Reproducibility of an Automated Quantitative MRI Assessment of Low-Grade Knee Articular Cartilage Lesions

3D-DESS MRI with CAIPIRINHA two- and fourfold acceleration for quantitatively assessing knee cartilage morphology

OBJECTIVES

This study aimed to assess the diagnostic image quality and compare the knee cartilage segmentation results using a controlled aliasing in parallel imaging results in higher acceleration (CAIPIRINHA)-accelerated 3D-dual echo steady-state (DESS) research package sequence in the knee.

MATERIALS AND METHODS

A total of 64 subjects underwent both two- and fourfold CAIPIRINHA-accelerated 3D-DESS and DESS without parallel acceleration technique of the knee on a 3.0 T system. Two musculoskeletal radiologists evaluated the images independently for image quality and diagnostic capability following randomization and anonymization. The consistency of automatic segmentation results between sequences was explored using an automatic knee cartilage segmentation research application. The descriptive statistics and inter-observer and inter-method concordance of various acceleration sequences were investigated. P values < .05 were considered significant.

RESULTS

For image quality evaluation, the image signal-to-noise ratio and contrast-to-noise ratio decreased with the decrease in scanning time. However, it is accompanied by the reduction of artifacts. Using 3D-DESS without parallel acceleration technique as the standard for cartilage grading diagnosisand the diagnostic agreement of two- and fourfold CAIPIRINHA-accelerated 3D-DESS was good, kappa value was 0.860 (P < .001) and 0.804 (p < 0.001), respectively. Regarding cartilage defects, the sensitivity and specificity of the twofold acceleration 3D-CAIPIRINHA-DESS were 95.56% and 97.70%, and the fourfold CAIPIRINHA-accelerated 3D-DESS were 91.49% and 97.65%, respectively. The intraclass correlation coefficients of various sequences in cartilage segmentation were almost all greater than 0.9.

CONCLUSION

The CAIPIRINHA-accelerated 3D-DESS sequence maintained comparable diagnostic and segmentations performance of knee cartilage after a 60% scan time reduction.

Variations in knee cartilage thickness: Fully automatic three-dimensional analysis of MRIs from five manufacturers

BACKGROUND

Measurements of knee cartilage thickness derived from MR images are attractive biomarkers for osteoarthritis research. Although some cross-sectional multivendor studies exist, none have employed fully automatic three-dimensional MRI analysis. Our objective was to evaluate the variations in knee cartilage thickness measurements obtained using automated methods and MRI instruments from five different vendors.

METHODS

The subjects were 10 healthy volunteers aged 22-60 years. MRI models with 3 Tesla strength from five different companies were used. Cartilage thickness was quantified fully automatically for seven regions. We hypothesized that "the MRI model influences cartilage thickness measurements." Inter-measurement error, defined as the absolute difference between the targeted and median thicknesses determined by the five MRI models, was analyzed using histograms. The factors generating the largest inter-measurement error were also examined.

RESULTS

No exceptional trends attributable to a specific instrument model were observed, and the p-value from the Kruskal-Wallis test exceeded 0.05 in all seven regions. Therefore, the study hypothesis was rejected. Of the 350 measurements, the inter-measurement error was ≤0.05 mm in 53 %, ≤0.10 mm in 75 %, and ≤0.20 mm in 95 %. Analysis of the medial tibial cartilage, which had the largest inter-measurement error, revealed mis-extraction of synovial fluid as cartilage.

CONCLUSIONS

The choice of MRI model did not influence cartilage thickness measurements. Overall, 95 % of the inter-measurement errors were within 0.20 mm. The greatest error resulted from mis-extracting synovial fluid as cartilage.

Lateral Meniscal Allograft Transplantation Shows a Long-Term Chondroprotective Effect on Quantitative Magnetic Resonance Imaging T2 Mapping at 7-Year Minimum Follow-Up

PURPOSE

To assess the long-term chondroprotective effect of lateral meniscal allograft transplantation (MAT) using quantitative magnetic resonance imaging (MRI) T2 mapping.

METHODS

In patients who underwent isolated lateral MAT, quantitative MRI T2 mapping was conducted preoperatively and postoperatively with at minimum follow-up of 7 years to assess the articular cartilage status. On the sagittal section image bisecting the lateral femoral condyle, the weight-bearing portions of the femoral and tibial articular cartilage were divided into 3 segments each-6 segments in total-based on the meniscal coverage area. The regions-of-interest analyses were performed on the 6 segments to measure the mean T2 value. Then the whole layer was divided into deep and superficial layers for further zonal analysis. The longitudinal change in T2 values was statistically analyzed using paired t-tests. Clinical outcome was evaluated using the Lysholm score.

RESULTS

A total of 31 patients were included in the study, with the MRI follow-up period of a minimum of 7 years (mean: 8.9 ± 1.3 years; range: 7.0-11.2 years). The mean T2 value of the whole layer showed significant improvement in all segments of the femoral cartilage and the posterior segment of tibial cartilage. In the zonal analysis, the mean T2 value of the tibial cartilage showed significant improvement in the superficial layer of the mid to posterior portion, while the deep layer remained stable. In contrast, the mean T2 value of the femoral cartilage showed significant improvement in the superficial and deep layers in all segments. The mean Lysholm score significantly improved from 62.6 ± 12.8 to 90.9 ± 10.5 (P < .001).

CONCLUSIONS

This study suggests that MAT appears to have a long-term chondroprotective effect on the articular cartilage as judged by quantitative T2 mapping.

LEVEL OF EVIDENCE

Level Ⅳ, case series.

A reproducibility study of knee cartilage volume and thickness values derived by fully automatic segmentation based on three-dimensional dual-echo in steady state data from 1.5 T and 3 T magnetic resonance imaging

OBJECTIVE

To evaluate the repeatability of cartilage volume and thickness values at 1.5 T MRI using a fully automatic cartilage segmentation method and reproducibility of the method between 1.5 T and 3 T data.

METHODS

The study included 20 knee joints from 10 healthy subjects with each subject having undergone double-knee MRI. All knees were scanned at 1.5 T and 3 T MR scanners using a three-dimensional (3D) high-resolution dual-echo in steady state (DESS) sequence. Cartilage volume and thickness of 21 subregions were quantified using a fully automatic cartilage segmentation research application (MR Chondral Health, version 3.0, Siemens Healthcare, Erlangen, Germany). The volume and thickness values derived from fully automatically computed segmentation masks were analyzed for the scan-rescan data from the same volunteers. The accuracy of the automatic segmentation of the cartilage in 1.5 T images was evaluated by the dice similarity coefficient (DSC) and Hausdorff distance (HD) using the manually corrected segmentation as a reference. The volume and thickness values calculated from 1.5 T and 3 T were also compared.

RESULTS

No statistically significant differences were found for cartilage thickness or volume across all subregions between the scan-rescanned data at 1.5 T (P > 0.05). The mean DSC between the fully automatic and manually corrected knee cartilage segmentation contours at 1.5 T was 0.9946. The average value of HD was 2.41 mm. Overall, there was no statistically significant difference in the cartilage volume or thickness in most-subregions between the two field strengths (P > 0.05) except for the medial region of femur and tibia. Bland-Altman plot and intraclass correlation coefficient (ICC) showed high consistency between results obtained based on same and different scanning sequences.

CONCLUSION

The cartilage segmentation software had high repeatability for DESS images obtained from the same device. In addition, the overall reproducibility of the images obtained from equipment of two different field strengths was satisfactory.

Osteoarthritis year in review 2021: imaging

PURPOSE

To provide a narrative review of original articles on imaging of osteoarthritis (OA) published between January 1, 2020 and March 31, 2021, with a special focus on imaging of inflammation, imaging of bone, cartilage and bone-cartilage interactions, imaging of peri-articular tissues, imaging scoring methods for OA, and artificial intelligence (AI) applied to OA imaging.

METHODS

The Embase, Pubmed, Medline, Cochrane databases were searched for original research articles in the English language on human, in vivo, imaging of OA published between January 1, 2020 and March 31, 2021. Search terms related to osteoarthritis combined with all imaging modalities and artificial intelligence were applied. A selection of articles reporting on one of the focus topics was discussed further.

RESULTS

The search resulted in 651 articles, of which 214 were deemed relevant to human OA imaging. Among the articles included, the knee joint (69%) and magnetic resonance imaging (MRI) (52%) were the predominant anatomical area and imaging modality studied. There were also a substantial number of papers (n = 46) reporting on AI applications in the field of OA imaging.

CONCLUSION

Imaging continues to play an important role in the assessment of OA. Recent advances in OA imaging include quantitative, non-contrast, and hybrid imaging techniques for improved characterization of multiple tissue processes in OA. In addition, an increasing effort in AI techniques is undertaken to enhance OA imaging acquisition and analysis.

Quantitative MRI evaluation of articular cartilage in patients with meniscus tear

PURPOSE

The aim of this study was to assess quantitatively articular cartilage volume, thickness, and T2 value alterations in meniscus tear patients.

MATERIALS AND METHODS

The study included 32 patients with meniscus tears (17 females, 15 males; mean age: 40.16 ± 11.85 years) and 24 healthy controls (12 females; 12 males; mean age: 36 ± 9.14 years). All subjects were examined by 3 T magnetic resonance imaging (MRI) with 3D dual-echo steady-state (DESS) and T2 mapping images. All patients underwent diagnostic arthroscopy and treatment. Cartilage thickness, cartilage volume and T2 values of 21 subregions of knee cartilage were measured using the prototype KneeCaP software (version 2.1; Siemens Healthcare, Erlangen, Germany). Mann-Whitney-U tests were utilized to determine if there were any significant differences among subregional articular cartilage volume, thickness and T2 value between patients with meniscus tear and the control group.

RESULTS

The articular cartilage T2 values in all subregions of the femur and tibia in the meniscus tear group were significantly higher (p< 0.05) than in the healthy control group. The cartilage thickness of the femoral condyle medial, femur trochlea, femur condyle lateral central, tibia plateau medial anterior and patella facet medial inferior in the meniscus tear group were slightly higher than in the control group (p< 0.05). In the femur trochlea medial, patella facet medial inferior, tibia plateau lateral posterior and tibia plateau lateral central, there were significant differences in relative cartilage volume percentage between the meniscus tear group and the healthy control group (p< 0.05). Nineteen patients had no cartilage abnormalities (Grade 0) in the meniscus tear group, as confirmed by arthroscopic surgery, and their T2 values in most subregions were significantly higher (p< 0.05) than those of the healthy control group.

CONCLUSION

The difference in articular cartilage indexes between patients with meniscus tears and healthy people without such tears can be detected by using quantitative MRI. Quantitative T2 values enable early and sensitive detection of early cartilage lesions.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning

OBJECTIVE

We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically.

DESIGN

We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison.

RESULTS

Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values.

CONCLUSIONS

Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.