Ultrashort echo time magnetization transfer imaging of knee cartilage and meniscus after long-distance running

Visualizing Preosteoarthritis: Updates on UTE-Based Compositional MRI and Deep Learning Algorithms

Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is crucial for early proactive management and limit growing disease burden. The more recent advanced quantitative imaging techniques and deep learning (DL) algorithms in musculoskeletal imaging have shown great potential for visualizing "pre-OA." In this review, we first focus on ultrashort echo time-based magnetic resonance imaging (MRI) techniques for direct visualization as well as quantitative morphological and compositional assessment of both short- and long-T2 musculoskeletal tissues, and second explore how DL revolutionize the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the classification, prediction, and management of OA. PLAIN LANGUAGE SUMMARY: Detecting osteoarthritis (OA) before the onset of irreversible changes is crucial for early proactive management. OA is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Ultrashort echo time-based magnetic resonance imaging (MRI), in particular, enables direct visualization and quantitative compositional assessment of short-T2 tissues. Deep learning is revolutionizing the way of MRI analysis (eg, automatic tissue segmentation and extraction of quantitative image biomarkers) and the detection, classification, and prediction of disease. They together have made further advances toward identification of imaging biomarkers/features for pre-OA. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Characterization of the in vivo transient responses of the femoral cartilage by means of quantitative ultrasound imaging techniques

BACKGROUND

Quantitative ultrasound (QUS) techniques are new diagnostic tools able to identify changes in structural and material properties of the investigated tissue. For the first time, we evaluated the capability of QUS techniques in determining the in vivo transient changes in knee joint cartilage after a stressful task.

METHODS

An ultrasound scanner collecting B-mode and radiofrequency data simultaneously was used to collect data from the femoral cartilage of the right knee in 15 participants. Cartilage thickness (CTK), ultrasound roughness index (URI), average magnitude ratio (AMR), and Nakagami parameters (NA) were evaluated before, immediately after and every 5 min up to 45 min a stressful task (30 min of running on a treadmill with a negative slope of 5%).

RESULTS

CTK was affected by time (main effect: p < 0.001). Post hoc test showed significant differences with CTK at rest, which were observed up to 30 min after the run. AMR and NA were affected by time (p < 0.01 for both variables), while URI was unaffected by it. For AMR, post hoc test showed significant differences with rest values in the first 35 min of recovery, while NA was increased compared to rest values in all time points.

CONCLUSION

Data suggest that a single running trial is not able to modify the integrity of the femoral cartilage, as reported by URI data. In vivo evaluation of QUS parameters of the femoral cartilage (NA, AMR, and URI) are able to characterize changes in cartilage properties over time.