [-1.99%, 2.89%], respectively. Axial FF was significantly associated with overall FF with the strongest correlations at 50%, 50%, and 65% the length of the GMED, GMIN, and TFL muscles, respectively (r: 0.93-0.97). An automated and localized analysis can provide efficient and accurate estimates of hip abductor muscle quality and size across muscle length. Specific regions of the muscle may be used to estimate overall muscle quality in an abbreviated evaluation of muscle quality.

摘要

关于髋外展肌在其长度上的质量变化,以及哪些部位最能代表整体肌肉质量,相关信息十分有限。由于手动肌肉分割的过程耗时较长,这限制了大样本队列分析的可行性,这一问题更加严重。本研究的目的是开发一种自动的、局部的分析管道,以准确估计轻度至中度髋骨关节炎(OA)患者的髋外展肌质量和大小,并确定每个肌肉的哪些区域可以提供整体肌肉质量的最佳估计。本研究招募了 44 名参与者(年龄 52.7±16.1 岁,BMI 23.7±3.4kg/m,男性 14 名),他们患有或不患有轻度至中度放射学髋 OA。在 3.0T MR 扫描仪上获取单侧髋关节磁共振(MR)图像,包括轴向 T 加权快速自旋回波和 3D 轴向迭代分解水和脂肪与回波不对称和最小二乘估计(IDEAL-IQ)扰动脉冲梯度回波(SPGR),具有多峰脂肪谱建模和单 T*校正。训练一个三维(3D)V-Net 卷积神经网络,以自动分割轴向 IDEAL-IQ 的臀中肌(GMED)、臀小肌(GMIN)和阔筋膜张肌(TFL)。评估手动和自动分割之间的一致性以及从 IDEAL-IQ 估计的轴向脂肪分数(FF)与整体肌肉 FF 之间的相关性。GMED、GMIN 和 TFL 的自动分割 Dice 评分分别为 0.94、0.87 和 0.91。GMED、GMIN 和 TFL 的容积和 FF 测量值在自动和手动分割之间具有很强的相关性(r:0.92-0.99),所有值均落在[-9.79cm,17.43cm]和[-1.99%,2.89%]的 95%一致性界限内。轴向 FF 与整体 FF 显著相关,在 GMED、GMIN 和 TFL 肌肉的 50%、50%和 65%长度处相关性最强(r:0.93-0.97)。自动和局部分析可以提供髋外展肌质量和大小的高效、准确估计,横跨肌肉长度。肌肉的特定区域可用于在肌肉质量的简短评估中估计整体肌肉质量。

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