Multinuclear MR and multilevel data processing: an insight into morphologic assessment of in vivo knee articular cartilage

Predictive Value of 3-T Magnetic Resonance Imaging in Diagnosing Grade 3 and 4 Chondral Lesions in the Hip

PURPOSE

To assess the diagnostic capability and predictive value of 3-T magnetic resonance imaging (MRI) in detecting grade 3 and 4 cartilage lesions in the hip.

METHODS

From August 2010 to April 2015, patients who underwent 3-T MRI and hip arthroscopy were included in the study. Data were prospectively collected and retrospectively reviewed. A radiologist prospectively documented MRI findings, and the surgeon documented cartilage damage at arthroscopy using the Outerbridge grading system. Arthroscopy was considered the diagnostic gold standard. This study was approved by the institutional review board.

RESULTS

The study group comprised 606 patients, with 354 men (58%) and 252 women (42%). The mean patient age was 34 years (range, 18 to 71 years). For femoral head defects, the sensitivity was 61% (95% confidence interval [CI], 53% to 68%), specificity was 58% (95% CI, 55% to 62%), positive predictive value was 29% (95% CI, 25% to 33%), and negative predictive value was 84% (95% CI, 81% to 87%). For chondral defects of the acetabulum, the sensitivity was 80% (95% CI, 75% to 84%), specificity was 41% (95% CI, 38% to 44%), positive predictive value was 42% (95% CI, 39% to 45%), and negative predictive value was 79% (95% CI, 74% to 84%).

CONCLUSIONS

The results of this study showed that 3-T MRI had sensitivity, as well as specificity, for identifying chondral defects that is similar to what has been previously reported. MRI showed increased sensitivity when identifying acetabular defects compared with femoral head defects. With a low positive predictive value, MRI may be most useful in ruling out cartilage lesions.

LEVEL OF EVIDENCE

Level III, diagnostic study.

Automatic segmentation of cartilage in high-field magnetic resonance images of the knee joint with an improved voxel-classification-driven region-growing algorithm using vicinity-correlated subsampling

Anatomical structures that can deteriorate over time, such as cartilage, can be successfully delineated with voxel-classification approaches in magnetic resonance (MR) images. However, segmentation via voxel-classification is a computationally demanding process for high-field MR images with high spatial resolutions. In this study, the whole femoral, tibial, and patellar cartilage compartments in the knee joint were automatically segmented in high-field MR images obtained from Osteoarthritis Initiative using a voxel-classification-driven region-growing algorithm with sample-expand method. Computational complexity of the classification was alleviated via subsampling of the background voxels in the training MR images and selecting a small subset of significant features by taking into consideration systems with limited memory and processing power. Although subsampling of the voxels may lead to a loss of generality of the training models and a decrease in segmentation accuracies, effective subsampling strategies can overcome these problems. Therefore, different subsampling techniques, which involve uniform, Gaussian, vicinity-correlated (VC) sparse, and VC dense subsampling, were used to generate four training models. The segmentation system was experimented using 10 training and 23 testing MR images, and the effects of different training models on segmentation accuracies were investigated. Experimental results showed that the highest mean Dice similarity coefficient (DSC) values for all compartments were obtained when the training models of VC sparse subsampling technique were used. Mean DSC values optimized with this technique were 82.6%, 83.1%, and 72.6% for femoral, tibial, and patellar cartilage compartments, respectively, when mean sensitivities were 79.9%, 84.0%, and 71.5%, and mean specificities were 99.8%, 99.9%, and 99.9%.