Reproducibility of an Automated Quantitative MRI Assessment of Low-Grade Knee Articular Cartilage Lesions. (December 2021)

• Record Type: Journal Article
• Authors: Juras, Vladimir; Szomolanyi, Pavol; Schreiner, Markus M.; Unterberger, Karin; Kurekova, Andrea; Hager, Benedikt; Laurent, Didier; Raithel, Esther; Meyer, Heiko; Trattnig, Siegfried
• Is Part Of: Cartilage. Volume 13:Number 1(2022)Supplement
• Page Start: 646S
• Page End: 657S
• Publication Date: 2021-12
• Subjects: cartilage repair -- repair -- magnetic resonance imaging -- diagnostics -- knee -- joint involved -- osteoarthritis -- diagnosis; Cartilage -- Periodicals; 616.7
• DOI: 10.1177/1947603520961165
• Languages: English
• ISSNs: 1947-6035

Abstract

Objective: The goal of this study was to assess the reproducibility of an automated knee cartilage segmentation of 21 cartilage regions with a model-based algorithm and to compare the results with manual segmentation. Design: Thirteen patients with low-grade femoral cartilage defects were included in the study and were scanned twice on a 7-T magnetic resonance imaging (MRI) scanner 8 days apart. A 3-dimensional double-echo steady-state (3D-DESS) sequence was used to acquire MR images for automated cartilage segmentation, and T2-mapping was performed using a 3D triple-echo steady-state (3D-TESS) sequence. Cartilage volume, thickness, and T2 and texture features were automatically extracted from each knee for each of the 21 subregions. DESS was used for manual cartilage segmentation and compared with automated segmentation using the Dice coefficient. The reproducibility of each variable was expressed using standard error of measurement (SEM) and smallest detectable change (SDC). Results: The Dice coefficient for the similarity between manual and automated segmentation ranged from 0.83 to 0.88 in different cartilage regions. Test-retest analysis of automated cartilage segmentation and automated quantitative parameter extraction revealed excellent reproducibility for volume measurement (mean SDC for all subregions of 85.6 mm 3 ), for thickness detection (SDC = 0.16 mm) and also for T2 values (SDC = 2.38 ms) and most gray-level co-occurrence matrix features (SDC = 0.1 a.u.). Conclusions: The proposed technique of automated knee cartilage evaluation based on the segmentation of 3D MR images and correlation with T2 mapping provides highly reproducible results and significantly reduces the segmentation effort required for the analysis of knee articular cartilage in longitudinal studies.