Rapid in vivo multicomponent T2 mapping of human knee menisci

Extracorporeal shockwave therapy for degenerative meniscal tears results in a decreased T2 relaxation time and pain relief: An exploratory randomized clinical trial

PURPOSE

The optimal management of degenerative meniscal tears remains controversial. Extracorporeal shockwave therapy (ESWT) has been shown to promote tissue repair in both preclinical and clinical studies; however, its effect on degenerative meniscal tears remains unknown. This study aimed to examine whether ESWT improves meniscal degeneration.

METHODS

This randomized trial was conducted between 2020 and 2022 and involved patients with degenerative medial meniscal tears. Patients were allocated to receive either focused ESWT (0.25 mJ/mm2, 2000 impulses, 3 sessions with a 1-week interval) or sham treatment. Patients were evaluated using magnetic resonance imaging (MRI) before treatment and at 12 months after treatment. The primary endpoint was improvement in meniscal degeneration, as assessed by the change in T2 relaxation time from baseline on MRI T2 mapping. Knee pain and clinical outcomes were also examined at the same time.

RESULTS

Of 29 randomized patients, 27 patients (mean age 63.9 ± 8.7 years; females 37%; ESWT group 14 patients; control group 13 patients) were included in the final analysis. At 12 months postintervention, patients in the ESWT group showed a greater decrease in the T2 relaxation time (ESWT group -2.9 ± 1.7 ms vs. control group 1.0 ± 1.9 ms; p < 0.001) and had less knee pain (p = 0.04). The clinical outcomes at 12 months post-treatment were not statistically significant. No adverse events were reported.

CONCLUSION

ESWT decreased the T2 relaxation time in the meniscus at 12 months post-treatment. ESWT also provided pain relief, but no differences were observed in clinical outcomes.

LEVEL OF EVIDENCE

Level II.

Relating MR relaxation times of ex vivo meniscus to tissue degeneration through comparison with histopathology

Background

Quantitative magnetic resonance imaging (MRI), e.g. relaxation parameter mapping, may be sensitive to structural and compositional tissue changes, and could potentially be used to non-invasively detect and monitor early meniscus degeneration related to knee osteoarthritis.

Objective

To investigate MR relaxation times as potential biomarkers for meniscus degeneration through comparisons with histopathology.

Methods

We measured MR relaxation parameters in the posterior horn of 40 menisci (medial and lateral) at a wide range of degenerative stages. T1, T2 and T2* were mapped using standard and ultrashort echo time sequences at 9.4 T and compared to gold standard histology using Pauli's histopathological scoring system, including assessment of surface integrity, collagen organization, cellularity and Safranin-O staining.

Results

All three relaxation times increased with total Pauli score (mean difference per score (95% CI) for T2*: 0.62 (0.37, 0.86), T2: 0.83 (0.53, 1.1) and T1: 24.7 (16.5, 32.8) ms/score). Clear associations were seen with scores of surface integrity (mean difference per score for T2*: 3.0 (1.8, 4.2), T2: 4.0 (2.5, 5.5) and T1: 116 (75.6, 156) ms/score) and collagen organization (mean difference between highest and lowest score for T2*: 5.3 (1.6, 8.9), T2: 6.1 (1.7, 11) and T1: 204 (75.9, 332) ms). The results were less clear for the remaining histopathological measures.

Conclusions

MR relaxation times T1, T2 and T2* of ex vivo human menisci are associated with histologically verified degenerative processes, in particular related to surface integrity and collagen organization. If confirmed in vivo, MR relaxation times may thus be potential biomarkers for meniscus degeneration.

Deep convolutional neural network for segmentation of knee joint anatomy

PURPOSE

To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation.

METHODS

A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder-decoder network was designed as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin-echo (3D-FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data.

RESULTS

The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non-specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm.

CONCLUSION

The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well-suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning-based segmentation method has promising potential applications in musculoskeletal imaging.

Distribution of tightly and loosely bound water in biological macromolecules and age-related diseases

This mini-review article is focused on publications devoted to the changes in water binding energy and content of bound water in biological tissues during aging processes, when bound water lost from the hydration layer becomes free water. Bound water is released during cataractogenesis. In skin, water bound to proteins and other biomacromolecules becomes more mobile with increasing skin age. Extracellular to intracellular water ratio increases with age and was associated with muscle cell atrophy. Bound water concentration decreases with age in hydrated human bone and can be correlated with the strength and toughness of the bone. Higher fraction of free water in malignant tissues compared to normal tissues was observed. Hydration water mobility is enhanced around tau amyloid fibers. Water plays a decisive role in amyloid formation as entropic driving force. In the natural aging processes dehydration and glycation may be considered as subsequent steps.

Automated T2-mapping of the Menisci From Magnetic Resonance Images in Patients with Acute Knee Injury

RATIONALE AND OBJECTIVES

This study aimed to evaluate the accuracy of an automated method for segmentation and T2 mapping of the medial meniscus (MM) and lateral meniscus (LM) in clinical magnetic resonance images from patients with acute knee injury.

MATERIALS AND METHODS

Eighty patients scheduled for surgery of an anterior cruciate ligament or meniscal injury underwent magnetic resonance imaging of the knee (multiplanar two-dimensional [2D] turbo spin echo [TSE] or three-dimensional [3D]-TSE examinations, T2 mapping). Each meniscus was automatically segmented from the 2D-TSE (composite volume) or 3D-TSE images, auto-partitioned into anterior, mid, and posterior regions, and co-registered onto the T2 maps. The Dice similarity index (spatial overlap) was calculated between automated and manual segmentations of 2D-TSE (15 patients), 3D-TSE (16 patients), and corresponding T2 maps (31 patients). Pearson and intraclass correlation coefficients (ICC) were calculated between automated and manual T2 values. T2 values were compared (Wilcoxon rank sum tests) between torn and non-torn menisci for the subset of patients with both manual and automated segmentations to compare statistical outcomes of both methods.

RESULTS

The Dice similarity index values for the 2D-TSE, 3D-TSE, and T2 map volumes, respectively, were 76.4%, 84.3%, and 75.2% for the MM and 76.4%, 85.1%, and 76.1% for the LM. There were strong correlations between automated and manual T2 values (r_MM = 0.95, ICC_MM = 0.94; r_LM = 0.97, ICC_LM = 0.97). For both the manual and the automated methods, T2 values were significantly higher in torn than in non-torn MM for the full meniscus and its subregions (P < .05). Non-torn LM had higher T2 values than non-torn MM (P < .05).

CONCLUSIONS

The present automated method offers a promising alternative to manual T2 mapping analyses of the menisci and a considerable advance for integration into clinical workflows.

Assessment of different fitting methods for in-vivo bi-component T2* analysis of human patellar tendon in magnetic resonance imaging

PURPOSE

To investigate the robustness of four fitting methods for bi-component effective spin-spin T2 (T2*) relaxation time analysis of human patellar tendon.

METHODS

A three-dimensional (3D) cone ultra-short echo-time (UTE) sequence was performed on the knees of ten healthy volunteers at 3.0T. Four fitting methods incorporating either Gaussian or Rician noise distribution were used for voxel-by-voxel bi-component T2* analysis of the patellar tendon. The T2* for the short relaxing (T**,s ) and long relaxing (T*2,l ) water components and the fraction of the short relaxing water component (fs ) were measured, and different fitting methods were compared using Friedman's and Wilcoxon signed rank tests. A numerical simulation study was also performed to predict the accuracy and precision of bi-component T2* parameter estimation in tendon at different signal-to-noise ratios (SNR) levels.

RESULTS

The average T*2,s , T*2,l , fs of human patellar tendon were 1.5ms, 30ms, and 80% respectively. Incorporating different noise models and fitting methods influenced the measured bi-component T2* parameters. Fitting methods incorporating Rician noise were superior to traditional fitting methods for bi-component T2* analysis especially at lower SNR. fs and T*2,s were less sensitive than T*2,1 to noise at even moderate and low SNR. The result of the in-vivo bi-component T2* analysis of tendon agreed well with numerical simulations.

CONCLUSION

Our study demonstrated the use of a 3D cone UTE sequence to perform in vivo voxel-by-voxel bi-component T2* analysis of human patellar tendon. Incorporating Rician noise was useful for improving bi-component T2* analysis especially at lower SNR.

LEVEL OF EVIDENCE

IV.

Multicomponent T2 analysis of articular cartilage with synovial fluid partial volume correction

PURPOSE

To investigate the use of a three-pool model to account for the confounding effects of synovial fluid on multicomponent T2 analysis of articular cartilage using Multicomponent Driven Equilibrium Single Shot Observation of T1 and T2 (mcDESPOT).

MATERIALS AND METHODS

mcDESPOT was performed on the knee of eight asymptomatic volunteers and eight patients with osteoarthritis at 3.0T with multicomponent T2 maps created using the two-pool model and a three-pool model containing a nonexchanging synovial fluid water pool. The fraction of the fast-relaxing water component (FF ) and the T2 relaxation times for the fast-relaxing (T2F ) and slow-relaxing (T2S ) water components were measured in the superficial and deep layers of patellar cartilage using the two-pool and three-pool models in asymptomatic volunteers and patients with osteoarthritis and were compared using Wilcoxon signed rank tests.

RESULTS

Within the superficial layer of patellar cartilage, FF was 22.5% and 25.6% for asymptomatic volunteers and 21.3% and 22.8% for patients with osteoarthritis when using the two-pool and three-pool models, respectively, while T2S was 73.9 msec and 62.0 msec for asymptomatic volunteers and 72.0 msec and 63.1 msec for patients with osteoarthritis when using the two-pool and three-pool models, respectively. For both asymptomatic volunteers and patients with osteoarthritis, the two-pool model provided significantly (P < 0.05) lower FF and higher T2S than the three-pool model, likely due to the effects of synovial fluid partial volume averaging.

CONCLUSION

The effects of partial volume averaging between superficial cartilage and synovial fluid may result in biased multicomponent T2 measurements that can be corrected using an mcDESPOT three-pool model containing a nonexchanging synovial fluid water pool.