Fourier-transform infrared study on effects of ageing, oestrogen level and altered dietary loading on rat mandibular condylar cartilage.

OBJECTIVE: Mandibular condylar cartilage (MCC) of the rat was examined with the Fourier-transform infrared (FITR) spectroscopic imaging to study the effects of ageing, oestrogen level and altered dietary loading on the structure of MCC. MATERIALS AND METHODS: The Sprague-Dawley rats (n = 96) aged 5 and 14 months were divided into 12 subgroups according to age, oestrogen status (ovariectomized [OVX], non-ovariectomized [non-OVX)]) and diet (hard, normal, soft). Specimens of the MCC were examined with FTIR spectroscopic imaging to quantify the distribution of collagens and proteoglycans. MCC was divided sagittally into three segments: anterior, most superior and posterior. From each segment, the collagen and proteoglycan contents at different depths of cartilage were statistically compared between the groups using an N-way analysis of variance (ANOVA). RESULTS: The amount of collagen content was significantly associated with old age in the deep layer of the anterior segment and in the middle layer of the posterior segment of MCC. In the deep layer of the most superior segment, the collagen content also increased with ageing. The amount of proteoglycan content increased significantly when dietary loading increased, and the oestrogen level decreased in the deep layer of the most superior segment of MCC. CONCLUSION: Ageing, oestrogen level and altered dietary loading have a significant effect on the location and content of collagens and proteoglycans of rat MCC. Ageing significantly increased the amount of collagen content in the superior and posterior segments, being highest in the older soft-diet rats. Decreased oestrogen levels and increased dietary loading increased the amount of proteoglycan content.

The ultrasound assessment of osteoarthritis: the current status.

Traditionally, osteoarthritis (OA) is diagnosed with the clinical examination supplemented by the conventional radiography (CR). In the research literature, the role of ultrasound (US) imaging in the diagnostics of OA has risen steadily during the last two decades. US imaging is cheap and globally widely available often already in primary healthcare. Here, we reviewed the most essential US literature focusing on OA diagnostics and progression prediction using the various search engines. Starting from the year 2000, our search provided 1 445 journal articles. After reviewing the abstracts, 89 articles were finally included. Most of the reviewed articles focused on the imaging of knee and hand OA, whereas only a minority dealt with the imaging of hip, ankle, midfoot, acromioclavicular, and temporomandibular joints. Overall, during the last 20 years, the use of US imaging for OA assessment has increased in the scientific literature. In knee and hand joints, US imaging has been reported to be a promising tool to evaluate OA changes. Furthermore, the reproducibility of US as well as its association to MRI findings are excellent. Importantly, US seems to even outperform CR in certain aspects, such as detection of osteophytes, joint inflammation, meniscus protrusion, and localized cartilage damage (especially at the medial femoral condyle and sulcus area). Based on the reviewed literature, US can be truly considered as a complementary tool to CR in the clinical setup for OA diagnostics. New technical developments may even enhance the diagnostic value of the US in the future.

Diagnostic Performance of Ultrasonography for Evaluation of Osteoarthritis of Ankle Joint: Comparison With Radiography, Cone-Beam CT, and Symptoms.

OBJECTIVES: To determine the diagnostic performance of ultrasonography (US) for evaluation of the ankle joint osteoarthritic (OA) changes. Cone-beam computed tomography (CT) was used as the gold standard and US performance was compared with conventional radiography (CR). As a secondary aim, associations between the imaging findings and ankle symptoms were assessed. METHODS: US was performed to 51 patients with ankle OA. Every patient had prior ankle CR and underwent cone-beam CT during the same day as US examination. On US, effusion/synovitis, osteophytes, talar cartilage damage, and tenosynovitis were evaluated. Comparison to respective imaging findings on CR and cone-beam CT was then performed. Single radiologist blinded to other modalities assessed all the imaging studies. Symptoms questionnaire, the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC), was available for 48 patients. RESULTS: US detected effusion/synovitis of the talocrural joint with 45% sensitivity and 90% specificity. For the detection of anterior talocrural osteophytes, US sensitivity was 78% and specificity 79%. For the medial talocrural osteophytes, they were 39 and 83%, and for the lateral talocrural osteophytes 54 and 100%, respectively. Considering cartilage damage of the talus, US yielded a low sensitivity of 18% and high specificity of 97%. Overall, the performance of US was only moderate and comparable to CR. The imaging findings showed only weak associations with ankle symptoms. CONCLUSIONS: The ability of US to detect ankle OA is only moderate. Interestingly, performance of CR also remained moderate. The associations between imaging findings and WOMAC score seem to be weak in ankle OA.

Preprocessing Strategies for Sparse Infrared Spectroscopy: A Case Study on Cartilage Diagnostics.

The aim of the study was to optimize preprocessing of sparse infrared spectral data. The sparse data were obtained by reducing broadband Fourier transform infrared attenuated total reflectance spectra of bovine and human cartilage, as well as of simulated spectral data, comprising several thousand spectral variables into datasets comprising only seven spectral variables. Different preprocessing approaches were compared, including simple baseline correction and normalization procedures, and model-based preprocessing, such as multiplicative signal correction (MSC). The optimal preprocessing was selected based on the quality of classification models established by partial least squares discriminant analysis for discriminating healthy and damaged cartilage samples. The best results for the sparse data were obtained by preprocessing using a baseline offset correction at 1800 cm-1, followed by peak normalization at 850 cm-1 and preprocessing by MSC.

Preclassification of Broadband and Sparse Infrared Data by Multiplicative Signal Correction Approach.

Preclassification of raw infrared spectra has often been neglected in scientific literature. Separating spectra of low spectral quality, due to low signal-to-noise ratio, presence of artifacts, and low analyte presence, is crucial for accurate model development. Furthermore, it is very important for sparse data, where it becomes challenging to visually inspect spectra of different natures. Hence, a preclassification approach to separate infrared spectra for sparse data is needed. In this study, we propose a preclassification approach based on Multiplicative Signal Correction (MSC). The MSC approach was applied on human and the bovine knee cartilage broadband Fourier Transform Infrared (FTIR) spectra and on a sparse data subset comprising of only seven wavelengths. The goal of the preclassification was to separate spectra with analyte-rich signals (i.e., cartilage) from spectra with analyte-poor (and high-matrix) signals (i.e., water). The human datasets 1 and 2 contained 814 and 815 spectra, while the bovine dataset contained 396 spectra. A pure water spectrum was used as a reference spectrum in the MSC approach. A threshold for the root mean square error (RMSE) was used to separate cartilage from water spectra for broadband and the sparse spectral data. Additionally, standard noise-to-ratio and principle component analysis were applied on broadband spectra. The fully automated MSC preclassification approach, using water as reference spectrum, performed as well as the manual visual inspection. Moreover, it enabled not only separation of cartilage from water spectra in broadband spectral datasets, but also in sparse datasets where manual visual inspection cannot be applied.

Integrin α11ß1 is a receptor for collagen XIII.

Collagen XIII is a conserved transmembrane collagen mainly expressed in mesenchymal tissues. Previously, we have shown that collagen XIII modulates tissue development and homeostasis. Integrins are a family of receptors that mediate signals from the environment into the cells and vice versa. Integrin α11ß1 is a collagen receptor known to recognize the GFOGER (O=hydroxyproline) sequence in collagens. Interestingly, collagen XIII and integrin α11ß1 both have a role in the regulation of bone homeostasis. To study whether α11ß1 is a receptor for collagen XIII, we utilized C2C12 cells transfected to express α11ß1 as their only collagen receptor. The interaction between collagen XIII and integrin α11ß1 was also confirmed by surface plasmon resonance and pull-down assays. We discovered that integrin α11ß1 mediates cell adhesion to two collagenous motifs, namely GPKGER and GF(S)QGEK, that were shown to act as the recognition sites for the integrin α11-I domain. Furthermore, we studied the in vivo significance of the α11ß1-collagen XIII interaction by crossbreeding α11 null mice (Itga11-/-) with mice overexpressing Col13a1 (Col13a1oe). When we evaluated the bone morphology by microcomputed tomography, Col13a1oe mice had a drastic bone overgrowth followed by severe osteoporosis, whereas the double mutant mouse line showed a much milder bone phenotype. To conclude, our data identifies integrin α11ß1 as a new collagen XIII receptor and demonstrates that this ligand-receptor pair has a role in the maintenance of bone homeostasis.

Automated analysis of rabbit knee calcified cartilage morphology using micro-computed tomography and deep learning.

Structural dynamics of calcified cartilage (CC) are poorly understood. Conventionally, CC structure is analyzed using histological sections. Micro-computed tomography (µCT) allows for three-dimensional (3D) imaging of mineralized tissues; however, the segmentation between bone and mineralized cartilage is challenging. Here, we present state-of-the-art deep learning segmentation for µCT images to assess 3D CC morphology. The sample includes 16 knees from 12 New Zealand White rabbits dissected into osteochondral samples from six anatomical regions: lateral and medial femoral condyles, lateral and medial tibial plateaus, femoral groove, and patella (n = 96). The samples were imaged with µCT and processed for conventional histology. Manually segmented CC from the images was used to train segmentation models with different encoder-decoder architectures. The models with the greatest out-of-fold evaluation Dice score were selected. CC thickness was compared across 24 regions, co-registered between the imaging modalities using Pearson correlation and Bland-Altman analyses. Finally, the anatomical CC thickness variation was assessed via a Linear Mixed Model analysis. The best segmentation models yielded average Dice of 0.891 and 0.807 for histology and µCT segmentation, respectively. The correlation between the co-registered regions was strong (r = 0.897, bias = 21.9 µm, standard deviation = 21.5 µm). Finally, both methods could separate the CC thickness between the patella, femoral, and tibial regions (p < 0.001). As a conclusion, the proposed µCT analysis allows for ex vivo 3D assessment of CC morphology. We demonstrated the biomedical relevance of the method by quantifying CC thickness in different anatomical regions with a varying mean thickness. CC was thickest in the patella and thinnest in the tibial plateau. Our method is relatively straightforward to implement into standard µCT analysis pipelines, allowing the analysis of CC morphology. In future research, µCT imaging might be preferable to histology, especially when analyzing dynamic changes in cartilage mineralization. It could also provide further understanding of 3D morphological changes that may occur in mineralized cartilage, such as thickening of the subchondral plate in osteoarthritis and other joint diseases.

Mineralization of dental tissues and caries lesions detailed with Raman microspectroscopic imaging.

Dental caries is the most common oral disease that causes demineralization of the enamel and later of the dentin. Depth-wise assessment of the demineralization process could be used to help in treatment planning. In this study, we aimed to provide baseline information for the development of a Raman probe by characterizing the mineral composition of the dental tissues from large composition maps (6 × 3 mm2 with 15 µm step size) using Raman microspectroscopy. Ten human wisdom teeth with different stages of dental caries lesions were examined. All of the teeth were cut in half at representative locations of the caries lesions and then imaged with a Raman imaging microscope. The pre-processed spectral maps were combined into a single data matrix, and the spectra of the enamel, dentin, and caries were identified by K-means cluster analysis. Our results showed that unsupervised identification of dental caries is possible with the K-means clustering. The compositional analysis revealed that the carious lesions are less mineralized than the healthy enamel, and when the lesions extend into the dentin, they are even less mineralized. Furthermore, there were more carbonate imperfections in the mineral crystal lattice of the caries tissues than in healthy tissues. Interestingly, we observed gradients in the sound enamel showing higher mineralization and greater mineral crystal perfection towards the tooth surface. To conclude, our results provide a baseline for the methodological development aimed at clinical diagnostics for the early detection of active caries lesions.

Ultrasonographic Assessment of the Normal Femoral Articular Cartilage of the Knee Joint: Comparison with 3D MRI.

OBJECTIVE: Ultrasonography (US) has a promising role in evaluating the knee joint, but capability to visualize the femoral articular cartilage needs systematic evaluation. We measured the extent of this acoustic window by comparing standardized US images with the corresponding MRI views of the femoral cartilage. DESIGN: Ten healthy volunteers without knee pathology underwent systematic US and MRI evaluation of both knees. The femoral cartilage was assessed on the oblique transverse axial plane with US and with 3D MRI. The acoustic window on US was compared to the corresponding views of the femoral sulcus and both condyles on MRI. The mean imaging coverage of the femoral cartilage and the cartilage thickness measurements on US and MRI were compared. RESULTS: Mean imaging coverage of the cartilage of the medial femoral condyle was 66% (range 54%-80%) and on the lateral femoral condyle 37% (range 25%-51%) compared with MRI. Mean cartilage thickness measurement in the femoral sulcus was 3.17 mm with US and 3.61 mm with MRI (14.0% difference). The corresponding measurements in the medial femoral condyle were 1.95 mm with US and 2.35 mm with MRI (21.0% difference), and in the lateral femoral condyle, they were 2.17 mm and 2.73 mm (25.6% difference), respectively. CONCLUSION: Two-thirds of the articular cartilage of the medial femoral condyle, and one-third in the lateral femoral condyle, can be assessed with US. The cartilage thickness measurements seem to be underestimated by US. These results show promise for the evaluation of the weight-bearing cartilage of the medial femoral condyle with US.

State of the Art: Imaging of Osteoarthritis-Revisited 2020.

Osteoarthritis (OA) is a highly prevalent chronic condition with marked implications for affected individuals and public health care. There are available treatments to manage pain and symptoms but no effective treatment for OA. In the past 10 years, joint imaging, particularly MRI, has evolved rapidly due to technical advances and their application to clinical research, which has led to abundant evidence regarding the natural history of the disease. Radiography remains the primary imaging modality in clinical practice for the diagnosis and follow-up of OA. The many developments in MRI techniques capable of assessing cartilage morphologic features and the methods for evaluating its biochemical composition will be discussed. Advances in quantitative morphologic cartilage assessment and semiquantitative whole-organ assessment will be reviewed, as will other modalities such as US, CT and CT arthrography, and nuclear medicine techniques that play a complementary role. Various therapeutic approaches and ongoing developments, including the impact of artificial intelligence on the field of OA imaging, will also be discussed.

Nanotechnological Strategies for Osteoarthritis Diagnosis, Monitoring, Clinical Management, and Regenerative Medicine: Recent Advances and Future Opportunities.

PURPOSE OF REVIEW: In this review article, we discuss the potential for employing nanotechnological strategies for the diagnosis, monitoring, and clinical management of osteoarthritis (OA) and explore how nanotechnology is being integrated rapidly into regenerative medicine for OA and related osteoarticular disorders. RECENT FINDINGS: We review recent advances in this rapidly emerging field and discuss future opportunities for innovations in enhanced diagnosis, prognosis, and treatment of OA and other osteoarticular disorders, the smart delivery of drugs and biological agents, and the development of biomimetic regenerative platforms to support cell and gene therapies for arresting OA and promoting cartilage and bone repair. Nanotubes, magnetic nanoparticles, and other nanotechnology-based drug and gene delivery systems may be used for targeting molecular pathways and pathogenic mechanisms involved in OA development. Nanocomposites are also being explored as potential tools for promoting cartilage repair. Nanotechnology platforms may be combined with cell, gene, and biological therapies for the development of a new generation of future OA therapeutics. Graphical Abstract.

Clinical Contrast-Enhanced Computed Tomography With Semi-Automatic Segmentation Provides Feasible Input for Computational Models of the Knee Joint.

Computational models can provide information on joint function and risk of tissue failure related to progression of osteoarthritis (OA). Currently, the joint geometries utilized in modeling are primarily obtained via manual segmentation, which is time-consuming and hence impractical for direct clinical application. The aim of this study was to evaluate the applicability of a previously developed semi-automatic method for segmenting tibial and femoral cartilage to serve as input geometry for finite element (FE) models. Knee joints from seven volunteers were first imaged using a clinical computed tomography (CT) with contrast enhancement and then segmented with semi-automatic and manual methods. In both segmentations, knee joint models with fibril-reinforced poroviscoelastic (FRPVE) properties were generated and the mechanical responses of articular cartilage were computed during physiologically relevant loading. The mean differences in the absolute values of maximum principal stress, maximum principal strain, and fibril strain between the models generated from semi-automatic and manual segmentations were <1 MPa, <0.72% and <0.40%, respectively. Furthermore, contact areas, contact forces, average pore pressures, and average maximum principal strains were not statistically different between the models (p >0.05). This semi-automatic method speeded up the segmentation process by over 90% and there were only negligible differences in the results provided by the models utilizing either manual or semi-automatic segmentations. Thus, the presented CT imaging-based segmentation method represents a novel tool for application in FE modeling in the clinic when a physician needs to evaluate knee joint function.

Association between grayscale sonographic and clinical findings in severe knee osteoarthritis.

PURPOSE: To assess whether ultrasonographic (US) findings associate with clinical findings in severe knee osteoarthritis (OA). Association of US findings with side-of-knee pain and inter-reader agreement of knee US were also evaluated. METHODS: One-hundred-two patients (in total 123 knees) with severe knee OA were recruited for this cross-sectional study. US was performed by a single observer, and on 53 knees by two independent observers to assess inter-reader reliability. Preoperative clinical data was available for 69 knees. Cutoff values were applied to dichotomize US and clinical findings. The Chi-square test, Mann-Whitney test, and prevalence- and bias-adjusted kappa (PABAK) were applied for statistical analyses. RESULTS: Seven of 99 associations tested were statistically significant. Associations were observed between range of flexion and lateral femoral (P = .009) and tibial (P = .001) osteophytes, mediolateral instability and damage to the lateral femoral cartilage (P = .014) and damage to the lateral meniscus (P = .031), and alignment and damage to the lateral femoral cartilage (P < .001), lateral tibial osteophytes (P = .037), and damage to the lateral meniscus (P < .001). A strong association was observed between medial-sided pain and same-sided cartilage damage and osteophytes (P < .001). That inter-reader agreement was excellent on the medial side of the knee joint (PABAK = 0.811-0.887). CONCLUSIONS: US findings show a rather poor association with clinical OA findings. Inter-reader agreement of knee US is excellent on the medial side.

WS2 and MoS2 thin film gas sensors with high response to NH3 in air at low temperature.

Transition metal dichalcogenides (TMDs) have received immense research interest in particular for their outstanding electrochemical and optoelectrical properties. Lately, chemical gas sensor applications of TMDs have been recognized as well owing to the low operating temperatures of devices, which is a great advantage over conventional metal oxide based sensors. In this work, we elaborate on the gas sensing properties of WS2 and MoS2 thin films made by simple and straightforward thermal sulfurization of sputter deposited metal films on silicon chips. The sensor response to H2, H2S, CO and NH3 analytes in air at 30 °C has been assessed and both MoS2 and WS2 were found to have an excellent selectivity to NH3 with a particularly high sensitivity of 0.10 ± 0.02 ppm-1 at sub-ppm concentrations in the case of WS2. The sensing behavior is explained on the bases of gas adsorption energies as well as carrier (hole) localization induced by the surface adsorbed moieties having reductive nature.

Anterior cruciate ligament transection alters the n-3/n-6 fatty acid balance in the lapine infrapatellar fat pad.

BACKGROUND: The infrapatellar fat pad (IFP) of the knee joint has received lots of attention recently due to its emerging role in the pathogenesis of osteoarthritis (OA), where it displays an inflammatory phenotype. The aim of the present study was to examine the infrapatellar fatty acid (FA) composition in a rabbit (Oryctolagus cuniculus) model of early OA created by anterior cruciate ligament transection (ACLT). METHODS: OA was induced randomly in the left or right knee joint of skeletally mature New Zealand White rabbits by ACLT, while the contralateral knee was left intact. A separate group of unoperated rabbits served as controls. The IFP of the ACLT, contralateral, and control knees were harvested following euthanasia 2 or 8 weeks post-ACLT and their FA composition was determined with gas chromatography-mass spectrometry. RESULTS: The n-3/n-6 polyunsaturated FA (PUFA) ratio shifted in a pro-inflammatory direction after ACLT, already observed 2 weeks after the operation (0.20 ± 0.008 vs. 0.18 ± 0.009). At 8 weeks, the FA profile of the ACLT group was characterized with increased percentages of 20:4n-6 (0.44 ± 0.064 vs. 0.98 ± 0.339 mol-%) and 22:6n-3 (0.03 ± 0.014 vs. 0.07 ± 0.015 mol-%) and with decreased monounsaturated FA (MUFA) sums (37.19 ± 1.586 vs. 33.20 ± 1.068 mol-%) and n-3/n-6 PUFA ratios (0.20 ± 0.008 vs. 0.17 ± 0.008). The FA signature of the contralateral knees resembled that of the unoperated controls in most aspects, but had increased proportions of total n-3 PUFA and reduced MUFA sums. CONCLUSIONS: These findings provide novel information on the effects of early OA on the infrapatellar FA profile in the rabbit ACLT model. The reduction in the n-3/n-6 PUFA ratio of the IFP is in concordance with the inflammation and cartilage degradation in early OA and could contribute to disease pathogenesis.

Quantitative susceptibility mapping of articular cartilage: Ex vivo findings at multiple orientations and following different degradation treatments.

PURPOSE: We investigated the feasibility of quantitative susceptibility mapping (QSM) for assessing degradation of articular cartilage by measuring ex vivo bovine cartilage samples subjected to different degradative treatments. Specimens were scanned at several orientations to study if degradation affects the susceptibility anisotropy. T2*-mapping, histological stainings, and polarized light microscopy were used as reference methods. Additionally, simulations of susceptibility in layered geometry were performed. METHODS: Samples (n = 9) were harvested from the patellae of skeletally mature bovines. Three specimens served as controls, and the rest were artificially degraded. MRI was performed at 9.4T using a 3D gradient echo sequence. QSM and T2* images and depth profiles through the centers of the samples were compared with each other and the histological findings. A planar isotropic model with depth-wise susceptibility variation was used in the simulations. RESULTS: A strong diamagnetic contrast was seen in the deep and calcified layers of cartilage, while T2* maps reflected the typical trilaminar structure of the collagen network. Anisotropy of susceptibility in cartilage was observed and was found to differ from the T2* anisotropy. Slight changes were observed in QSM and T2* following the degradative treatments. In simulations, anisotropy was observed. CONCLUSIONS: The results suggest that QSM is not sensitive to cartilage proteoglycan content, but shows sensitivity to the amount of calcification and to the integrity of the collagen network, providing potential for assessing osteoarthritis. The simulations suggested that the anisotropy of susceptibility might be partially explained by the layered geometry of susceptibility in cartilage.

Effects of tofacitinib in early arthritis-induced bone loss in an adjuvant-induced arthritis rat model.

Objectives: The main goal of this work was to analyse how treatment intervention with tofacitinib prevents the early disturbances of bone structure and mechanics in the rat model of adjuvant-induced arthritis. This is the first study to access the impact of tofacitinib on the skeletal bone effects of inflammation. Methods: Fifty Wistar rats with adjuvant-induced arthritis were randomly housed in experimental groups, as follows: non-arthritic healthy group (n = 20); arthritic non-treated group (n = 20); and 10 animals undergoing tofacitinib treatment. Rats were monitored during 22 days after disease induction for the inflammatory score, ankle perimeter and body weight. Healthy non-arthritic rats were used as controls for comparison. After 22 days of disease progression, rats were killed and bone samples collected for histology, micro-CT, three-point bending and nanoindentation analysis. Blood samples were also collected for quantification of bone turnover markers and systemic cytokines. Results: At the tissue level, measured by nanoindentation, tofacitinib increased bone cortical and trabecular hardness. However, micro-CT and three-point bending tests revealed that tofacitinib did not reverse the effects of arthritis on the cortical and trabecular bone structure and on mechanical properties. Conclusion: Possible reasons for these observations might be related to the mechanism of action of tofacitinib, which leads to direct interactions with bone metabolism, and/or to the kinetics of its bone effects, which might need longer exposure.

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.

BACKGROUND: Texture analysis methods based on gray level co-occurrence matrices (GLCM) can be optimized to probe the spatial correspondence information from knee MRI T2 maps and the changes caused by osteoarthritis, and thus possibly leading to a more sensitive characterization of osteoarthritic cartilage. Curvature of the cartilage surfaces combined with the low image resolution in relation to cartilage thickness set special requirements for an effective texture analysis tool. PURPOSE/HYPOTHESIS: To introduce a novel implementation of GLCM algorithm optimized for cartilage texture analysis; to evaluate the performance of the designed algorithm against mean T2 relaxation time analysis; and to identify the most suitable texture features for discerning osteoarthritic subjects and asymptomatic controls. STUDY TYPE: Case control. POPULATION/SUBJECTS/PHANTOM/SPECIMEN/ANIMAL MODEL: Eighty symptomatic osteoarthritis patients and 64 asymptomatic controls. FIELD STRENGTH/SEQUENCE: Multislice multiecho spin echo sequence on a 3T MRI system. ASSESSMENT: The T2 relaxation time maps were manually segmented by an operator trained for the task. Texture analysis was performed using an in-house algorithm developed in MATLAB. STATISTICAL TESTS: Symptomatic and asymptomatic subjects were compared using Mann-Whitney U-test. Repeatability of different features was addressed using the concordance correlation coefficient (CCC). Spearman's correlations between the features were determined. RESULTS: The algorithm displayed excellent performance in discerning symptomatic and asymptomatic subjects. Fifteen features provided a significant difference between the groups (P ≤ 0.05) and 12 of those had P values smaller than the mean T2 differences. Most of the studied texture features were highly repeatable with CCC over 90%. For the features with significant differences, correlation with mean T2 was low or moderate (|r| ≤ 0.5). DATA CONCLUSION: With careful parameter and feature selection and algorithm optimization, texture analysis provides a powerful tool for assessing knee osteoarthritis with more sensitive detection of cartilage degeneration compared to the mean value of the T2 relaxation times in an identical region of interest. LEVEL OF EVIDENCE: 2 Technical Efficacy Stage 2 J. Magn. Reson. Imaging 2018;47:1316-1327.

Correlations of low-field NMR and variable-field NMR parameters with osteoarthritis in human articular cartilage under load.

NMR experiments carried out at magnetic fields below 1 T provide new relaxation parameters unavailable with conventional clinical scanners. Contrast of T1 generally becomes larger towards low fields, as slow molecular reorientation processes dominate relaxation at the corresponding Larmor frequencies. This advantage has to be considered in the context of lower sensitivity and frequently reduced spatial resolution. The layered structure of cartilage is one example where a particularly strong variation of T1 across the tissue occurs, being affected by degenerative diseases such as osteoarthritis (OA). Furthermore, the presence of 1 H-14  N cross-relaxation, leading to so-called quadrupolar dips in the 1 H relaxation time dispersion, provide insight into the concentration and mobility of proteoglycans and collagen in cartilage, both being affected by OA. In this study, low-field imaging and variable-field NMR relaxometry were combined for the first time for tissue samples, employing unidirectional load to probe the mechanical properties. 20 human knee cartilage samples were placed in a compression cell, and studied by determining relaxation profiles without and with applied pressure (0.6 MPa) at 50 µm in-plane resolution, and comparing with volume-averaged T1 dispersion. Samples were subsequently stored in formalin, prepared for histology and graded according to the Mankin score system. Quadrupolar dips and thickness change under load showed the strongest correlation with Mankin grade. Average T1 and change of maximum T1 under load, as well as its position, correlate with thickness and thickness change. Furthermore, T1 (ω) above 25 mT was found to correlate with thickness change. While volume-averaged T1 is not a suitable indicator for OA, its change due to mechanical load and its extreme values are suggested as biomarkers available in low-field MRI systems. The shape of the dispersion T1 (ω) represents a promising access to understanding and quantifying molecular dynamics in tissue, pointing toward future in vivo tissue studies.

Elevated adiabatic T1ρ and T2ρ in articular cartilage are associated with cartilage and bone lesions in early osteoarthritis: A preliminary study.

PURPOSE: To evaluate adiabatic T1ρ and T2ρ of articular cartilage in symptomatic osteoarthritis (OA) patients and asymptomatic volunteers, and to determine their association with magnetic resonance imaging (MRI)-based structural abnormalities in cartilage and bone. MATERIALS AND METHODS: A total of 24 subjects (age range: 50-68 years; 12 female) were enrolled, including 12 early OA patients and 12 volunteers with normal joint function. Patients and volunteers underwent 3T MRI. T2 , adiabatic T1ρ , and T2ρ relaxation times of knee articular cartilage were measured. Proton density (PD)- and T1 -weighted MR image series were also obtained and separately evaluated for morphological changes using the MRI OA Knee Scoring (MOAKS) system. Comparisons using the Mann-Whitney nonparametric test were performed after dividing the study participants according to physical symptoms as determined by Western Ontario and McMaster Universities (WOMAC) score or presence of cartilage lesions, bone marrow lesions, or osteophytes. RESULTS: Elevated adiabatic T1ρ and T2ρ relaxation times of articular cartilage were associated with cartilage loss (P = 0.024-0.047), physical symptoms (0.0068-0.035), and osteophytes (0.0039-0.027). Elevated adiabatic T1ρ was also associated with bone marrow lesions (0.033). CONCLUSION: Preliminary data suggest that elevated adiabatic T1ρ and T2ρ of cartilage are associated with morphological abnormalities of cartilage and bone, and thus may be applicable for in vivo OA research and diagnostics. LEVEL OF EVIDENCE: 2 Technical Efficacy: Stage 2 J. MAGN. RESON. IMAGING 2017;46:678-689.