Multi-vendor multi-site quantitative MRI analysis of cartilage degeneration 10 Years after anterior cruciate ligament reconstruction: MOON-MRI protocol and preliminary results

Bilateral waveform analysis of gait biomechanics presurgery to 12 months following ACL reconstruction compared to controls

The purpose of this study was to compare gait biomechanics between limbs and to matched uninjured controls (i.e., sex, age, and body mass index) preoperatively and at 2, 4, 6, and 12 months following primary unilateral anterior cruciate ligament reconstruction (ACLR). Functional mixed effects models were used to identify differences in gait biomechanics throughout the stance phase between the a) ACLR limb and uninvolved limb, b) ACLR limb and controls, and c) uninvolved limb and controls. Compared with the uninvolved limb, the ACLR limb demonstrated lesser knee extension moment (KEM; within 8-37% range of stance) during early stance as well as lesser knee flexion moment (KFM; 45-84%) and greater knee flexion angle (KFA; 43-90%) during mid- to late stance at all timepoints. Compared with controls, the ACLR limb demonstrated lesser vertical ground reaction force (vGRF; 5-26%), lesser KEM (7-47%), and lesser knee adduction moment (KAM; 12-35%) during early stance as well as greater vGRF (39-63%) and greater KFA (34-95%) during mid- to late stance at all timepoints. Compared with controls, the uninvolved limb demonstrated lesser KFA (1-56%) and lesser KEM (12-54%) during early to mid-stance at all timepoints. While gait becomes more symmetrical over the first 12 months post-ACLR, the ACLR and uninvolved limbs both demonstrate persistent aberrant gait biomechanics compared to controls. Biomechanical waveforms throughout stance can be generally described as less dynamic following ACL injury and ACLR compared with uninjured controls.

Cartilage T2 mapping-based radiomics in knee osteoarthritis research: Status, progress and future outlook

Osteoarthritis (OA) affects more than 500 millions people worldwide and places an enormous economic and medical burden on patients and healthcare systems. The knee is the most commonly affected joint. However, there is no effective early diagnostic method for OA. The main pathological feature of OA is cartilage degeneration. Owing to the poor regenerative ability of chondrocytes, early detection of OA and prompt intervention are extremely important. The T2 relaxation time indicates changes in cartilage composition and responds to alterations in the early cartilage matrix. T2 mapping does not require contrast agents or special equipment, so it is widely used. Radiomics analysis methods are used to construct diagnostic or predictive models based on information extracted from clinical images. Owing to the development of artificial intelligence methods, radiomics has made excellent progress in segmentation and model construction. In this review, we summarize the progress of T2 mapping radiomics research methods in terms of T2 map acquisition, image postprocessing, and OA diagnosis or predictive model construction.

Chondral Injury Associated With ACL Injury: Assessing Progressive Chondral Degeneration With Morphologic and Quantitative MRI Techniques

BACKGROUND

Anterior cruciate ligament (ACL) injuries are associated with a risk of post-traumatic osteoarthritis due to chondral damage. Magnetic resonance imaging (MRI) techniques provide excellent visualization and assessment of cartilage and can detect subtle and early chondral damage. This is often preceding clinical and radiographic post-traumatic osteoarthritis.

HYPOTHESIS

Morphologic and quantitative MRI techniques can assess early and progressive degenerative chondral changes after acute ACL injury.

STUDY DESIGN

Prospective longitudinal cohort.

LEVEL OF EVIDENCE

Level 3.

METHODS

Sixty-five participants with acute unilateral ACL injuries underwent bilateral knee MRI scans within 1 month of injury. Fifty-seven participants presented at 6 months, while 54 were evaluated at 12 months. MRI morphologic evaluation using a modified Noyes score assessed cartilage signal alteration, chondral damage, and subchondral bone status. Quantitative T1ρ and T2 mapping at standardized anatomic locations in both knees was assessed. Participant-reported outcomes at follow-up time points were recorded.

RESULTS

Baseline Noyes scores of MRI detectable cartilage damage were highest in the injured knee lateral tibial plateau (mean 2.5, standard error (SE) 0.20, P < 0.01), followed by lateral femoral condyle (mean 2.1, SE 0.18, P < 0.01), which progressed after 1 year. Longitudinal prolongation at 12 months in the injured knees was significant for T1ρ affecting the medial and lateral femoral condyles (P < 0.01) and trochlea (P < 0.01), whereas T2 values were prolonged for medial and lateral femoral condyles (P < 0.01) and trochlea (P < 0.01). The contralateral noninjured knees also demonstrated T1ρ and T2 prolongation in the medial and lateral compartment chondral subdivisions. Progressive chondral damage occurred despite improved patient-reported outcomes.

CONCLUSION

After ACL injury, initial and sustained chondral damage predominantly affects the lateral tibiofemoral compartment, but longitudinal chondral degeneration also occurred in other compartments of the injured and contralateral knee.

CLINICAL RELEVANCE

Early identification of chondral degeneration post-ACL injury using morphological and quantitative MRI techniques could enable interventions to be implemented early to prevent or delay PTOA.

Cartilage compositional MRI-a narrative review of technical development and clinical applications over the past three decades

Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

Quantitative Cartilage T2 and T1rho Mapping: Is There a Clinical Role? From the AJR Special Series on Quantitative Imaging

Despite more than 20 years of development, the MRI-based cartilage compositional biomarkers T2 and T1rho have not been routinely applied in clinical practice. This review examines these measures' historical development and frames the challenges in the application of these quantitative imaging tools to the care of patients with cartilage injury and osteoarthritis using the hierarchical model of efficacy proposed by Fryback and Thornbury. T2 and T1rho have been validated for the evaluation of early compositional and structural changes in cartilage extracellular matrix. Yet, these biomarkers lack direct correlation with pain or function loss, lack standardization of methods for acquisition and analysis, and have a limited role in guiding therapeutic management given the absence of effective disease-modifying osteoarthritis drugs. These issues present significant challenges in the path to the biomarkers' future implementation in clinical care. Nonetheless, these MRI-based cartilage compositional biomarkers provide an essential tool for musculoskeletal research and can provide important information on the biophysical properties of cartilage that will continue to contribute to our understanding of cartilage injury and osteoarthritis pathogenesis.

Clinical validation of fully automated laminar knee cartilage transverse relaxation time (T2) analysis in anterior cruciate ligament (ACL)-injured knees- on behalf of the osteoarthritis (OA)-Bio consortium

Background

Magnetic resonance imaging (MRI) cartilage transverse relaxation time (T2) reflects cartilage composition, mechanical properties, and early osteoarthritis (OA). T2 analysis requires cartilage segmentation. In this study, we clinically validate fully automated T2 analysis at 1.5 Tesla (T) in anterior cruciate ligament (ACL)-injured and healthy knees.

Methods

We studied 71 participants: 20 ACL-injured patients with, and 22 without dynamic knee instability, 13 with surgical reconstruction, and 16 healthy controls. Sagittal multi-echo-spin-echo (MESE) MRIs were acquired at baseline and 1-year follow-up. Femorotibial cartilage was segmented manually; a convolutional neural network (CNN) algorithm was trained on MRI data from the same scanner.

Results

Dice similarity coefficients (DSCs) of automated versus manual segmentation in the 71 participants were 0.83 (femora) and 0.89 (tibiae). Deep femorotibial T2 was similar between automated (45.7±2.6 ms) and manual (45.7±2.7 ms) segmentation (P=0.828), whereas superficial layer T2 was slightly overestimated by automated analysis (53.2±2.2 vs. 52.1±2.1 ms for manual; P<0.001). T2 correlations were r=0.91-0.99 for deep and r=0.86-0.97 for superficial layers across regions. The only statistically significant T2 increase over 1 year was observed in the deep layer of the lateral femur [standardized response mean (SRM) =0.58 for automated vs. 0.52 for manual analysis; P<0.001]. There was no relevant difference in baseline/longitudinal T2 values/changes between the ACL-injured groups and healthy participants, with either segmentation method.

Conclusions

This clinical validation study suggests that automated cartilage T2 analysis from MESE at 1.5T is technically feasible and accurate. More efficient 3D sequences and longer observation intervals may be required to detect the impact of ACL injury induced joint instability on cartilage composition (T2).

Whole knee joint mapping using a phase modulated UTE adiabatic T1ρ (PM-UTE-AdiabT1ρ ) sequence

PURPOSE

To develop a 3D phase modulated UTE adiabatic T1ρ (PM-UTE-AdiabT1ρ ) sequence for whole knee joint mapping on a clinical 3 T scanner.

METHODS

This new sequence includes six major features: (1) a magnetization reset module, (2) a train of adiabatic full passage pulses for spin locking, (3) a phase modulation scheme (i.e., RF cycling pair), (4) a fat saturation module, (5) a variable flip angle scheme, and (6) a 3D UTE Cones sequence for data acquisition. A simple exponential fitting was used for T1ρ quantification. Phantom studies were performed to investigate PM-UTE-AdiabT1ρ 's sensitivity to compositional changes and reproducibility as well as its correlation with continuous wave-T1ρ measurement. The PM-UTE-AdiabT1ρ technique was then applied to five ex vivo and five in vivo normal knees to measure T1ρ values of femoral cartilage, meniscus, posterior cruciate ligament, anterior cruciate ligament, patellar tendon, and muscle.

RESULTS

The phantom study demonstrated PM-UTE-AdiabT1ρ 's high sensitivity to compositional changes, its high reproducibility, and its strong linear correlation with continuous wave-T1ρ measurement. The ex vivo and in vivo knee studies demonstrated average T1ρ values of 105.6 ± 8.4 and 77.9 ± 3.9 ms for the femoral cartilage, 39.2 ± 5.1 and 30.1 ± 2.2 ms for the meniscus, 51.6 ± 5.3 and 29.2 ± 2.4 ms for the posterior cruciate ligament, 79.0 ± 9.3 and 52.0 ± 3.1 ms for the anterior cruciate ligament, 19.8 ± 4.5 and 17.0 ± 1.8 ms for the patellar tendon, and 91.1 ± 8.8 and 57.6 ± 2.8 ms for the muscle, respectively.

CONCLUSION

The 3D PM-UTE-AdiabT1ρ sequence allows volumetric T1ρ assessment for both short and long T2 tissues in the knee joint on a clinical 3 T scanner.

Deep learning-based automatic pipeline for quantitative assessment of thigh muscle morphology and fatty infiltration

PURPOSE

Fast and accurate thigh muscle segmentation from MRI is important for quantitative assessment of thigh muscle morphology and composition. A novel deep learning (DL) based thigh muscle and surrounding tissues segmentation model was developed for fully automatic and reproducible cross-sectional area (CSA) and fat fraction (FF) quantification and tested in patients at 10 years after anterior cruciate ligament reconstructions.

METHODS

A DL model combining UNet and DenseNet was trained and tested using manually segmented thighs from 16 patients (32 legs). Segmentation accuracy was evaluated using Dice similarity coefficients (DSC) and average symmetric surface distance (ASSD). A UNet model was trained for comparison. These segmentations were used to obtain CSA and FF quantification. Reproducibility of CSA and FF quantification was tested with scan and rescan of six healthy subjects.

RESULTS

The proposed UNet and DenseNet had high agreement with manual segmentation (DSC >0.97, ASSD < 0.24) and improved performance compared with UNet. For hamstrings of the operated knee, the automated pipeline had largest absolute difference of 6.01% for CSA and 0.47% for FF as compared to manual segmentation. In reproducibility analysis, the average difference (absolute) in CSA quantification between scan and rescan was better for the automatic method as compared with manual segmentation (2.27% vs. 3.34%), whereas the average difference (absolute) in FF quantification were similar.

CONCLUSIONS

The proposed method exhibits excellent accuracy and reproducibility in CSA and FF quantification compared with manual segmentation and can be used in large-scale patient studies.