Variable angle gray level co-occurrence matrix analysis of T2 relaxation time maps reveals degenerative changes of cartilage in knee osteoarthritis: Oulu knee osteoarthritis study

Quantitative MRI methods for the assessment of structure, composition, and function of musculoskeletal tissues in basic research and preclinical applications

Osteoarthritis (OA) is a disabling chronic disease involving the gradual degradation of joint structures causing pain and dysfunction. Magnetic resonance imaging (MRI) has been widely used as a non-invasive tool for assessing OA-related changes. While anatomical MRI is limited to the morphological assessment of the joint structures, quantitative MRI (qMRI) allows for the measurement of biophysical properties of the tissues at the molecular level. Quantitative MRI techniques have been employed to characterize tissues' structural integrity, biochemical content, and mechanical properties. Their applications extend to studying degenerative alterations, early OA detection, and evaluating therapeutic intervention. This article is a review of qMRI techniques for musculoskeletal tissue evaluation, with a particular emphasis on articular cartilage. The goal is to describe the underlying mechanism and primary limitations of the qMRI parameters, their association with the tissue physiological properties and their potential in detecting tissue degeneration leading to the development of OA with a primary focus on basic and preclinical research studies. Additionally, the review highlights some clinical applications of qMRI, discussing the role of texture-based radiomics and machine learning in advancing OA research.

Predicting osteoarthritis onset and progression with 3D texture analysis of cartilage MRI DESS: 6-Year data from osteoarthritis initiative

In this study, we developed a gray level co-occurrence matrix-based 3D texture analysis method for dual-echo steady-state (DESS) magnetic resonance (MR) images to be used for knee cartilage analysis in osteoarthritis (OA) studies and use it to study changes in articular cartilage between different subpopulations based on their rate of progression into radiographically confirmed OA. In total, 642 series of right knee DESS MR images at 3T were obtained from baseline, 36- and 72-month follow-ups from the OA Initiative database. At baseline, all 214 subjects included in the study had Kellgren-Lawrence (KL) grade <2. Three groups were defined, based on time of progression into radiographic OA (ROA) (KL grades ≥2): control (no progression), fast progressor (ROA at 36 months), and slow progressor (ROA at 72 months) groups. 3D texture analysis was used to extract textural features for femoral and tibial cartilages. All textural features, in both femur and tibia, showed significant longitudinal changes across all groups and tissue layers. Most of the longitudinal changes were observed in progressors, but significant changes were observed also in controls. Differences between groups were mostly seen at baseline and 72 months. The method is sensitive to cartilage changes before and after ROA. It was able to detect longitudinal changes in controls and progressors and to distinguish cartilage alterations due to OA and aging. Moreover, it was able to distinguish controls and different progressor groups before any radiographic signs of OA and during OA. Thus, texture analysis could be used as a marker for the onset and progression of OA.

MRI-based Texture Analysis of Infrapatellar Fat Pad to Predict Knee Osteoarthritis Incidence

Background Infrapatellar fat pad (IPFP) quality has been implicated as a marker for predicting knee osteoarthritis (KOA); however, no valid quantification for subtle IPFP abnormalities has been established. Purpose To investigate whether MRI-based three-dimensional texture analysis of IPFP abnormalities could help predict incident radiographic KOA. Materials and Methods In this prospective nested case-control study, 690 participants whose knees were at risk for KOA were included from the Pivotal Osteoarthritis Initiative MRI Analyses incident osteoarthritis cohort. All knees had a Kellgren-Lawrence grade of 1 or less at baseline. During the 4-year follow-up, case participants were matched 1:1 to control participants, with incident radiographic KOA as the outcome. MRI scans were segmented at the incident time point of KOA (hereafter, P0), 1 year before P0 (hereafter, P-1), and baseline. MRI-based three-dimensional texture analysis was performed to extract IPFP texture features. Least absolute shrinkage and selection operator and multivariable logistic regressions were applied in the development cohort and evaluated in the test cohort. The area under the receiver operating characteristic curve (AUC) was used to evaluate the discriminative value of the clinical score, IPFP texture score, and MRI Osteoarthritis Knee Score. Results Participants were allocated to development (n = 500, 340 women; mean age, 60 years) and test (n = 190, 120 women; mean age, 61 years) cohorts. In both cohorts, IPFP texture scores (AUC ≥0.75 for all) showed greater discrimination than clinical scores (AUC ≤0.69 for all) at baseline, P-1, and P0, with significant differences in pairwise comparisons (P ≤ .002 for all). Greater predictive and concurrent validities of IPFP texture scores (AUC ≥0.75 for all) compared with MRI Osteoarthritis Knee Scores (AUC ≤0.66 for all) were also demonstrated (P < .001 for all). Conclusion MRI-based three-dimensional texture of the infrapatellar fat pad was associated with future development of knee osteoarthritis. ClinicalTrials.gov registration no.: NCT00080171 © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Fischer in this issue.

Differentiation of Cartilage Repair Techniques Using Texture Analysis from T2 Maps

OBJECTIVE

The aim of this study was to investigate texture features from T2 maps as a marker for distinguishing the maturation of repair tissue after 2 different cartilage repair procedures.

DESIGN

Seventy-nine patients, after either microfracture (MFX) or matrix-associated chondrocyte transplantation (MACT), were examined on a 3-T magnetic resonance (MR) scanner with morphological and quantitative (T2 mapping) MR sequences 2 years after surgery. Twenty-one texture features from a gray-level co-occurrence matrix (GLCM) were extracted. The texture feature difference between 2 repair types was assessed individually for the femoral condyle and trochlea/anterior condyle using linear regression models. The stability and reproducibility of texture features for focal cartilage were calculated using intra-observer variability and area under curve from receiver operating characteristics.

RESULTS

There was no statistical significance found between MFX and MACT for T2 values (P = 0.96). There was, however, found a statistical significance between MFX and MACT in femoral condyle in GLCM features autocorrelation (P < 0.001), sum of squares (P = 0.023), sum average (P = 0.005), sum variance (P = 0.0048), and sum entropy (P = 0.05); and in anterior condyle/trochlea homogeneity (P = 0.02) and dissimilarity (P < 0.001).

CONCLUSION

Texture analysis using GLCM provides a useful extension to T2 mapping for the characterization of cartilage repair tissue by increasing its sensitivity to tissue structure. Some texture features were able to distinguish between repair tissue after different cartilage repair procedures, as repair tissue texture (and hence, probably collagen organization) 24 months after MACT more closely resembled healthy cartilage than did MFX repair tissue.

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

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 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.).

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.

Texture analysis in the classification of T2 -weighted magnetic resonance images in persons with and without low back pain

Magnetic resonance imaging findings often do not distinguish between people with and without low back pain (LBP). However, there are still a large number of people who undergo magnetic resonance imaging to help determine the etiology of their back pain. Texture analysis shows promise for the classification of tissues that look similar, and machine learning can minimize the number of comparisons. This study aimed to determine if texture features from lumbar spine magnetic resonance imaging differ between people with and without LBP. In total, 14 participants with chronic LBP were matched for age, weight, and gender with 14 healthy volunteers. A custom texture analysis software was used to construct a gray-level co-occurrence matrix with one to four pixels offset in 0° direction for the disc and superior and inferior endplate regions. The Random Forests Algorithm was used to select the most promising classifiers. The linear mixed-effect model analysis was used to compare groups (pain vs. pain-free) at each level controlling for age. The Random Forest Algorithm recommended focusing on intervertebral discs and endplate zones at L4-5 and L5-S1. Differences were observed between groups for L5-S1 superior endplate contrast, homogeneity, and energy (p = .02). Differences were observed for L5-S1 disc contrast and homogeneity (p < .01), as well as for the inferior endplates contrast, homogeneity, and energy (p < .03). Magnetic resonance imaging textural features may have potential in identifying structures that may be the target of further investigations about the reasons for LBP.

Radiomics Feature Analysis of Cartilage and Subchondral Bone in Differentiating Knees Predisposed to Posttraumatic Osteoarthritis after Anterior Cruciate Ligament Reconstruction from Healthy Knees

Objectives

To introduce a new implementation of radiomics analysis for cartilage and subchondral bone of the knee and to compare the performance of the proposed models to classic T2 relaxation time in distinguishing knees predisposed to posttraumatic osteoarthritis (PTOA) after anterior cruciate ligament reconstruction (ACLR) and healthy controls.

Methods

114 patients following ACLR after at least 2 years and 43 healthy controls were reviewed and allocated to training (n = 110) and testing (n = 47) cohorts. Radiomics models are built for cartilage and subchondral bone regions of different compartments: lateral femur (LF), lateral tibia (LT), medial femur (MF), and medial tibia (MT) and combined models of four compartments on T2 mapping images. The model performance of discrimination between patients and controls was illustrated with the receiver operating characteristic curve and compared with a classic T2 value-based model.

Results

The T2 value model of cartilage yielded moderate predictive performance in discerning patients and controls, with an AUC of 0.731 (95% confidence interval, 0.556–0.875) in the testing cohort, while the radiomics signature of cartilage and subchondral bone of different compartments demonstrated excellent performance, with AUCs of 0.864–0.979. Furthermore, the combined model reported an even better performance, with AUCs of 0.977 (95% confidence interval, 0.919–1.000) for the cartilage and 0.934 (95% confidence interval, 0.865–0.994) for the subchondral bone in the testing cohort.

Conclusion

The radiomics features of the cartilage and subchondral bone may be able to provide powerful tools with more sensitive detection than T2 values in differentiating knees at risk for PTOA after ACLR from healthy knees.

Machine learning classification on texture analyzed T2 maps of osteoarthritic cartilage: oulu knee osteoarthritis study

OBJECTIVE

To introduce local binary pattern (LBP) texture analysis to cartilage osteoarthritis (OA) research and compare the performance of different classification systems in discrimination of OA subjects from healthy controls using gray-level co-occurrence matrix (GLCM) and LBP texture data. Classification algorithms were used to reduce the dimensionality of texture data into a likelihood of subject belonging to the reference class.

METHOD

T2 relaxation time mapping with multi-slice multi-echo spin echo sequence was performed for eighty symptomatic OA patients and 63 asymptomatic controls on a 3T clinical MRI scanner. Relaxation time maps were subjected to GLCM and LBP texture analysis, and classification algorithms were deployed with an in-house developed software. Implemented algorithms were K nearest neighbors, support vector machine, and neural network classifier.

RESULTS

LBP and GLCM discerned OA patients from controls with a significant difference in all studied regions. Classification models comprising GLCM and LBP showed high accuracy in classing OA patients and controls. The best performance was obtained with a multilayer perceptron type classifier with an overall accuracy of 90.2 %.

CONCLUSION

LBP texture analysis complements prior results with GLCM, and together LBP and GLCM serve as significant input data for classification algorithms trained for OA assessment. Presented algorithms are adaptable to versatile OA evaluations also for future gradational or predictive approaches.

Longitudinal T2 Mapping and Texture Feature Analysis in the Detection and Monitoring of Experimental Post-Traumatic Cartilage Degeneration

Background: Traumatic cartilage injuries predispose articulating joints to focal cartilage defects and, eventually, posttraumatic osteoarthritis. Current clinical-standard imaging modalities such as morphologic MRI fail to reliably detect cartilage trauma and to monitor associated posttraumatic degenerative changes with oftentimes severe prognostic implications. Quantitative MRI techniques such as T2 mapping are promising in detecting and monitoring such changes yet lack sufficient validation in controlled basic research contexts. Material and Methods: 35 macroscopically intact cartilage samples obtained from total joint replacements were exposed to standardized injurious impaction with low (0.49 J, n = 14) or high (0.98 J, n = 14) energy levels and imaged before and immediately, 24 h, and 72 h after impaction by T2 mapping. Contrast, homogeneity, energy, and variance were quantified as features of texture on each T2 map. Unimpacted controls (n = 7) and histologic assessment served as reference. Results: As a function of impaction energy and time, absolute T2 values, contrast, and variance were significantly increased, while homogeneity and energy were significantly decreased. Conclusion: T2 mapping and texture feature analysis are sensitive diagnostic means to detect and monitor traumatic impaction injuries of cartilage and associated posttraumatic degenerative changes and may be used to assess cartilage after trauma to identify “cartilage at risk”.

Current status of functional MRI of osteoarthritis for diagnosis and prognosis

PURPOSE OF REVIEW

Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials.

RECENT FINDINGS

Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T1ρ) and collagen organization (T2, T2, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process.

SUMMARY

Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.

Defective WNT signaling may protect from articular cartilage deterioration - a quantitative MRI study on subjects with a heterozygous WNT1 mutation

OBJECTIVE

WNT signaling is of key importance in chondrogenesis and defective WNT signaling may contribute to the pathogenesis of osteoarthritis and other cartilage diseases. Biochemical composition of articular cartilage in patients with aberrant WNT signaling has not been studied. Our objective was to assess the knee articular cartilage in WNT1 mutation-positive individuals using a 3.0T MRI unit to measure cartilage thickness, relaxation times, and texture features.

DESIGN

Cohort comprised mutation-positive (N = 13; age 17-76 years) and mutation-negative (N = 13; 16-77 years) subjects from two Finnish families with autosomal dominant WNT1 osteoporosis due to a heterozygous missense mutation c.652T>G (p.C218G) in WNT1. All subjects were imaged with a 3.0T MRI unit and assessed for cartilage thickness, T2 and T1ρ relaxation times, and T2 texture features contrast, dissimilarity and homogeneity of T2 relaxation time maps in six regions of interest (ROIs) in the tibiofemoral cartilage.

RESULTS

All three texture features showed opposing trends with age between the groups in the medial tibiofemoral cartilage (P = 0.020-0.085 for the difference of the regression coefficients), the mutation-positive individuals showing signs of cartilage preservation. No significant differences were observed in the lateral tibiofemoral cartilage. Cartilage thickness and means of T2 relaxation time did not differ between groups. Means of T1ρ relaxation time were significantly different in one ROI but the regression analysis displayed no differences.

CONCLUSIONS

Our results show less age-related cartilage deterioration in the WNT1 mutation-positive than the mutation-negative subjects. This suggests, that the WNT1 mutation may alter cartilage turnover and even have a potential cartilage-preserving effect.

The Possibilities of Magnetic Resonance Imaging in the Diagnosis of Microstructural Changes of the Subchondral Bone in Osteoarthritis

Background. Magnetic resonance imaging not only has powerful capabilities for visualization, but is also of interest in terms of obtaining ideas about microstructural and biochemical changes in the tissues of the joints in osteoarthritis.

Aims. To assess the possibility of T2-images of magnetic resonance imaging in the diagnosis of microstructural changes in the subchondral bone in osteoarthritis.

Materials and methods. 62 patients with osteoarthritis and 8 volunteers without osteoarthritis were examined. All patients underwent magnetic resonance imaging of knee. To assess the variability of transverse relaxation time, the T2-images segmentation of the subchondral segmentation in the frontal projection was performed by hand. The proton density was estimated from a 3D histogram on a scale of 0 to 255.

Results. At the first stage of osteoarthritis, the intensity of the magnetic resonance signal decreases over the entire surface of the tibial plateau, with minimal values in the region of the medial part of the knee joint. At stage 2 osteoarthritis, there was an even greater decrease in the number of protons that made the phase transition with the lowest value in the medial region. The subchondral bone texture in stage 3 was characterized by a significant decrease in signal intensity in the region of the medial plateau of the tibia. In the terminal stage of osteoarthritis.

Conclusion. The revealed regularity of the change in the relaxation time spectrum of T2-images reflects the degenerative process in subchondral bone with osteoarthritis.