Abstract
ObjectiveThe 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.DesignThirteen 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).ResultsThe 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 mm3), 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.).ConclusionsThe 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.
Highlights
Magnetic resonance imaging (MRI) is a valuable tool that provides the ability to detect signs of osteoarthritis (OA) in the whole joint and in all joint structures, as well as to quantify changes in cartilage volume and thickness during the course of the disease.[1,2] Recently, a number of MR methods have been developed that are relatively specific for the proteoglycan and collagen content in OA-affected articular cartilage
Small corrections were needed in all cases, most often in the lateral posterior femur, and the anterior and posterior lateral tibia
The exemplary manual and automated segmentations in various views are depicted in Fig. 1 and 2
Summary
Magnetic resonance imaging (MRI) is a valuable tool that provides the ability to detect signs of osteoarthritis (OA) in the whole joint and in all joint structures, as well as to quantify changes in cartilage volume and thickness during the course of the disease.[1,2] Recently, a number of MR methods have been developed that are relatively specific for the proteoglycan and collagen content in OA-affected articular cartilage These compositional markers can noninvasively determine collagen content and organization,[3] proteoglycan content,[4] biomechanical properties,[5] and detect early-stage focal cartilage lesions.[6] Transverse relaxation time (T2) mapping is a well-established quantitative MRI method, which reflects the interplay of water Cartilage 00(0). T2-mapping is often used in longitudinal studies where it can provide valuable information on collagen matrix status as the disease progresses.[10,11] On ultra-high-field MR scanners, more progressive sequences for T2-mapping can be used rather than a conventional multi-echo spin-echo sequence, such as triple-echo steady state (TESS) sequence, which provide 3-dimensional (3D) knee coverage, lower specific absorption rate demands, and shorter measurement times.[12,13,14] texture analysis of quantitative MR maps using gray-level cooccurrence matrix (GLCM) features provides additional information on collagen organization and can be used to determine cartilage status.[15]
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