High frame rate deformation analysis of knee cartilage by spiral dualMRI and relaxation mapping

Deep learning enables accurate soft tissue tendon deformation estimation in vivo via ultrasound imaging

Image-based deformation estimation is an important tool used in a variety of engineering problems, including crack propagation, fracture, and fatigue failure. These tools have been important in biomechanics research where measuring in vitro and in vivo tissue deformations are important for evaluating tissue health and disease progression. However, accurately measuring tissue deformation in vivo is particularly challenging due to limited image signal-to-noise ratio. Therefore, we created a novel deep-learning approach for measuring deformation from a sequence of images collected in vivo called StrainNet. Utilizing a training dataset that incorporates image artifacts, StrainNet was designed to maximize performance in challenging, in vivo settings. Artificially generated image sequences of human flexor tendons undergoing known deformations were used to compare benchmark StrainNet against two conventional image-based strain measurement techniques. StrainNet outperformed the traditional techniques by nearly 90%. High-frequency ultrasound imaging was then used to acquire images of the flexor tendons engaged during contraction. Only StrainNet was able to track tissue deformations under the in vivo test conditions. Findings revealed strong correlations between tendon deformation and applied forces, highlighting the potential for StrainNet to be a valuable tool for assessing rehabilitation strategies or disease progression. Additionally, by using real-world data to train our model, StrainNet was able to generalize and reveal important relationships between the effort exerted by the participant and tendon mechanics. Overall, StrainNet demonstrated the effectiveness of using deep learning for image-based strain analysis in vivo.

Time-Resolved Quantification of Patellofemoral Cartilage Deformation in Response to Loading and Unloading via Dynamic MRI With Prospective Motion Correction

BACKGROUND

In vivo cartilage deformation has been studied by static magnetic resonance imaging (MRI) with in situ loading, but knowledge about strain dynamics after load onset and release is scarce.

PURPOSE

To measure the dynamics of patellofemoral cartilage deformation and recovery in response to in situ loading and unloading by using MRI with prospective motion correction.

STUDY TYPE

Prospective.

SUBJECTS

Ten healthy male volunteers (age: [31.4 ± 3.2] years).

FIELD STRENGTH/SEQUENCE

T1-weighted RF-spoiled 2D gradient-echo sequence with a golden angle radial acquisition scheme, augmented with prospective motion correction, at 3 T.

ASSESSMENT

In situ knee loading was realized with a flexion angle of approximately 40° using an MR-compatible pneumatic loading device. The loading paradigm consisted of 2 minutes of unloaded baseline followed by a 5-minute loading bout with 50% body weight and an unloading period of 38 minutes. The cartilage strain was assessed as the mean distance between patellar and femoral bone-cartilage interfaces as a percentage of the initial (pre-load) distance.

STATISTICAL TESTS

Wilcoxon signed-rank tests (significance level: P < 0.05), Pearson correlation coefficient (r).

RESULTS

The cartilage compression and recovery behavior was characterized by a viscoelastic response. The elastic compression ([-12.5 ± 3.1]%) was significantly larger than the viscous compression ([-7.6 ± 1.5]%) and the elastic recovery ([10.5 ± 2.1]%) was significantly larger than the viscous recovery ([6.1 ± 1.8]%). There was a significant residual offset strain ([-3.6 ± 2.3]%) across the cohort. A significant negative correlation between elastic compression and elastic recovery was observed (r = -0.75).

DATA CONCLUSION

The in vivo cartilage compression and recovery time course in response to loading was successfully measured via dynamic MRI with prospective motion correction. The clinical relevance of the strain characteristics needs to be assessed in larger subject and patient cohorts.

LEVEL OF EVIDENCE

2 TECHNICAL EFFICACY: Stage 1.

In vivo human knee varus-valgus loading apparatus for analysis of MRI-based intratissue strain and relaxometry

The diagnosis of early-stage osteoarthritis remains as an unmet challenge in medicine and a roadblock to evaluating the efficacy of disease-modifying treatments. Recent studies demonstrate that unique patterns of intratissue cartilage deformation under cyclic loading can serve as potential biomarkers to detect early disease pathogenesis. However, a workflow to obtain deformation, strain maps, and quantitative MRI metrics due to the loading of articular cartilage in vivo has not been fully developed. In this study, we characterize and demonstrate an apparatus that is capable of applying a varus-valgus load to the human knee in vivo within an MRI environment to enable the measurement of cartilage structure and mechanical function. The apparatus was first tested in a lab environment, then the functionality and utility of the apparatus were examined during varus loading in a clinical 3T MRI system for human imaging. We found that the device enables quantitative MRI metrics for biomechanics and relaxometry data acquisition during joint loading leading to compression of the medial knee compartment. Integration with spiral DENSE MRI during cyclic loading provided time-dependent displacement and strain maps within the tibiofemoral cartilage. The results from these procedures demonstrate that the performance of this loading apparatus meets the design criteria and enables a simple and practical workflow for future studies of clinical cohorts, and the identification and validation of imaging-based biomechanical biomarkers.

MRI-derived Articular Cartilage Strains Predict Patient-Reported Outcomes Six Months Post Anterior Cruciate Ligament Reconstruction

Key terms

Multicontrast and Multiparametric, Magnetic Resonance Imaging, Osteoarthritis, Functional Biomechanical Imaging, Knee Joint Degeneration What is known about the subject: dualMRI has been used to quantify strains in a healthy human population in vivo and in cartilage explant models. Previously, OA severity, as determined by histology, has been positively correlated to increased shear and transverse strains in cartilage explants. What this study adds to existing knowledge: This is the first in vivo use of dualMRI in a participant demographic post-ACL reconstruction and at risk for developing osteoarthritis. This study shows that dualMRI-derived strains are more significantly correlated with patient-reported outcomes than any MRI relaxometry metric.

Background

Anterior cruciate ligament (ACL) injuries lead to an increased risk of osteoarthritis, characterized by altered cartilage tissue structure and function. Displacements under applied loading by magnetic resonance imaging (dualMRI) is a novel MRI technique that can be used to quantify mechanical strain in cartilage while undergoing a physiological load.

Purpose

To determine if strains derived by dualMRI and relaxometry measures correlate with patient-reported outcomes at six months post unilateral ACL reconstruction.

Study Design

Cohort study.

Methods

Quantitative MRI (T2, T2*, T1ρ) measurements and transverse, axial, and shear strains were quantified in the medial articular tibiofemoral cartilage of 35 participants at six-months post unilateral ACL reconstruction. The relationships between patient-reported outcomes (WOMAC, KOOS, MARS) and all qMRI relaxation times were quantified using general linear mixed-effects models. A combined best-fit multicontrast MRI model was then developed using backwards regression to determine the patient features and MRI metrics that are most predictive of patient-reported outcome scores.

Results

Higher femoral strains were significantly correlated with worse patient-reported functional outcomes. Femoral shear and transverse strains were positively correlated with six-month KOOS and WOMAC scores, after controlling for covariates. No relaxometry measures were correlated with patient-reported outcome scores. We identified the best-fit model for predicting WOMAC score using multiple MRI measures and patient-specific information, including sex, age, graft type, femoral transverse strain, femoral axial strain, and femoral shear strain. The best-fit model significantly predicted WOMAC score (p<0.001) better than any one individual MRI metric alone. When we regressed the model-predicted WOMAC scores against the patient-reported WOMAC scores, we found that our model achieved a goodness of fit exceeding 0.52.

Conclusions

This work presents the first use of dualMRI in vivo in a cohort of participants at risk for developing osteoarthritis. Our results indicate that both shear and transverse strains are highly correlated with patient-reported outcome severity could serve as novel imaging biomarkers to predict the development of osteoarthritis.

Use of a novel magnetically actuated compression system to study the temporal dynamics of axial and lateral strain in human osteochondral plugs

The high water content of articular cartilage allows this biphasic tissue to withstand large compressive loads through fluid pressurization. The system presented here, termed the "MagnaSquish", provides new capabilities for quantifying the effect of rehydration on cartilage behavior during cyclic loading. An imbalanced rate of fluid exudation during load and fluid re-entry during recovery can lead to the accumulation of strain during successive loading cycles - a phenomenon known as ratcheting. Typical experimental systems for cartilage biomechanics use continuous contact between the platen and sample, which may affect tissue rehydration by compressing the top layer of cartilage and slowing fluid re-entry. To address this limitation, we developed a magnetically actuated device that provides full lift-off of the platen in between loading cycles. We investigated strain accumulation in cadaveric human osteochondral plugs during 750 loading cycles, with two dimensional profiles of the cartilage captured at 30 frames per second throughout loading and 10 min of additional free swelling recovery. Axial and lateral strain measurements were extracted from the tissue profiles using a UNet-based deep learning algorithm to circumvent manual tracing. We observed increased axial strain accumulation with shorter inter-cycle recovery, with static loading serving as the extreme case of zero recovery. The loading waveform during the 750 cycles dictated the pace of the recovery during the extended free swelling period, as shorter inter-cycle recovery led to more persistent axial strain accumulation for up to five minutes. This work showcases the importance of fluid re-entry in resisting strain accumulation during cyclical compression.

A method for defining tissue injury criteria reveals that ligament deformation thresholds are multimodal

Soft tissue injuries (such as ligament, tendon, and meniscus tears) are the result of extracellular matrix damage from excessive tissue stretching. Deformation thresholds for soft tissues, however, remain largely unknown due to a lack of methods that can measure and compare the spatially heterogeneous damage and deformation that occurs in these materials. Here, we propose a full-field method for defining tissue injury criteria: multimodal strain limits for biological tissues analogous to yield criteria that exist for crystalline materials. Specifically, we developed a method for defining strain thresholds for mechanically-driven fibrillar collagen denaturation in soft tissues, using regional multimodal deformation and damage data. We established this new method using the murine medial collateral ligament (MCL) as our model tissue. Our findings revealed that multiple modes of deformation contribute to collagen denaturation in the murine MCL, contrary to the common assumption that collagen damage is driven only by strain in the direction of fibers. Remarkably, hydrostatic strain (computed here with an assumption of plane strain) was the best predictor of mechanically-driven collagen denaturation in ligament tissue, suggesting crosslink-mediated stress transfer plays a role in molecular damage accumulation. This work demonstrates that collagen denaturation can be driven by multiple modes of deformation and provides a method for defining deformation thresholds, or injury criteria, from spatially heterogeneous data. STATEMENT OF SIGNIFICANCE: Understanding the mechanics of soft tissue injuries is crucial for the development of new technology for injury detection, prevention, and treatment.  Yet, tissue-level deformation thresholds for injury are unknown, due to a lack of methods that combine full-field measurements of multimodal deformation and damage in mechanically loaded soft tissues. Here, we propose a method for defining tissue injury criteria: multimodal strain thresholds for biological tissues. Our findings reveal that multiple modes of deformation contribute to collagen denaturation, contrary to the common assumption that collagen damage is driven by strain in the fiber direction alone. The method will inform the development of new mechanics-based diagnostic imaging, improve computational modeling of injury, and be employed to study the role of tissue composition in injury susceptibility.

Multi-frame biomechanical and relaxometry analysis during in vivo loading of the human knee by spiral dualMRI and compressed sensing

PURPOSE

Knee cartilage experiences repetitive loading during physical activities, which is altered during the pathogenesis of diseases like osteoarthritis. Analyzing the biomechanics during motion provides a clear understanding of the dynamics of cartilage deformation and may establish essential imaging biomarkers of early-stage disease. However, in vivo biomechanical analysis of cartilage during rapid motion is not well established.

METHODS

We used spiral displacement encoding with stimulated echoes (DENSE) MRI on in vivo human tibiofemoral cartilage during cyclic varus loading (0.5 Hz) and used compressed sensing on the k-space data. The applied compressive load was set for each participant at 0.5 times body weight on the medial condyle. Relaxometry methods were measured on the cartilage before (T1ρ , T2 ) and after (T1ρ ) varus load.

RESULTS

Displacement and strain maps showed a gradual shift of displacement and strain in time. Compressive strain was observed in the medial condyle cartilage and shear strain was roughly half of the compressive strain. Male participants had more displacement in the loading direction compared to females, and T1ρ values did not change after cyclic varus load. Compressed sensing reduced the scanning time up to 25% to 40% when comparing the displacement maps and substantially lowered the noise levels.

CONCLUSION

These results demonstrated the ease of which spiral DENSE MRI could be applied to clinical studies because of the shortened imaging time, while quantifying realistic cartilage deformations that occur through daily activities and that could serve as biomarkers of early osteoarthritis.

A method for defining tissue injury criteria reveals ligament deformation thresholds are multimodal

Soft tissue injuries (such as ligament, tendon, and meniscus tears) are the result of extracellular matrix damage from excessive tissue stretching. Deformation thresholds for soft tissues, however, remain largely unknown due to a lack of methods that can measure and compare the spatially heterogeneous damage and deformation that occurs in these materials. Here, we propose a method for defining tissue injury criteria : multimodal strain limits for biological tissues analogous to yield criteria that exist for crystalline materials. Specifically, we developed a method for defining injury criteria for mechanically-driven fibrillar collagen denaturation in soft tissues, using regional multimodal deformation and damage data. We established this new method using the murine medial collateral ligament (MCL) as our model tissue. Our findings revealed that multiple modes of deformation contribute to collagen denaturation in the murine MCL, contrary to the common assumption that collagen damage is driven by strain in the fiber direction alone. Remarkably, our results indicated that hydrostatic strain, or volumetric expansion, may be the best predictor of mechanically-driven collagen denaturation in ligament tissue, suggesting crosslink-mediated stress transfer plays a role in molecular damage accumulation. This work demonstrates that collagen denaturation can be driven by multiple modes of deformation and provides a method for defining deformation thresholds, or injury criteria, from spatially heterogeneous data.