Quantitative assessment of articular cartilage degeneration using 3D ultrashort echo time cones adiabatic T1ρ (3D UTE-Cones-AdiabT1ρ) imaging

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.

The effect of cartilage dehydration and rehydration on quantitative ultrashort echo time biomarkers

Background

The effect of dehydration of ex vivo cartilage samples and rehydration with native synovial fluid or normal saline on quantitative ultrashort echo time (UTE) biomarkers are unknown. We aimed to investigate the effect of cartilage dehydration-rehydration on UTE biomarkers and to compare the rehydration capabilities of native synovial fluid and normal saline.

Methods

A total of 37 cartilage samples were harvested from patients (n=5) who underwent total knee replacement. Fresh cartilage samples were exposed to air to dehydrate for 2 hours after baseline magnetic resonance (MR) scanning, then randomly divided into two groups: one soaking in native synovial fluid (n=17) and the other in normal saline (n=20) to rehydrate for 4 hours. UTE-based biomarkers [T1, adiabatic T1r (AdiabT1r), macromolecular fraction (MMF), magnetization transfer ratio (MTR), and T2*] and sample weights were evaluated for fresh, dehydrated, and rehydrated cartilage samples. Differences and agreements between groups were assessed using the values of fresh cartilage samples as reference standard.

Results

Dehydrating in air for 2 hours resulted in significant weight loss (P=0.000). T1, AdiabT1r, and T2* decreased significantly while MMF and MTR increased significantly (all P<0.02). Non-significant differences were observed in cartilage weights after rehydrating in both synovial fluid and normal saline, with P values being 0.204 and 0.769, respectively. There were no significant differences in T1, AdiabT1r, MMF, and MTR after rehydrating in synovial fluid (P>0.0167, with Bonferroni correction) while T2* (P=0.001) still had significant differences compared with fresh samples. However, no significant differences were detected for any of the evaluated UTE biomarkers after rehydrating in normal saline (all P>0.05). No differences were detected in the agreement of UTE biomarker measurements between fresh samples and samples rehydrated with synovial fluid and normal saline.

Conclusions

Cartilage dehydration resulted in significant changes in UTE biomarkers. Rehydrating with synovial fluid or normal saline had non-significant effect on all the evaluated UTE biomarkers except T2* values, which still had significant differences compared with fresh samples after rehydrating with synovial fluid. No significant difference was observed in the rehydration capabilities of native synovial fluid and normal saline.

Deep Learning Convolutional Neural Network Reconstruction and Radial k-Space Acquisition MR Technique for Enhanced Detection of Retropatellar Cartilage Lesions of the Knee Joint

OBJECTIVES

To assess diagnostic performance of standard radial k-space (PROPELLER) MRI sequences and compare with accelerated acquisitions combined with a deep learning-based convolutional neural network (DL-CNN) reconstruction for evaluation of the knee joint.

METHODS

Thirty-five patients undergoing MR imaging of the knee at 1.5 T were prospectively included. Two readers evaluated image quality and diagnostic confidence of standard and DL-CNN accelerated PROPELLER MR sequences using a four-point Likert scale. Pathological findings of bone, cartilage, cruciate and collateral ligaments, menisci, and joint space were analyzed. Inter-reader agreement (IRA) for image quality and diagnostic confidence was assessed using intraclass coefficients (ICC). Cohen's Kappa method was used for evaluation of IRA and consensus between sequences in assessing different structures. In addition, image quality was quantitatively evaluated by signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements.

RESULTS

Mean acquisition time of standard vs. DL-CNN sequences was 10 min 3 s vs. 4 min 45 s. DL-CNN sequences showed significantly superior image quality and diagnostic confidence compared to standard MR sequences. There was moderate and good IRA for assessment of image quality in standard and DL-CNN sequences with ICC of 0.524 and 0.830, respectively. Pathological findings of the knee joint could be equally well detected in both sequences (κ-value of 0.8). Retropatellar cartilage could be significantly better assessed on DL-CNN sequences. SNR and CNR was significantly higher for DL-CNN sequences (both p < 0.05).

CONCLUSIONS

In MR imaging of the knee, DL-CNN sequences showed significantly higher image quality and diagnostic confidence compared to standard PROPELLER sequences, while reducing acquisition time substantially. Both sequences perform comparably in the detection of knee-joint pathologies, while DL-CNN sequences are superior for evaluation of retropatellar cartilage lesions.

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications

Osteoarthritis (OA) is a widely occurring degenerative joint disease that is severely debilitating and causes significant socioeconomic burdens to society. Magnetic resonance imaging (MRI) is the preferred imaging modality for the morphological evaluation of cartilage due to its excellent soft tissue contrast and high spatial resolution. However, its utilization typically involves subjective qualitative assessment of cartilage. Compositional MRI, which refers to the quantitative characterization of cartilage using a variety of MRI methods, can provide important information regarding underlying compositional and ultrastructural changes that occur during early OA. Cartilage compositional MRI could serve as early imaging biomarkers for the objective evaluation of cartilage and help drive diagnostics, disease characterization, and response to novel therapies. This review will summarize current and ongoing state-of-the-art cartilage compositional MRI techniques and highlight emerging methods for cartilage compositional MRI including MR fingerprinting, compressed sensing, multiexponential relaxometry, improved and robust radio-frequency pulse sequences, and deep learning-based acquisition, reconstruction, and segmentation. The review will also briefly discuss the current challenges and future directions for adopting these emerging cartilage compositional MRI techniques for use in clinical practice and translational OA research studies. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 2.