Correlation of semiquantitative vs quantitative MRI meniscus measures in osteoarthritic knees: results from the Osteoarthritis Initiative

Three-dimensional MRI shows cartilage defect extension with no separation from the meniscus in women in their 70 s with knee osteoarthritis

The positional relationship between cartilage defects and the meniscus is poorly understood for osteoarthritis of the knee. Our purpose was to clarify how cartilage defects extend and their association with the meniscus location during osteoarthritis progression. The subjects were women in their 70 s who were registered in the Kanagawa Knee Study. We obtained 3D MRI images of the tibial surfaces with menisci in subjects with cartilage area ratios < 0.95 and examined the morphological association between cartilage defects and the medial meniscus (MM) by viewing the defects according to the cartilage area ratio at the medial tibial region. Of the 561 Kanagawa Knee Study subjects, 45 were included in the analyses, and 11 had a cartilage area ratio < 0.95 at the medial tibia. Significant differences were observed in the localization of cartilage defects among 9 subregions, with cartilage defects occurring predominantly in the middle external subregion. The inner margin of the MM contacted the cartilage defect in 7 knees and crossed the cartilage defect in 4 knees but was never found separated from the cartilage defect. The cartilage defects occurred from the middle external subregion and extended to the surrounding area without separating from the inner margin of the MM.

Trial registration UMIN, UMIN000032826; 1 September 2018.

Volumetric assessment of extrusion in medial meniscus posterior root tears through semi-automatic segmentation on 3-tesla magnetic resonance images

BACKGROUND

Many reports have described the relationship between medial meniscus posterior root tears (MMPRTs) and meniscal extrusion on coronal magnetic resonance (MR) images. However, volumetric assessment of meniscal extrusion has not been performed, and the correlation between extrusion length and volume remains unclear.

HYPOTHESIS

Extrusion in both length and volume would be greater in MMPRTs than that in the normal medial meniscus, and the extrusion length measured on coronal MR images would be correlated with the extrusion volume.

PATIENTS AND METHODS

A total of 20 knees who underwent isolated MMPRTs without trauma history were included in the MMPRT group, and another 20 knees with normal medial meniscus were selected as the control group. All 40 knees underwent 3-tesla MR imaging. The extrusion length of the medial meniscus was measured using coronal MR images only. Volumetric assessments of the meniscus were performed and analyzed via a semi-automatic segmentation. Group-wise comparisons of the extrusion length and volumetric values were conducted, and the correlation between the two measures in both groups was evaluated.

RESULTS

The mean extrusion length of the medial meniscus in the MMPRT group was significantly longer (2.60 vs. 0.63mm; p<0.001) than that in the control group. The mean extrusion volume was also significantly higher in the MMPRT than that in the control group (770.93 vs. 193.80 mm³; p<0.001). The extrusion length was significantly and positively correlated with the extrusion volume in both groups (R=0.64; p=0.002 in MMPRT, R=0.73; p<0.001 in the control group).

DISCUSSION

Semi-automatic segmentation was used to measure the volume of meniscal extrusion, which had previously only been estimated indirectly with the extrusion length on coronal MR images. MMPRTs significantly increased the extrusion in both measures. The extrusion length measured on coronal MR images was positively correlated with the extrusion volume in both groups.

LEVEL OF EVIDENCE

III, Case-control study.

Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U-Net with transfer learning

PURPOSE

To develop a deep learning-based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ , and T 2 ∗ parameters, which can be used to assess knee osteoarthritis (OA).

METHODS

Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ -weighted MR images. Transfer learning was applied to develop 2D attention U-Net convolutional neural networks for the menisci segmentation based on each radiologist's ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ , T 2 ∗ relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared.

RESULTS

The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists' manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ , and T 2 ∗ relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter-observer variability of 2 radiologists.

CONCLUSION

The proposed deep learning-based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

Stage-specific meniscal features predict progression of osteoarthritis of the knee: a retrospective cohort study using data from the osteoarthritis initiative

Background

In the progression of osteoarthritis (OA) of the knee, a correlation between meniscal posterior segment injuries and medial meniscal extrusion has been reported, but there have been few reports on the relationship with the meniscal shape. The purpose of this study was to clarify the features of the meniscal shape involved in the progression of knee OA.

Methods

Data were obtained from the Osteoarthritis Initiative (OAI) database. We defined two sets of subjects. One set included 455 knees of subjects whose OA grade on the Kellgren Lawrence (KL) scale progressed in 24 months from baseline and the other set consisted of 455 knees with no progression. The OA progressed subjects were divided to three groups: the “OA change group”, KL0 and KL1 knees that progressed to KL2 and KL3; the “mild change group”, KL2 knees that progressed to KL3; and the “severe change group”, KL2 and KL3 knees that progressed to KL4. The no progression set was divided into three groups whose OA grade remained unchanged. We used magnetic resonance imaging data and manually measured seven items (longitudinal diameter [LD], anterior wedge thickness, anterior wedge width, posterior wedge width, posterior wedge thickness, anterior wedge angle, posterior wedge angle) from the sagittal slice and the extrusion from the coronal slice. These measurements were compared between knees with and without OA progression.

Results

In the “OA change group” and “mild change group”, the anterior and posterior wedge widths and the extrusion were significantly larger, but the anterior and the posterior wedge angles were significantly smaller. In the “severe change group,” the LD and the extrusion were significantly larger. In each group, there was no uniform tendency for the correlation coefficient of the parameters evaluated.

Conclusions

Our findings suggested (1) a larger meniscal LD at the baseline predicted progression of knee OA after 24 months and (2) a larger meniscal width and smaller meniscal angle predicted progression of knee OA after 24 months.

Knee menisci segmentation using convolutional neural networks: data from the Osteoarthritis Initiative

OBJECTIVE

To present a novel method for automated segmentation of knee menisci from MRIs. To evaluate quantitative meniscal biomarkers for osteoarthritis (OA) estimated thereof.

METHOD

A segmentation method employing convolutional neural networks in combination with statistical shape models was developed. Accuracy was evaluated on 88 manual segmentations. Meniscal volume, tibial coverage, and meniscal extrusion were computed and tested for differences between groups of OA, joint space narrowing (JSN), and WOMAC pain. Correlation between computed meniscal extrusion and MRI Osteoarthritis Knee Score (MOAKS) experts' readings was evaluated for 600 subjects. Suitability of biomarkers for predicting incident radiographic OA from baseline to 24 months was tested on a group of 552 patients (184 incident OA, 386 controls) by performing conditional logistic regression.

RESULTS

Segmentation accuracy measured as dice similarity coefficient was 83.8% for medial menisci (MM) and 88.9% for lateral menisci (LM) at baseline, and 83.1% and 88.3% at 12-month follow-up. Medial tibial coverage was significantly lower for arthritic cases compared to non-arthritic ones. Medial meniscal extrusion was significantly higher for arthritic knees. A moderate correlation between automatically computed medial meniscal extrusion and experts' readings was found (ρ = 0.44). Mean medial meniscal extrusion was significantly greater for incident OA cases compared to controls (1.16 ± 0.93 mm vs 0.83 ± 0.92 mm; P < 0.05).

CONCLUSION

Especially for medial menisci an excellent segmentation accuracy was achieved. Our meniscal biomarkers were validated by comparison to experts' readings as well as analysis of differences w.r.t groups of OA, JSN, and WOMAC pain. It was confirmed that medial meniscal extrusion is a predictor for incident OA.

Relationship between knee osteoarthritis and meniscal shape in observation of Japanese patients by using magnetic resonance imaging

Background

The aims of this study were to reveal the characteristics of the meniscal shape at each knee osteoarthritis (OA) severity level and to predict trends or patterns of the meniscal shape change as associated with knee OA progression.

Methods

Fifty-one patients diagnosed with knee OA based on X-ray and magnetic resonance (MR) images were evaluated. They were divided into three groups based on the Kellgren–Lawrence (KL) grade: normal group (KL grade of 0 or 1), mild group (KL grade of 2 or 3), and severe group (KL grade of 4). We measured the patients’ meniscal size and meniscal extrusion using MR images. In addition, semiquantitative measurement was performed using MR images to determine the arthritic status of the corresponding compartment using a whole-organ magnetic resonance imaging score (WORMS).

Results

The longitudinal diameter and posterior wedge angle of the medial meniscus were significantly larger, and the posterior wedge width of the medial meniscus was significantly smaller in the severe group than in the normal group. The WORMS scores for cartilage and osteophytes in the medial region were significantly different among the groups. The WORMS score of each region was strongly correlated with the longitudinal diameter. The WORMS scores of the lateral region were lower than those of the medial region.

Conclusion

Our observation of the shape change of the medial meniscus in the posterior region was roughly consistent with that in many previous studies of meniscal degeneration. On the other hand, we saw that the most relevant relation between the progression of the knee OA and the deformation of the meniscus was in the longitudinal direction.

Relationship Between Medial Meniscal Extrusion and Cartilage Loss in Specific Femorotibial Subregions: Data From the Osteoarthritis Initiative

OBJECTIVE

Medial meniscal extrusion is known to be related to structural progression of knee osteoarthritis. However, it is unclear whether medial meniscal extrusion is more strongly associated with cartilage loss in certain medial femorotibial subregions than in others.

METHODS

Segmentation of the medial tibial and femoral cartilage (baseline; 1-year followup) and the medial meniscus (baseline) was performed in 60 participants with frequent knee pain (mean ± SD ages 61.3 ± 9.2 years, body mass index 31.3 ± 3.9 kg/m(2) ) and with unilateral medial radiographic joint space narrowing (JSN) grades 1-3, using double-echo steady-state magnetic resonance images. Medial meniscal extrusion distance and extrusion area (percentage) between the external meniscal and tibial margin at baseline, and longitudinal medial cartilage loss in 8 anatomic subregions were determined.

RESULTS

A significant association (Pearson's correlation coefficient) was seen between medial meniscal extrusion area in JSN knees and cartilage loss over 1 year throughout the entire medial femorotibial compartment. The strongest correlation was with cartilage loss in the external medial tibia (r =-0.34, P < 0.01 in JSN; r =-0.30, P = 0.02 in knees without JSN).

CONCLUSION

Medial meniscal extrusion was associated with subsequent medial cartilage loss. The external medial tibial cartilage may be particularly vulnerable to thinning once the meniscus extrudes and its surface is exposed to direct, nonphysiological, cartilage-to-cartilage contact.

Conventional and novel imaging modalities in osteoarthritis: current state of the evidence

PURPOSE OF REVIEW

Imaging modalities are currently an inseparable part of osteoarthritis diagnosis. In this review, we describe the current state of evidence regarding conventional and novel imaging modalities in evaluation of osteoarthritis. Modalities including radiography (qualitative and semi-quantitative assessments), ultrasonography, computed tomography [CT; conventional multidetector CT (MDCT), cone-beam CT (CBCT) and four-dimensional CT (4DCT)], MRI (MRI; semi-quantitative, quantitative and compositional) and PET and their applications are reviewed.

RECENT FINDINGS

Radiography is the modality of choice for initial assessment of osteoarthritis. However, due to its low sensitivity and specificity, numerous recent investigations have proposed MRI as a powerful addition to detect and grade osteoarthritis features, which are not apparent in radiography. Semi-quantitative MRI measurements are feasible to perform in routine clinical practice. Quantitative and compositional MRI measurements have extended the amount of information an MRI examination can provide regarding the three-dimensional shape and tissue composition of articular cartilage. 4DCT and CBCT are introduced as imaging examinations that may reveal biomechanical cartilage abnormalities in osteoarthritis joint by dynamic and weight-bearing evaluations, respectively. Recent PET studies may unveil the underlying metabolic activities that can be associated with osteoarthritis.

SUMMARY

In addition to the established role of radiographs, MRI is the advanced modality of choice for detection and quantification of various osteoarthritis features. 4DCT and CBCT may have specified applications when diagnosis of underlying motion abnormality or dynamic changes in weight-bearing situation is suspected. Future studies should elucidate the specific clinical applications of ultrasonography and PET.

Longitudinal change in quantitative meniscus measurements in knee osteoarthritis--data from the Osteoarthritis Initiative

OBJECTIVE

We aimed to apply 3D MRI-based measurement technology to studying 2-year change in quantitative measurements of meniscus size and position.

METHODS

Forty-seven knees from the Osteoarthritis Initiative with medial radiographic joint space narrowing had baseline and 2-year follow-up MRIs. Quantitative measures were obtained from manual segmentation of the menisci and tibia using coronal DESSwe images. The standardized response mean (SRM = mean/SD change) was used as measure of sensitivity to longitudinal change.

RESULTS

Medial tibial plateau coverage decreased from 34.8% to 29.9% (SRM -0.82; p < 0.001). Change in medial meniscus extrusion in a central image (SRM 0.18) and in the central five slices (SRM 0.22) did not reach significance, but change in extrusion across the entire meniscus (SRM 0.32; p = 0.03) and in the relative area of meniscus extrusion (SRM 0.56; p < 0.001) did. There was a reduction in medial meniscus volume (10%; p < 0.001), width (7%; p < 0.001), and height (2%; p = 0.08); meniscus substance loss was strongest in the posterior (SRM -0.51; p = 0.001) and weakest in the anterior horn (SRM -0.15; p = 0.31).

CONCLUSION

This pilot study reports, for the first time, longitudinal change in quantitative 3D meniscus measurements in knee osteoarthritis. It provides evidence of improved sensitivity to change of 3D measurements compared with single slice analysis.

KEY POINTS

• First longitudinal MRI-based measurements of change of meniscus position and size. • Quantitative longitudinal evaluation of meniscus change in knee osteoarthritis. • Improved sensitivity to change of 3D measurements compared with single slice analysis.

Automated segmentation and analysis of normal and osteoarthritic knee menisci from magnetic resonance images--data from the Osteoarthritis Initiative

OBJECTIVE

To validate an automatic scheme for the segmentation and quantitative analysis of the medial meniscus (MM) and lateral meniscus (LM) in magnetic resonance (MR) images of the knee.

METHOD

We analysed sagittal water-excited double-echo steady-state MR images of the knee from a subset of the Osteoarthritis Initiative (OAI) cohort. The MM and LM were automatically segmented in the MR images based on a deformable model approach. Quantitative parameters including volume, subluxation and tibial-coverage were automatically calculated for comparison (Wilcoxon tests) between knees with variable radiographic osteoarthritis (rOA), medial and lateral joint space narrowing (mJSN, lJSN) and pain. Automatic segmentations and estimated parameters were evaluated for accuracy using manual delineations of the menisci in 88 pathological knee MR examinations at baseline and 12 months time-points.

RESULTS

The median (95% confidence-interval (CI)) Dice similarity index (DSI) (2 ∗|Auto ∩ Manual|/(|Auto|+|Manual|)∗ 100) between manual and automated segmentations for the MM and LM volumes were 78.3% (75.0-78.7), 83.9% (82.1-83.9) at baseline and 75.3% (72.8-76.9), 83.0% (81.6-83.5) at 12 months. Pearson coefficients between automatic and manual segmentation parameters ranged from r = 0.70 to r = 0.92. MM in rOA/mJSN knees had significantly greater subluxation and smaller tibial-coverage than no-rOA/no-mJSN knees. LM in rOA knees had significantly greater volumes and tibial-coverage than no-rOA knees.

CONCLUSION

Our automated method successfully segmented the menisci in normal and osteoarthritic knee MR images and detected meaningful morphological differences with respect to rOA and joint space narrowing (JSN). Our approach will facilitate analyses of the menisci in prospective MR cohorts such as the OAI for investigations into pathophysiological changes occurring in early osteoarthritis (OA) development.

Imaging research results from the osteoarthritis initiative (OAI): a review and lessons learned 10 years after start of enrolment

The Osteoarthritis Initiative (OAI) is a multicentre, prospective, observational, cohort study of knee osteoarthritis (OA) that began recruitment in 2004. The OAI provides public access to clinical and image data, enabling researchers to examine risk factors/predictors and the natural history of knee OA incidence and progression, and the qualification of imaging and other biomarkers. In this narrative review, we report imaging findings and lessons learned 10 years after enrolment has started. A literature search for full text articles published from the OAI was performed up to 31 December 2013 using Pubmed and the OAI web page. We summarise the rationale, design and imaging protocol of the OAI, and the history of OAI publications. We review studies from early partial, and later full OAI public data releases. The latter are structured by imaging method and tissue, reviewing radiography and then MRI findings on cartilage morphology, cartilage lesions and composition (T2), bone, meniscus, muscle and adipose tissue. Finally, analyses directly comparing findings from MRI and radiography are summarised. Ten years after the first participants were enrolled and first papers published, the OAI has become an invaluable resource to the OA research community. It has fuelled novel methodological approaches of analysing images, and has provided a wealth of information on OA pathophysiology. Continued collection and public release of long-term observations will help imaging measures to gain scientific and regulatory acceptance as 'prognostic' or 'efficacy of intervention' biomarkers, potentially enabling shorter and more efficient clinical trials that can test structure-modifying therapeutic interventions (NCT00080171).