Getting Cartilage Thickness Measurements Right: A Systematic Inter-Method Comparison Using MRI Data from the Osteoarthritis Initiative.

OBJECTIVE: Magnetic resonance imaging is the standard imaging modality to assess articular cartilage. As the imaging surrogate of degenerative joint disease, cartilage thickness is commonly quantified after tissue segmentation. In lack of a standard method, this study systematically compared five methods for automatic cartilage thickness measurements across the knee joint and as a function of region and sub-region: 3D mesh normals (3D-MN), 3D nearest neighbors (3D-NN), 3D ray tracing (3D-RT), 2D centerline normals (2D-CN), and 2D surface normals (2D-SN). DESIGN: Based on the manually segmented femoral and tibial cartilage of 507 human knee joints, mean cartilage thickness was computed for the entire femorotibial joint, 4 joint regions, and 20 subregions using these methods. Inter-method comparisons of mean cartilage thickness and computation times were performed by one-way analysis of variance (ANOVA), Bland-Altman analyses and Lin's concordance correlation coefficient (CCC). RESULTS: Mean inter-method differences in cartilage thickness were significant in nearly all subregions (P < 0.001). By trend, mean differences were smallest between 3D-MN and 2D-SN in most (sub)regions, which is also reflected by highest quantitative inter-method agreement and CCCs. 3D-RT was prone to severe overestimation of up to 2.5 mm. 3D-MN, 3D-NN, and 2D-SN required mean processing times of ≤5.3 s per joint and were thus similarly efficient, whereas the time demand of 2D-CN and 3D-RT was much larger at 133 ± 29 and 351 ± 10 s per joint (P < 0.001). CONCLUSIONS: In automatic cartilage thickness determination, quantification accuracy and computational burden are largely affected by the underlying method. Mesh and surface normals or nearest neighbor searches should be used because they accurately capture variable geometries while being time-efficient.

Varus stress MRI in the refined assessment of the posterolateral corner of the knee joint.

Magnetic resonance imaging (MRI) is commonly used to assess traumatic and non-traumatic conditions of the knee. Due to its complex and variable anatomy, the posterolateral corner (PLC)-often referred to as the joint's dark side-remains diagnostically challenging. We aimed to render the diagnostic evaluation of the PLC more functional by combining MRI, varus loading, and image post-processing in a model of graded PLC injury that used sequential transections of the lateral collateral ligament, popliteus tendon, popliteofibular ligament, and anterior cruciate ligament. Ten human cadaveric knee joint specimens underwent imaging in each condition as above, and both unloaded and loaded using an MR-compatible device that standardized loading (of 147 N) and position (at 30° flexion). Following manual segmentation, 3D joint models were used to computationally measure lateral joint space opening for each specimen, configuration, and condition, while manual measurements provided the reference standard. With more extensive ligament deficiency and loading, lateral joint spaces increased significantly. In conclusion, varus stress MRI allows comprehensive PLC evaluation concerning structural integrity and associated functional capacity. Beyond providing normative values of lateral compartment opening, this study has potential implications for diagnostic and surgical decision-making and treatment monitoring in PLC injuries.

Fast, Accurate, and Robust T2 Mapping of Articular Cartilage by Neural Networks.

For T2 mapping, the underlying mono-exponential signal decay is traditionally quantified by non-linear Least-Squares Estimation (LSE) curve fitting, which is prone to outliers and computationally expensive. This study aimed to validate a fully connected neural network (NN) to estimate T2 relaxation times and to assess its performance versus LSE fitting methods. To this end, the NN was trained and tested in silico on a synthetic dataset of 75 million signal decays. Its quantification error was comparatively evaluated against three LSE methods, i.e., traditional methods without any modification, with an offset, and one with noise correction. Following in-situ acquisition of T2 maps in seven human cadaveric knee joint specimens at high and low signal-to-noise ratios, the NN and LSE methods were used to estimate the T2 relaxation times of the manually segmented patellofemoral cartilage. In-silico modeling at low signal-to-noise ratio indicated significantly lower quantification error for the NN (by medians of 6−33%) than for the LSE methods (p < 0.001). These results were confirmed by the in-situ measurements (medians of 10−35%). T2 quantification by the NN took only 4 s, which was faster than the LSE methods (28−43 s). In conclusion, NNs provide fast, accurate, and robust quantification of T2 relaxation times.

Reliability as a Precondition for Trust-Segmentation Reliability Analysis of Radiomic Features Improves Survival Prediction.

Machine learning results based on radiomic analysis are often not transferrable. A potential reason for this is the variability of radiomic features due to varying human made segmentations. Therefore, the aim of this study was to provide comprehensive inter-reader reliability analysis of radiomic features in five clinical image datasets and to assess the association of inter-reader reliability and survival prediction. In this study, we analyzed 4598 tumor segmentations in both computed tomography and magnetic resonance imaging data. We used a neural network to generate 100 additional segmentation outlines for each tumor and performed a reliability analysis of radiomic features. To prove clinical utility, we predicted patient survival based on all features and on the most reliable features. Survival prediction models for both computed tomography and magnetic resonance imaging datasets demonstrated less statistical spread and superior survival prediction when based on the most reliable features. Mean concordance indices were Cmean = 0.58 [most reliable] vs. Cmean = 0.56 [all] (p < 0.001, CT) and Cmean = 0.58 vs. Cmean = 0.57 (p = 0.23, MRI). Thus, preceding reliability analyses and selection of the most reliable radiomic features improves the underlying model's ability to predict patient survival across clinical imaging modalities and tumor entities.

Artificial intelligence-based automatic assessment of lower limb torsion on MRI.

Abnormal torsion of the lower limbs may adversely affect joint health. This study developed and validated a deep learning-based method for automatic measurement of femoral and tibial torsion on MRI. Axial T2-weighted sequences acquired of the hips, knees, and ankles of 93 patients (mean age, 13 ± 5 years; 52 males) were included and allocated to training (n = 60), validation (n = 9), and test sets (n = 24). A U-net convolutional neural network was trained to segment both femur and tibia, identify osseous anatomic landmarks, define pertinent reference lines, and quantify femoral and tibial torsion. Manual measurements by two radiologists provided the reference standard. Inter-reader comparisons were performed using repeated-measures ANOVA, Pearson's r, and the intraclass correlation coefficient (ICC). Mean Sørensen-Dice coefficients for segmentation accuracy ranged between 0.89 and 0.93 and erroneous segmentations were scarce. Ranges of torsion as measured by both readers and the algorithm on the same axial image were 15.8°-18.0° (femur) and 33.9°-35.2° (tibia). Correlation coefficients (ranges, .968 ≤ r ≤ .984 [femur]; .867 ≤ r ≤ .904 [tibia]) and ICCs (ranges, .963 ≤ ICC ≤ .974 [femur]; .867 ≤ ICC ≤ .894 [tibia]) indicated excellent inter-reader agreement. Algorithm-based analysis was faster than manual analysis (7 vs 207 vs 230 s, p < .001). In conclusion, fully automatic measurement of torsional alignment is accurate, reliable, and sufficiently fast for clinical workflows.

Micro- and Macroscale Assessment of Posterior Cruciate Ligament Functionality Based on Advanced MRI Techniques.

T2 mapping assesses tissue ultrastructure and composition, yet the association of imaging features and tissue functionality is oftentimes unclear. This study aimed to elucidate this association for the posterior cruciate ligament (PCL) across the micro- and macroscale and as a function of loading. Ten human cadaveric knee joints were imaged using a clinical 3.0T scanner and high-resolution morphologic and T2 mapping sequences. Emulating the posterior drawer test, the joints were imaged in the unloaded (δ0) and loaded (δ1) configurations. For the entire PCL, its subregions, and its osseous insertion sites, loading-induced changes were parameterized as summary statistics and texture variables, i.e., entropy, homogeneity, contrast, and variance. Histology confirmed structural integrity. Statistical analysis was based on parametric and non-parametric tests. Mean PCL length (37.8 ± 1.8 mm [δ0]; 44.0 ± 1.6 mm [δ1] [p < 0.01]), mean T2 (35.5 ± 2.0 ms [δ0]; 37.9 ± 1.3 ms [δ1] [p = 0.01]), and mean contrast values (4.0 ± 0.6 [δ0]; 4.9 ± 0.9 [δ1] [p = 0.01]) increased significantly under loading. Other texture features or ligamentous, osseous, and meniscal structures remained unaltered. Beyond providing normative T2 values across various scales and configurations, this study suggests that ligaments can be imaged morphologically and functionally based on joint loading and advanced MRI acquisition and post-processing techniques to assess ligament integrity and functionality in variable diagnostic contexts.

The MRI posterior drawer test to assess posterior cruciate ligament functionality and knee joint laxity.

Clinical Magnetic Resonance Imaging (MRI) of joints is limited to mere morphologic evaluation and fails to directly visualize joint or ligament function. In this controlled laboratory study, we show that knee joint functionality may be quantified in situ and as a function of graded posterior cruciate ligament (PCL)-deficiency by combining MRI and standardized loading. 11 human knee joints underwent MRI under standardized posterior loading in the unloaded and loaded (147 N) configurations and in the intact, partially, and completely PCL-injured conditions. For each specimen, configuration, and condition, 3D joint models were implemented to analyse joint kinematics based on 3D Euclidean vectors and their projections on the Cartesian planes. Manual 2D measurements served as reference. With increasing PCL deficiency, vector projections increased significantly in the anteroposterior dimension under loading and manual measurements demonstrated similar patterns of change. Consequently, if combined with advanced image post-processing, stress MRI is a powerful diagnostic adjunct to evaluate ligament functionality and joint laxity in multiple dimensions and may have a role in differentiating PCL injury patterns, therapeutic decision-making, and treatment monitoring.

Comprehensive Assessment of Medial Knee Joint Instability by Valgus Stress MRI.

Standard clinical MRI techniques provide morphologic insights into knee joint pathologies, yet do not allow evaluation of ligament functionality or joint instability. We aimed to study valgus stress MRI, combined with sophisticated image post-processing, in a graded model of medial knee joint injury. To this end, eleven human cadaveric knee joint specimens were subjected to sequential injuries to the superficial medial collateral ligament (sMCL) and the anterior cruciate ligament (ACL). Specimens were imaged in 30° of flexion in the unloaded and loaded configurations (15 kp) and in the intact, partially sMCL-deficient, completely sMCL-deficient, and sMCL- and ACL-deficient conditions using morphologic sequences and a dedicated pressure-controlled loading device. Based on manual segmentations, sophisticated 3D joint models were generated to compute subchondral cortical distances for each condition and configuration. Statistical analysis included appropriate parametric tests. The medial compartment opened gradually as a function of loading and injury, especially anteriorly. Corresponding manual reference measurements by two readers confirmed these findings. Once validated in clinical trials, valgus stress MRI may comprehensively quantify medial compartment opening as a functional imaging surrogate of medial knee joint instability and qualify as an adjunct diagnostic tool in the differential diagnosis, therapeutic decision-making, and monitoring of treatment outcomes.

In-Situ Cartilage Functionality Assessment Based on Advanced MRI Techniques and Precise Compartmental Knee Joint Loading through Varus and Valgus Stress.

Stress MRI brings together mechanical loading and MRI in the functional assessment of cartilage and meniscus, yet lacks basic scientific validation. This study assessed the response-to-loading patterns of cartilage and meniscus incurred by standardized compartmental varus and valgus loading of the human knee joint. Eight human cadaveric knee joints underwent imaging by morphologic (i.e., proton density-weighted fat-saturated and 3D water-selective) and quantitative (i.e., T1ρ and T2 mapping) sequences, both unloaded and loaded to 73.5 N, 147.1 N, and 220.6 N of compartmental pressurization. After manual segmentation of cartilage and meniscus, morphometric measures and T2 and T1ρ relaxation times were quantified. CT-based analysis of joint alignment and histologic and biomechanical tissue measures served as references. Under loading, we observed significant decreases in cartilage thickness (p < 0.001 (repeated measures ANOVA)) and T1ρ relaxation times (p = 0.001; medial meniscus, lateral tibia; (Friedman test)), significant increases in T2 relaxation times (p ≤ 0.004; medial femur, lateral tibia; (Friedman test)), and adaptive joint motion. In conclusion, varus and valgus stress MRI induces meaningful changes in cartilage and meniscus secondary to compartmental loading that may be assessed by cartilage morphometric measures as well as T2 and T1ρ mapping as imaging surrogates of tissue functionality.

A Novel Combined Level Set Model for Carpus Segmentation from Magnetic Resonance Images with Prior Knowledge aligned in Polar Coordinate System.

BACKGROUND AND OBJECTIVE: Segmentation on carpus provides essential information for clinical applications including pathological evaluations, therapy planning, wrist biomechanical analysis, etc. Along with the acquisition procedure of magnetic resonance (MR) technique, poor quality of wrist images (e.g., occlusion, low signal-to-noise ratio, and contrast) often causes segmentation failure. METHODS: In this work, to address such problems, a shape prior enhanced level set model was proposed. By transferring a shape contour in Cartesian Coordinate System (COS) into a curve in Polar Coordinate System (POS), parameters describing conventional shape invariance, i.e., translations, rotation, and scale were simplified into a single parameter for phase shift, which strongly improved algorithm efficiency. Given a training set in COS, a confidence interval representing the corresponding curves in POS was utilized as the shape prior set term in the model. Integrated with an edge detector, a local intensity descriptor, and a regularization term, the proposed method further possessed abilities against noise, intensity inhomogeneity as well as re-initialization problem. Images from 15 in-vivo acquired MR-datasets of the human wrist were used for validation. The performance of the proposed method has been compared with three state-of-the-art methods. RESULTS: We reported a Dice Similarity Coefficient of 96.88±1.20%, a Relative Volume Difference of -1.53±3.01%, a Volume Overlap Error of 6.03±2.23%, a 95% Hausdorff Distance of 1.43±0.66 mm, an Average Symmetric Surface Distance of 0.50±0.17 mm, and a Root Mean Square Distance of 0.71±0.25 mm for the proposed method. The time consumption was 36.03±19.98 s. CONCLUSIONS: Experimental results indicated that, compared with three other methods, the proposed method achieved significant improvement in terms of accuracy and efficiency.

Deep Learning-Based Post-Processing of Real-Time MRI to Assess and Quantify Dynamic Wrist Movement in Health and Disease.

While morphologic magnetic resonance imaging (MRI) is the imaging modality of choice for the evaluation of ligamentous wrist injuries, it is merely static and incapable of diagnosing dynamic wrist instability. Based on real-time MRI and algorithm-based image post-processing in terms of convolutional neural networks (CNNs), this study aims to develop and validate an automatic technique to quantify wrist movement. A total of 56 bilateral wrists (28 healthy volunteers) were imaged during continuous and alternating maximum ulnar and radial abduction. Following CNN-based automatic segmentations of carpal bone contours, scapholunate and lunotriquetral gap widths were quantified based on dedicated algorithms and as a function of wrist position. Automatic segmentations were in excellent agreement with manual reference segmentations performed by two radiologists as indicated by Dice similarity coefficients of 0.96 ± 0.02 and consistent and unskewed Bland-Altman plots. Clinical applicability of the framework was assessed in a patient with diagnosed scapholunate ligament injury. Considerable increases in scapholunate gap widths across the range-of-motion were found. In conclusion, the combination of real-time wrist MRI and the present framework provides a powerful diagnostic tool for dynamic assessment of wrist function and, if confirmed in clinical trials, dynamic carpal instability that may elude static assessment using clinical-standard imaging modalities.

Seeing Beyond Morphology-Standardized Stress MRI to Assess Human Knee Joint Instability.

While providing the reference imaging modality for joint pathologies, MRI is focused on morphology and static configurations, thereby not fully exploiting the modality's diagnostic capabilities. This study aimed to assess the diagnostic value of stress MRI combining imaging and loading in differentiating partial versus complete anterior cruciate ligament (ACL)-injury. Ten human cadaveric knee joint specimens were subjected to serial imaging using a 3.0T MRI scanner and a custom-made pressure-controlled loading device. Emulating the anterior-drawer test, joints were imaged before and after arthroscopic partial and complete ACL transection in the unloaded and loaded configurations using morphologic sequences. Following manual segmentations and registration of anatomic landmarks, two 3D vectors were computed between anatomic landmarks and registered coordinates. Loading-induced changes were quantified as vector lengths, angles, and projections on the x-, y-, and z-axis, related to the intact unloaded configuration, and referenced to manual measurements. Vector lengths and projections significantly increased with loading and increasing ACL injury and indicated multidimensional changes. Manual measurements confirmed gradually increasing anterior tibial translation. Beyond imaging of ligament structure and functionality, stress MRI techniques can quantify joint stability to differentiate partial and complete ACL injury and, possibly, compare surgical procedures and monitor treatment outcomes.

Automated Analysis of Alignment in Long-Leg Radiographs by Using a Fully Automated Support System Based on Artificial Intelligence.

PURPOSE: To develop and validate a deep learning-based method for automatic quantitative analysis of lower-extremity alignment. MATERIALS AND METHODS: In this retrospective study, bilateral long-leg radiographs (LLRs) from 255 patients that were obtained between January and September of 2018 were included. For training data (n = 109), a U-Net convolutional neural network was trained to segment the femur and tibia versus manual segmentation. For validation data (n = 40), model parameters were optimized. Following identification of anatomic landmarks, anatomic and mechanical axes were identified and used to quantify alignment through the hip-knee-ankle angle (HKAA) and femoral anatomic-mechanical angle (AMA). For testing data (n = 106), algorithm-based angle measurements were compared with reference measurements by two radiologists. Angles and time for 30 random radiographs were compared by using repeated-measures analysis of variance and one-way analysis of variance, whereas correlations were quantified by using Pearson r and intraclass correlation coefficients. RESULTS: Bilateral LLRs of 255 patients (mean age, 26 years ± 23 [standard deviation]; range, 0-88 years; 157 male patients) were included. Mean Sørensen-Dice coefficients for segmentation were 0.97 ± 0.09 for the femur and 0.96 ± 0.11 for the tibia. Mean HKAAs and AMAs as measured by the readers and the algorithm ranged from 0.05° to 0.11° (P = .5) and from 4.82° to 5.43° (P < .001). Interreader correlation coefficients ranged from 0.918 to 0.995 (r range, P < .001), and agreement was almost perfect (intraclass correlation coefficient range, 0.87-0.99). Automatic analysis was faster than the two radiologists' manual measurements (3 vs 36 vs 35 seconds, P < .001). CONCLUSION: Fully automated analysis of LLRs yielded accurate results across a wide range of clinical and pathologic indications and is fast enough to enhance and accelerate clinical workflows.Supplemental material is available for this article.© RSNA, 2020See also commentary by Andreisek in this issue.

No pressure, no diamonds? - Static vs. dynamic compressive in-situ loading to evaluate human articular cartilage functionality by functional MRI.

Biomechanical Magnetic Resonance Imaging (MRI) of articular cartilage, i.e. its imaging under loading, is a promising diagnostic tool to assess the tissue's functionality in health and disease. This study aimed to assess the response to static and dynamic loading of histologically intact cartilage samples by functional MRI and pressure-controlled in-situ loading. To this end, 47 cartilage samples were obtained from the medial femoral condyles of total knee arthroplasties (from 24 patients), prepared to standard thickness, and placed in a standard knee joint in a pressure-controlled whole knee-joint compressive loading device. Cartilage samples' responses to static (i.e. constant), dynamic (i.e. alternating), and no loading, i.e. free-swelling conditions, were assessed before (δ0), and after 30 min (δ1) and 60 min (δ2) of loading using serial T1ρ maps acquired on a 3.0T clinical MRI scanner (Achieva, Philips). Alongside texture features, relative changes in T1ρ (Δ1, Δ2) were determined for the upper and lower sample halves and the entire sample, analyzed using appropriate statistical tests, and referenced to histological (Mankin scoring) and biomechanical reference measures (tangent stiffness). Histological, biomechanical, and T1ρ sample characteristics at δ0 were relatively homogenous in all samples. In response to loading, relative increases in T1ρ were strong and significant after dynamic loading (Δ1 = 10.3 ± 17.0%, Δ2 = 21.6 ± 21.8%, p = 0.002), while relative increases in T1ρ after static loading and in controls were moderate and not significant. Generally, texture features did not demonstrate clear loading-related associations underlying the spatial relationships of T1ρ. When realizing the clinical translation, this in-situ study suggests that serial T1ρ mapping is best combined with dynamic loading to assess cartilage functionality in humans based on advanced MRI techniques.

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".

A serial multiparametric quantitative magnetic resonance imaging study to assess proteoglycan depletion of human articular cartilage and its effects on functionality.

Water, collagen, and proteoglycans determine articular cartilage functionality. If altered, susceptibility to premature degeneration is increased. This study investigated the effects of enzymatic proteoglycan depletion on cartilage functionality as assessed by advanced Magnetic Resonance Imaging (MRI) techniques under standardized loading. Lateral femoral condylar cartilage-bone samples from patients undergoing knee replacement (n = 29) were serially imaged by Proton Density-weighted and T1, T1ρ, T2, and T2* mapping sequences on a clinical 3.0 T MRI scanner (Achieva, Philips). Using pressure-controlled indentation loading, samples were imaged unloaded and quasi-statically loaded to 15.1 N and 28.6 N, and both before and after exposure to low-concentrated (LT, 0.1 mg/mL, n = 10) or high-concentrated trypsin (HT, 1.0 mg/mL, n = 10). Controls were not treated (n = 9). Responses to loading were assessed for the entire sample and regionally, i.e. sub- and peri-pistonally, and zonally, i.e. upper and lower sample halves. Trypsin effects were quantified as relative changes (Δ), analysed using appropriate statistical tests, and referenced histologically. Histological proteoglycan depletion was reflected by significant sub-pistonal decreases in T1 (p = 0.003) and T2 (p = 0.008) after HT exposure. Loading-induced changes in T1ρ and T2* were not related. In conclusion, proteoglycan depletion alters cartilage functionality and may be assessed using serial T1 and T2 mapping under loading.

Identifying the imaging correlates of cartilage functionality based on quantitative MRI mapping - The collagenase exposure model.

Cartilage functionality is determined by tissue structure and composition. If altered, cartilage is predisposed to premature degeneration. This pathomimetical study of early osteoarthritis evaluated the dose-dependant effects of collagenase-induced collagen disintegration and proteoglycan depletion on cartilage functionality as assessed by serial T1, T1ρ, T2, and T2* mapping under loading. 30 human femoral osteochondral samples underwent imaging on a clinical 3.0 T MRI scanner (Achieva, Philips) in the unloaded reference configuration (δ0) and under pressure-controlled quasi-static indentation loading to 15.1 N (δ1) and to 28.6 N (δ2). Imaging was performed before and after exposure to low (LC, 0.5 mg/mL; n = 10) or high concentration (HC, 1.5 mg/mL; n = 10) of collagenase. Untreated samples served as controls (n = 10). Loading responses were determined for the entire sample and the directly loaded (i.e. sub-pistonal) and bilaterally adjacent (i.e. peri­pistonal) regions, referenced histologically, quantified as relative changes, and analysed using adequate parametric and non-parametric statistical tests. Dose-dependant surface disintegration and tissue loss were reflected by distinctly different pre- and post-exposure response-to-loading patterns. While T1 generally decreased with loading, regardless of collagenase exposure, T1ρ increased significantly after HC exposure (p = 0.008). Loading-induced decreases in T2 were significant after LC exposure (p = 0.006), while changes in T2* were ambiguous. In conclusion, aberrant loading-induced changes in T2 and T1ρ reflect moderate and severe matrix changes, respectively, and indicate the close interrelatedness of matrix changes and functionality in cartilage.

Magnetic resonance imaging of human knee joint functionality under variable compressive in-situ loading and axis alignment.

Magnetic resonance imaging (MRI) under mechanical loading, commonly referred to as stress MRI, allows the evaluation of functional properties of intra- and periarticular tissues non-invasively beyond static assessment. Quantitative MRI can identify physiological and pathological responses to loading as indication of, potentially treatable, early degeneration and load transmission failure. Therefore, we have developed and validated an MRI-compatible pressure-controlled axial loading device to compress human knee specimens under variable loading intensity and axis deviation. Ten structurally intact human knee specimens (mean age 83.2 years) were studied on a 3.0T scanner (Achieva, Philips). Proton density-weighted fat-saturated turbo spin-echo and high-resolution 3D water selective 3D gradient-echo MRI scans were acquired sequentially at 10° joint flexion in seven configurations: unloaded and then at approximately half and full body weight loading in neutral, 10° varus and 10° valgus alignment, respectively. Following manual segmentation in both femorotibial compartments, cartilage thickness (ThC) was determined as well as meniscus extrusion (ExM). These measures were compared to computed tomography scans, histological grading (Mankin and Pauli scores), and biomechanical properties (Instantaneous Young's Modulus). Compartmental, regional and subregional changes in ThC and ExM were reflective of loading intensity and joint alignment, with the greatest changes observed in the medial compartment during varus and in the lateral compartment during valgus loading. These were not significantly associated with the histological tissue status or biomechanical properties. In conclusion, this study explores the physiological in-situ response of knee cartilage and meniscus, based on stress MRI, and as a function of loading intensity, joint alignment, histological tissue status, and biomechanical properties, as another step towards clinical implementation.

An MRI-compatible varus-valgus loading device for whole-knee joint functionality assessment based on compartmental compression: a proof-of-concept study.

OBJECTIVE: Beyond static assessment, functional techniques are increasingly applied in magnetic resonance imaging (MRI) studies. Stress MRI techniques bring together MRI and mechanical loading to study knee joint and tissue functionality, yet prototypical axial compressive loading devices are bulky and complex to operate. This study aimed to design and validate an MRI-compatible pressure-controlled varus-valgus loading device that applies loading along the joint line. METHODS: Following the device's thorough validation, we demonstrated proof of concept by subjecting a structurally intact human cadaveric knee joint to serial imaging in unloaded and loaded configurations, i.e. to varus and valgus loading at 7.5 kPa (= 73.5 N), 15 kPa (= 147.1 N), and 22.5 kPa (= 220.6 N). Following clinical standard (PDw fs) and high-resolution 3D water-selective cartilage (WATSc) sequences, we performed manual segmentations and computations of morphometric cartilage measures. We used CT and radiography (to quantify joint space widths) and histology and biomechanics (to assess tissue quality) as references. RESULTS: We found (sub)regional decreases in cartilage volume, thickness, and mean joint space widths reflective of areal pressurization of the medial and lateral femorotibial compartments. DISCUSSION: Once substantiated by larger sample sizes, varus-valgus loading may provide a powerful alternative stress MRI technique.