Articular Cartilage of the Human Knee Joint: In Vivo Multicomponent T2 Analysis at 3.0 T

Performance of MR learned pulse sequences for 3D bi-exponential, stretched-exponential, and mono-exponential T2 and T1ρ mapping of knee cartilage

PURPOSE

To compare the performance of a learned magnetization-prepared gradient echo (L-MPGRE) sequence against a commonly used sequence for 3D T2 and T1ρ mapping of the knee joint, the magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots (MAPSS), on bi-exponential (BE), stretched-exponential (SE), and mono-exponential (ME) relaxation models.

METHODS

We used a combined differentiable and non-differentiable optimization to learn pulse sequence structure and its parameters for 3D T2 and T1ρ mapping of the knee joint using ME, SE, and BE models. The learned pulse sequence framework was used to improve quantitative accuracy and SNR and to reduce filtering effects. We compare the measured multi-compartment values between the two sequences (n = 8), and their repeatability (n = 4) in healthy volunteers (n = 12 total).

RESULTS

The voxel-wise median absolute percentage difference (MAPD) between the T2 and T1ρ maps obtained with each sequence was 18.6% and 19.9%, respectively. The T2 and T1ρ repeatability tests showed a MAPD of 18.5% and 19.1% for MAPSS, and 16.8% and 15.5% for L-MPGRE. Bland-Altman region of interest (ROI)-wise analysis shows that bias is small, close to -1.5%, and the coefficient of variation is around 5.5% when comparing ROIs from both sequences.

CONCLUSION

The L-MPGRE sequences can be used as a replacement for MAPSS for T2 and T1ρ mapping in the knee cartilage with advantages, achieving similar accuracy and 15% better repeatability in only half of its scan time.

Quantification of Synovial Fluid Using Magnetic Resonance Fingerprinting Multicomponent Imaging in the Articular Cartilage of the Knee

RATIONALE AND OBJECTIVES

The purpose of this study was to verify the feasibility of magnetic resonance fingerprinting (MRF)-derived synovial fluid fraction (SFF) mapping for quantifying subvoxel-sized cartilage defects.

MATERIALS AND METHODS

MRF was performed on a 3-Tesla scanner and used to derive T2 and SFF maps. An ex vivo experiment was performed using bovine bone; different numbers of holes (4, 6, 8, 10, and 12) were drilled separately on the articular surface, and SFF values were compared among the drilled areas. In a clinical study, 16 osteoarthritis patients underwent sagittal 3D fast spinecho (FSE) and MRF scanning, and knee cartilage segmentation was performed on each image. For morphologic analysis, fluid-excluded images of the SFF (FEISFF) and T2 maps (FEIT2) were generated using the cartilage segmentations, and the whole-organ magnetic resonance imaging score (WORMS) of each FEI and 3D FSE image were compared using the kappa coefficient. For quantitative analysis, intact cartilage volumes in the SFF (VSFF) and T2 maps (VT2) were calculated, and their correlations with reference to the actual cartilage volume on 3D FSE images (V3D) were evaluated.

RESULTS

In the ex vivo experiment, the SFF value increased as the number of holes increased. The kappa coefficients of the WORMS were 0.80 and 0.64 in the SFF and T2 maps, respectively, and substantial to almost perfect agreement was observed in the medial tibiofemoral joint. The V3D-VSFF and V3D-VT2 correlation coefficients differed by 0.03 or more in the medial tibiofemoral joint.

CONCLUSION

The MRF-derived SFF map can feasibly evaluate small, invisible cartilage defects and quantify cartilage volumes.

Application of functional magnetic resonance imaging for evaluation of cartilage injury effect on knee joint function by recurrent patellar dislocation

Explore the therapeutic effect of vastus medialis oblique plasty and the reliability and applicability of functional magnetic resonance imaging as a diagnostic method for early cartilage degeneration and injury diagnosis. From July 2020 to July 2022, there were 53 patients with recurrent patellar dislocation who met the inclusion criteria for surgery, including 34 women and 19 men, aged 11 to 53 years, with an average age of 24.4 years. After patient selection, functional magnetic resonance imaging was performed before surgery. According to the presence or absence of cartilage injury, they were divided into cartilage injury group (n = 28) and non-cartilage injury group (n = 25), and underwent vastus medialis oblique plasty. Preoperative patellar axial radiographs were performed in both groups of patients to measure the patellar tilt angle and lateral patellofemoral angle. The Lysholm, Kujala, and VAS (visual analogue scale) scores were applied to assess changes in knee joint function and anterior knee pain. All patients were postoperatively followed up. The patellar tilt angle and lateral patellofemoral angle of the 2 groups were significantly improved postoperatively (P < .05), with no statistical difference between the 2 groups (P > .05). Significant differences were observed in the VAS changes between the cartilage injury group and the non-cartilage injury group before and after operation (P < .05). There was a statistical difference in VAS score between the groups (P < .05). The changes in the Lysholm and Kujala scores before and after the operation in the cartilage injury and the non-cartilage injury groups were statistically different (P < .05). There was statistical difference between the 2 groups in Lysholm score and Kujala score after operation (P < .05). Vastus medialis oblique plasty significantly improved knee joint function and pain. Patients with cartilage injury had worse preoperative and postoperative knee function than patients without cartilage injury. Functional magnetic resonance imaging can reflect the early-stage changes in the biochemical cartilage components caused by recurrent patellar dislocation.

Comparison of Dynamic Knee Contact Mechanics with T2 Imaging in Different Ages of Healthy Participants

Aging is a known risk factor for Osteoarthritis (OA), however, relations between cartilage composition and aging remain largely unknown in understanding human OA. T2 imaging provides an approach to assess cartilage composition. Whether these T2 relaxation times in the joint contact region change with time during gait remain unexplored. The study purpose was to demonstrate a methodology for linking dynamic joint contact mechanics to cartilage composition as measured by T2 relaxometry. T2 relaxation times for unloaded cartilage were measured in a 3T General Electric magnetic resonance (MR) scanner in this preliminary study. High-speed biplanar video-radiography (HSBV) was captured for five 20-30-year-old and five 50-60-year-old participants with asymptomatic knees. By mapping the T2 cartilages to the dynamic contact regions, T2 values were averaged over the contact area at each measurement within the gait cycle. T2 values demonstrated a functional relationship across the gait cycle. There were no statistically significant differences between 20- and 30-year-old and 50-60-year-old participant T2 values at first force peak of the gait cycle in the medial femur (p = 1.00, U = 12) or in the medial tibia (p = 0.31, U = 7). In the medial and lateral femur in swing phase, the joint moved from a region of high T2 values at 75% of gait to a minimum at 85-95% of swing. The lateral femur and tibia demonstrated similar patterns to the medial compartments but were less pronounced. This research advances understanding of the linkage between cartilage contact and cartilage composition. The change from a high T2 value at ~ 75% of gait to a lower value near the initiation of terminal swing (90% gait) indicates that there are changes to T2 averages corresponding to changes in the contact region across the gait cycle. No differences were found between age groups for healthy participants. These preliminary findings provide interesting insights into the cartilage composition corresponding to dynamic cyclic motion and inform mechanisms of osteoarthritis.

New insights in osteoarthritis diagnosis and treatment: Nano-strategies for an improved disease management

Osteoarthritis (OA) is a common chronic joint pathology that has become a predominant cause of disability worldwide. Even though the origin and evolution of OA rely on different factors that are not yet elucidated nor understood, the development of novel strategies to treat OA has emerged in the last years. Cartilage degradation is the main hallmark of the pathology though alterations in bone and synovial inflammation, among other comorbidities, are also involved during OA progression. From a molecular point of view, a vast amount of signaling pathways are implicated in the progression of the disease, opening up a wide plethora of targets to attenuate or even halt OA. The main purpose of this review is to shed light on the recent strategies published based on nanotechnology for the early diagnosis of the disease as well as the most promising nano-enabling therapeutic approaches validated in preclinical models. To address the clinical issue, the key pathways involved in OA initiation and progression are described as the main potential targets for OA prevention and early treatment. Furthermore, an overview of current therapeutic strategies is depicted. Finally, to solve the drawbacks of current treatments, nanobiomedicine has shown demonstrated benefits when using drug delivery systems compared with the administration of the equivalent doses of the free drugs and the potential of disease-modifying OA drugs when using nanosystems. We anticipate that the development of smart and specific bioresponsive and biocompatible nanosystems will provide a solid and promising basis for effective OA early diagnosis and treatment. This article is categorized under: Diagnostic Tools > In Vivo Nanodiagnostics and Imaging Implantable Materials and Surgical Technologies > Nanotechnology in Tissue Repair and Replacement.

Quantitative MRI for Evaluation of Musculoskeletal Disease: Cartilage and Muscle Composition, Joint Inflammation, and Biomechanics in Osteoarthritis

ABSTRACT

Magnetic resonance imaging (MRI) is a valuable tool for evaluating musculoskeletal disease as it offers a range of image contrasts that are sensitive to underlying tissue biochemical composition and microstructure. Although MRI has the ability to provide high-resolution, information-rich images suitable for musculoskeletal applications, most MRI utilization remains in qualitative evaluation. Quantitative MRI (qMRI) provides additional value beyond qualitative assessment via objective metrics that can support disease characterization, disease progression monitoring, or therapy response. In this review, musculoskeletal qMRI techniques are summarized with a focus on techniques developed for osteoarthritis evaluation. Cartilage compositional MRI methods are described with a detailed discussion on relaxometric mapping (T 2 , T 2 *, T 1ρ ) without contrast agents. Methods to assess inflammation are described, including perfusion imaging, volume and signal changes, contrast-enhanced T 1 mapping, and semiquantitative scoring systems. Quantitative characterization of structure and function by bone shape modeling and joint kinematics are described. Muscle evaluation by qMRI is discussed, including size (area, volume), relaxometric mapping (T 1 , T 2 , T 1ρ ), fat fraction quantification, diffusion imaging, and metabolic assessment by 31 P-MR and creatine chemical exchange saturation transfer. Other notable technologies to support qMRI in musculoskeletal evaluation are described, including magnetic resonance fingerprinting, ultrashort echo time imaging, ultrahigh-field MRI, and hybrid MRI-positron emission tomography. Challenges for adopting and using qMRI in musculoskeletal evaluation are discussed, including the need for metal artifact suppression and qMRI standardization.

T2 mapping for knee cartilage degeneration in young patients with mild symptoms

BACKGROUND

We aimed to analyze the distribution of knee cartilage degeneration in young patients with mild symptoms using quantitative magnetic resonance imaging (MRI) T2 mapping.

MATERIALS AND METHODS

This study included sixty six patients (case group) and twenty eight healthy volunteers (control group). The participants underwent 3.0 T conventional MRI plus a multi-echo sequence. The cartilage of each participant was divided into twenty eight subregions. We then calculated the T2 mean values and standard deviation or median and quartile range for each subregion according to whether the normal distribution was satisfied. Besides, we employed Kruskal-Wallis test to determine the statistical differences of each subregion in the control group while the Mann-Whitney U test was used to define the statistical difference between the case group and the control group and between the control group and subjects aged less than or equal to 35 years in the case group.

RESULTS

In the case group, age of 30 male patients was 31.5 ± 9.3 and age of 36 female patients was 35.7 ± 8.3. In the two groups, the superficial central lateral femoral region exhibited relatively high T2 values (control/case group: 49.6 ± 2.7/55.9 ± 8.8), and the deep medial patellar region exhibited relatively low T2 values (control/case group: 34.2 ± 1.3/33.5(32.2, 35.5)). Comparison of the T2 values between the case and the control group demonstrated a statistically significant increase in nine subregions (P₁ < 0.05) and there were five subregions in the case group with age ≤ 35 years (P₂ < 0.05). In particular, the p-values for four subregions of the patellofemoral joint were all less than 0.05 (P₁ = 0.002, 0.015, 0.036, 0.005).

CONCLUSION

T2 values of patients were significantly different with values of healthy groups, especially in the superficial cartilage of the patellofemoral joint. It made T2 mapping helpful to early identify patients with knee cartilage degeneration.

Rapid MR relaxometry using deep learning: An overview of current techniques and emerging trends

Quantitative mapping of MR tissue parameters such as the spin-lattice relaxation time (T1 ), the spin-spin relaxation time (T2 ), and the spin-lattice relaxation in the rotating frame (T1ρ ), referred to as MR relaxometry in general, has demonstrated improved assessment in a wide range of clinical applications. Compared with conventional contrast-weighted (eg T1 -, T2 -, or T1ρ -weighted) MRI, MR relaxometry provides increased sensitivity to pathologies and delivers important information that can be more specific to tissue composition and microenvironment. The rise of deep learning in the past several years has been revolutionizing many aspects of MRI research, including image reconstruction, image analysis, and disease diagnosis and prognosis. Although deep learning has also shown great potential for MR relaxometry and quantitative MRI in general, this research direction has been much less explored to date. The goal of this paper is to discuss the applications of deep learning for rapid MR relaxometry and to review emerging deep-learning-based techniques that can be applied to improve MR relaxometry in terms of imaging speed, image quality, and quantification robustness. The paper is comprised of an introduction and four more sections. Section 2 describes a summary of the imaging models of quantitative MR relaxometry. In Section 3, we review existing "classical" methods for accelerating MR relaxometry, including state-of-the-art spatiotemporal acceleration techniques, model-based reconstruction methods, and efficient parameter generation approaches. Section 4 then presents how deep learning can be used to improve MR relaxometry and how it is linked to conventional techniques. The final section concludes the review by discussing the promise and existing challenges of deep learning for rapid MR relaxometry and potential solutions to address these challenges.

A semi-automatic framework based upon quantitative analysis of MR-images for classification of femur cartilage into asymptomatic, early OA, and advanced-OA groups

To develop a semi-automatic framework for quantitative analysis of biochemical properties and thickness of femur cartilage using magnetic resonance (MR) images and evaluate its potential for femur cartilage classification into asymptomatic (AS), early osteoarthritis (OA), and advanced OA groups. In this study, knee joint MRI data (fat suppressed-proton density-weighted and multi-echo T2-weighted images) of eight AS-volunteers (data acquired twice) and 34 OA patients including 20 early OA (16 Grade-I and 4 Grade-II), 14 advanced-OA (Grade-III) were acquired at 3.0T MR scanner. Modified Outerbridge classification criteria was performed for the clinical evaluation of data by an experienced radiologist. Cartilage segmentation, T2-mapping, 2D-WearMap generation, and subregion analysis were performed semi-automatically using in-house developed algorithms. The intraclass correlation coefficient (ICC) and coefficient of variation (CV) were computed for testing the reproducibility of T2 values. One-way analysis of variance with Tukey-Kramer post hoc test was performed for evaluating the differences among the groups. The performance of individual T2 and thickness, as well as their combination using logistic regression, were evaluated with receiver operating characteristics (ROC) curve analysis. The interscan agreement based on the ICC index was 0.95 and the CV was 2.45 ± 1.33%. T2 mean of values greater than 75th percentile showed sensitivity and specificity of 94.1% and 81.3% (AUC = 0.93, cut-off value = 47.9 ms) in differentiating AS volunteers versus OA group, while sensitivity and specificity of 90.0% and 81.3% (AUC = 0.90, cut-off value = 47.9 ms) in differentiating AS volunteers versus early OA groups, respectively. In the differentiation of early OA versus advanced-OA group, ROC results of combination (T2 and thickness) showed the highest sensitivity and specificity of 85.7%, and 70.0% (AUC = 0.79, cut-off value = 0.39) compared with individual T2 and thickness features, respectively. A computer-aided quantitative evaluation of femur cartilage degeneration showed promising results and can be used to assist clinicians in diagnosing OA.

Bi-component T2 mapping correlates with articular cartilage material properties

Non-invasive estimation of cartilage material properties is useful for understanding cartilage health and creating subject-specific computational models. Bi-component T2 mapping measured using Multi-Component Driven Equilibrium Single Shot Observation of T₁ and T₂ (mcDESPOT) is sensitive for detecting cartilage degeneration within the human knee joint, but has not been correlated with cartilage composition and mechanical properties. Therefore, the purpose of this study was to investigate the relationship between bi-component T₂ parameters measured using mcDESPOT at 3.0 T and cartilage composition and mechanical properties. Ex-vivo patellar cartilage specimens harvested from five human cadaveric knees were imaged using mcDESPOT at 3.0 T. Cartilage samples were removed from the patellae, mechanically tested to determine linear modulus and dissipated energy, and chemically tested to determine proteoglycan and collagen content. Parameter maps of single-component T₂ relaxation time (T₂), the T₂ relaxation times of the fast relaxing macromolecular bound water component (T_2F) and slow relaxing bulk water component (T_2S), and the fraction of the fast relaxing macromolecular bound water component (F_F) were compared to mechanical and chemical measures using linear regression. F_F was significantly (p < 0.05) correlated with energy dissipation and linear modulus. T₂ was significantly (p ≤ 0.05) correlated with elastic modulus at 1 Hz and energy dissipated at all frequencies. There were no other significant (p = 0.13-0.97) correlations between mcDESPOT parameters and mechanical properties. F_F was significantly (p = 0.04) correlated with proteoglycan content. There were no other significant (p = 0.19-0.92) correlations between mcDESPOT parameters and proteoglycan or collagen content. This study suggests that F_F measured using mcDESPOT at 3.0 T could be used to non-invasively estimate cartilage proteoglycan content, elastic modulus, and energy dissipation.

Fast multicomponent 3D-T1ρ relaxometry

NMR relaxometry can provide information about the relaxation of the magnetization in different tissues, increasing our understanding of molecular dynamics and biochemical composition in biological systems. In general, tissues have complex and heterogeneous structures composed of multiple pools. As a result, bulk magnetization returns to its original state with different relaxation times, in a multicomponent relaxation. Recovering the distribution of relaxation times in each voxel is a difficult inverse problem; it is usually unstable and requires long acquisition time, especially on clinical scanners. MRI can also be viewed as an inverse problem, especially when compressed sensing (CS) is used. The solution of these two inverse problems, CS and relaxometry, can be obtained very efficiently in a synergistically combined manner, leading to a more stable multicomponent relaxometry obtained with short scan times. In this paper, we will discuss the details of this technique from the viewpoint of inverse problems.

Improving Quantitative Magnetic Resonance Imaging Using Deep Learning

Deep learning methods have shown promising results for accelerating quantitative musculoskeletal (MSK) magnetic resonance imaging (MRI) for T2 and T1ρ relaxometry. These methods have been shown to improve musculoskeletal tissue segmentation on parametric maps, allowing efficient and accurate T2 and T1ρ relaxometry analysis for monitoring and predicting MSK diseases. Deep learning methods have shown promising results for disease detection on quantitative MRI with diagnostic performance superior to conventional machine-learning methods for identifying knee osteoarthritis.

Multi-spin echo T2 relaxation imaging with compressed sensing (METRICS) for rapid myelin water imaging

PURPOSE

Myelin water imaging (MWI) provides a valuable biomarker for myelin, but clinical application has been restricted by long acquisition times. Accelerating the standard multi-echo T₂ acquisition with gradient echoes (GRASE) or by 2D multi-slice data collection results in image blurring, contrast changes, and other issues. Compressed sensing (CS) can vastly accelerate conventional MRI. In this work, we assessed the use of CS for in vivo human MWI, using a 3D multi spin-echo sequence.

METHODS

We implemented multi-echo T₂ relaxation imaging with compressed sensing (METRICS) and METRICS with partial Fourier acceleration (METRICS-PF). Scan-rescan data were acquired from 12 healthy controls for assessment of repeatability. MWI data were acquired for METRICS in 9 m:58 s and for METRICS-PF in 7 m:25 s, both with 1.5 × 2 × 3 mm³ voxels, 56 echoes, 7 ms ΔTE, and 240 × 240 × 170 mm³ FOV. METRICS was compared with a novel multi-echo spin-echo gold-standard (MSE-GS) MWI acquisition, acquired for a single additional subject in 2 h:2 m:40 s.

RESULTS

METRICS/METRICS-PF myelin water fraction had mean: repeatability coefficient 1.5/1.1, coefficient of variation 6.2/4.5%, and intra-class correlation coefficient 0.79/0.84. Repeatability metrics comparing METRICS with METRICS-PF were similar, and both sequences agreed with reference values from literature. METRICS images and quantitative maps showed excellent qualitative agreement with those of MSE-GS.

CONCLUSION

METRICS and METRICS-PF provided highly repeatable MWI data without the inherent disadvantages of GRASE or 2D multi-slice acquisition. CS acceleration allows MWI data to be acquired rapidly with larger FOV, higher estimated SNR, more isotropic voxels and more echoes than with previous techniques. The approach introduced here generalizes to any multi-component T₂ mapping application.

MRI T2 and T1ρ relaxation in patients at risk for knee osteoarthritis: a systematic review and meta-analysis

Background

Magnetic resonance imaging (MRI) T2 and T1ρ relaxation are increasingly being proposed as imaging biomarkers potentially capable of detecting biochemical changes in articular cartilage before structural changes are evident. We aimed to: 1) summarize MRI methods of published studies investigating T2 and T1ρ relaxation time in participants at risk for but without radiographic knee OA; and 2) compare T2 and T1ρ relaxation between participants at-risk for knee OA and healthy controls.

Methods

We conducted a systematic review of studies reporting T2 and T1ρ relaxation data that included both participants at risk for knee OA and healthy controls. Participant characteristics, MRI methodology, and T1ρ and T2 relaxation data were extracted. Standardized mean differences (SMDs) were calculated within each study. Pooled effect sizes were then calculated for six commonly segmented knee compartments.

Results

55 articles met eligibility criteria. There was considerable variability between scanners, coils, software, scanning protocols, pulse sequences, and post-processing. Moderate risk of bias due to lack of blinding was common. Pooled effect sizes indicated participants at risk for knee OA had lengthened T2 relaxation time in all compartments (SMDs from 0.33 to 0.74; p < 0.01) and lengthened T1ρ relaxation time in the femoral compartments (SMD from 0.35 to 0.40; p < 0.001).

Conclusions

T2 and T1ρ relaxation distinguish participants at risk for knee OA from healthy controls. Greater standardization of MRI methods is both warranted and required for progress towards biomarker validation.

Electronic supplementary material

The online version of this article (10.1186/s12891-019-2547-7) contains supplementary material, which is available to authorized users.

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.

Maturation-Related Changes in T2 Relaxation Times of Cartilage and Meniscus of the Pediatric Knee Joint at 3 T

OBJECTIVE

The objective of our study was to use a T2 mapping sequence performed at 3 T to investigate changes in the composition and microstructure of the cartilage and menisci of the pediatric knee joint during maturation.

MATERIALS AND METHODS

This retrospective study was performed of MRI examinations of 76 pediatric knees without internal derangement in 72 subjects (29 boys [mean age, 12.5 years] and 43 girls [mean age, 13.0 years]) who were evaluated with a sagittal T2 mapping sequence. T2 relaxation time values were quantitatively measured in eight cartilage subregions and in the medial and lateral menisci. Wilcoxon rank sum and Kruskal-Wallis tests were used to analyze the relationship between cartilage and meniscus T2 relaxation time values and sex and skeletal maturation, respectively. A multivariate linear regression model was used to investigate the independent association between cartilage T2 relaxation time values and age, weight, and body mass index (BMI [weight in kilograms divided by the square of height in meters]).

RESULTS

There were no significant sex differences (p = 0.26-0.91) in T2 relaxation time values for cartilage or meniscus. T2 relaxation time values in each individual cartilage subregion significantly decreased (p < 0.001) with progressive maturation. T2 relaxation time values in the lateral meniscus significantly increased (p = 0.001) with maturation, whereas T2 relaxation time values in the medial meniscus did not significantly change (p = 0.82). There was a significant association (p < 0.001) between cartilage T2 relaxation time values and age independent of weight and BMI, but no significant association between cartilage T2 relaxation time values and weight (p = 0.06) and BMI (p = 0.20) independent of age.

CONCLUSION

Cartilage T2 relaxation time values significantly decreased in all cartilage subregions and meniscus T2 relaxation time values significantly increased in the lateral meniscus during maturation. These changes in T2 relaxation time values reflect age-related changes in tissue composition and microstructure.

Systematic review and meta-analysis of the reliability and discriminative validity of cartilage compositional MRI in knee osteoarthritis

OBJECTIVE

To assess reliability and discriminative validity of cartilage compositional magnetic resonance imaging (MRI) in knee osteoarthritis (OA).

DESIGN

The study was carried out per PRISMA recommendations. We searched MEDLINE and EMBASE (1974 - present) for eligible studies. We performed qualitative synthesis of reliability data. Where data from at least two discrimination studies were available, we estimated pooled standardized mean difference (SMD) between subjects with and without OA. Discrimination analyses compared controls and subjects with mild OA (Kellgren-Lawrence (KL) grade 1-2), severe OA (KL grade 3-4) and OA not otherwise specified (NOS) where not possible to stratify. We assessed quality of the evidence using Quality Appraisal of Diagnostic Reliability (QAREL) and Quality Assessment of Diagnostic Accuracy (QUADAS-2) tools.

RESULTS

Fifty-eight studies were included in the reliability analysis and 26 studies were included in the discrimination analysis, with data from a total of 2,007 knees. Intra-observer, inter-observer and test-retest reliability of compositional techniques were excellent with most intraclass correlation coefficients >0.8 and coefficients of variation <10%. T1rho and T2 relaxometry were significant discriminators between subjects with mild OA and controls, and between subjects with OA (NOS) and controls (P < 0.001). T1rho showed best discrimination for mild OA (SMD [95% CI] = 0.73 [0.40 to 1.06], P < 0.001) and OA (NOS) (0.60 [0.41 to 0.80], P < 0.001). Quality of evidence was moderate for both parts of the review.

CONCLUSIONS

Cartilage compositional MRI techniques are reliable and, in the case of T1rho and T2 relaxometry, can discriminate between subjects with OA and controls.

Cationic poly-l-lysine-encapsulated melanin nanoparticles as efficient photoacoustic agents targeting to glycosaminoglycans for the early diagnosis of articular cartilage degeneration in osteoarthritis

Cartilage degeneration is the hallmark of osteoarthritis (OA) and its early diagnosis is essential for effective cartilage repair. However, until now, there was still a lack of imaging modalities that can accurately detect and evaluate cartilage degeneration in its early stage. Herein, we introduce endogenous melanin nanoparticles (MNPs) encapsulated by poly-l-lysine (PLL) as positively charged contrast agents for the accurate photoacoustic (PA) imaging of cartilage degeneration through its strong electrostatic interaction with anionic glycosaminoglycans (GAGs) in the cartilage. PLL-MNPs presented high PA intensity, photostability and biocompatibility. In vitro PAI studies showed that PLL-MNPs with a zeta potential of +32.5 ± 9.3 mV had more cartilage uptake and longer retention time than anionic MNPs, and generated a positive relationship with the GAG content in the cartilage. After administration via intra-articular injection in living mouse models, PLL-MNPs exhibited about a two-fold stronger PA signal in a normal joint (with high GAG content) than an OA joint (with low GAG content). Furthermore, the obtained PAI results provided accurate information of the GAG content distribution in the OA knee joint. Consequently, by detecting and analyzing the changes of the GAG content in OA cartilage using PAI, we can clearly distinguish early OA from late OA and monitor the therapeutic efficacy in OA after drug treatment. All PAI results were examined histologically.

A forgotten retained drain inside a knee for 10 years: A case report

INTRODUCTION

Surgical drains are inserted into the wound after an arthroscopic knee procedure mainly to decrease fluid collection after the operation. The use of postoperative surgical drains remains controversial.

CASE PRESENTATION

This report presents a rare case of a forgotten retained drain that was accidentally found inside a knee 10 years after an arthroscopic procedure. The drain was removed without any complications.

DISCUSSION

A retained and broken drain during removal is a very rare and preventable complication that can be stressful for both the patient and surgeon. Most of the literature supports that retained drains in the soft tissues do not affect long-term outcomes, but if the drain fragment is in the intra-articular area, it might cause complications. Furthermore, there are several preventive measures to avoid retained surgical drains.

CONCLUSION

By reporting this case of a forgotten drain retained inside a knee for approximately 10 years, we aim to illustrate the potential risk of leaving a drain inside the joint following an arthroscopic procedure. Furthermore, we advise that surgeons maintain a high index of suspicion for iatrogenic complications when a patient continues to complain about unexplained pain at the surgical site.

Clinical high-resolution mapping of the proteoglycan-bound water fraction in articular cartilage of the human knee joint

PURPOSE

We applied our recently introduced Bayesian analytic method to achieve clinically-feasible in-vivo mapping of the proteoglycan water fraction (PgWF) of human knee cartilage with improved spatial resolution and stability as compared to existing methods.

MATERIALS AND METHODS

Multicomponent driven equilibrium single-pulse observation of T₁ and T₂ (mcDESPOT) datasets were acquired from the knees of two healthy young subjects and one older subject with previous knee injury. Each dataset was processed using Bayesian Monte Carlo (BMC) analysis incorporating a two-component tissue model. We assessed the performance and reproducibility of BMC and of the conventional analysis of stochastic region contraction (SRC) in the estimation of PgWF. Stability of the BMC analysis of PgWF was tested by comparing independent high-resolution (HR) datasets from each of the two young subjects.

RESULTS

Unlike SRC, the BMC-derived maps from the two HR datasets were essentially identical. Furthermore, SRC maps showed substantial random variation in estimated PgWF, and mean values that differed from those obtained using BMC. In addition, PgWF maps derived from conventional low-resolution (LR) datasets exhibited partial volume and magnetic susceptibility effects. These artifacts were absent in HR PgWF images. Finally, our analysis showed regional variation in PgWF estimates, and substantially higher values in the younger subjects as compared to the older subject.

CONCLUSIONS

BMC-mcDESPOT permits HR in-vivo mapping of PgWF in human knee cartilage in a clinically-feasible acquisition time. HR mapping reduces the impact of partial volume and magnetic susceptibility artifacts compared to LR mapping. Finally, BMC-mcDESPOT demonstrated excellent reproducibility in the determination of PgWF.

Biexponential T2 relaxation estimation of human knee cartilage in vivo at 3T

PURPOSE

To evaluate biexponential T₂ relaxation mapping of human knee cartilage in vivo in clinically feasible scan times.

MATERIALS AND METHODS

T₂ -weighted magnetic resonance (MR) images were acquired from eight healthy volunteers using a standard 3T clinical scanner. A 3D Turbo-Flash sequence was modified to enable T₂ -weighted imaging with different echo times. Series of T₂ -weighted images were fitted using mono- and biexponential models with two- and four-parametric nonlinear approaches, respectively.

RESULTS

Biexponential relaxation of T₂ was detected in the knee cartilage in five regions of interest in all eight healthy volunteers. Short/long relaxation components of T₂ were estimated to be 8.27 ± 0.68 / 45.35 ± 3.79 msec with corresponding fractions of 41.3 ± 1.1% / 58.6 ± 4.6%, respectively. The monoexponential relaxation of T₂ was measured to be 26.9 ± 2.27 msec. The experiments showed good repeatability with coefficient of variation root mean square (CV_rms ) < 18% in all regions. The only difference in gender was observed in medial tibial cartilage, where the biexponential T₂ in female volunteers was significantly higher compared to male volunteers (P = 0.014). Significant differences were observed in T₂ relaxation between different regions on interest.

CONCLUSION

Biexponential relaxation of T₂ was observed in the human knee cartilage in vivo. The short and long components are thought to be related to the tightly bound and loosely bound macromolecular water compartments. These preliminary results of biexponential T₂ analysis could potentially be used to increase the specificity for detection of early osteoarthritis by measuring different water compartments and their fractions.

LEVEL OF EVIDENCE

1 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;47:809-819.

Morphologic and quantitative magnetic resonance imaging of knee articular cartilage for the assessment of post-traumatic osteoarthritis

Orthopedic trauma, such as anterior cruciate ligament (ACL) disruption, is a common source of osteoarthritis in the knee. Magnetic resonance imaging (MRI) is a non-invasive multi-planar imaging modality commonly used to evaluate hard and soft tissues of diarthrodial joints following traumatic injury. The contrast provided by generated images enables the evaluation of bone marrow lesions as well as delamination and degeneration of articular cartilage. We will provide background information about MRI signal generation and decay (T₁ and T₂ values), the utility of morphologic MRI, and the quantitative MRI techniques of T_1ρ , T₂ , and T₂ * mapping, to evaluate subjects with traumatic knee injuries, such as ACL rupture. Additionally, we will provide information regarding the dGEMRIC, sodium, and gagCEST imaging techniques. Finally, the description and utility of newer post hoc analysis techniques, such as texture analysis, will be given. Continued development and refinement of these advanced MRI techniques will facilitate their clinical translation. © 2016 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 35:412-423, 2017.

MR Parametric Mapping as a Biomarker of Early Joint Degeneration

Context:

Osteoarthritis (OA) is a common, worldwide disorder. Magnetic resonance (MR) imaging can directly and noninvasively evaluate articular cartilage and has emerged as an essential tool in the study of OA.

Evidence Acquisition:

A PubMed search was performed using the keywords quantitative MRI and cartilage. No limits were set on the range of years searched. Articles were reviewed for relevance with an emphasis on in vivo studies performed at 3 tesla.

Study Design:

Clinical review.

Level of Evidence:

Level 4.

Results:

T2, T2*, T1 (particularly when measured after exogenous contrast administration, such as with the delayed gadolinium-enhanced MR imaging of cartilage [dGEMRIC] technique), and T1ρ are among the most widely utilized quantitative MR imaging techniques to evaluate cartilage and have been implemented in various patient cohorts. Existing challenges include reproducibility of results, insufficient consensus regarding optimal sequences and parameters, and interpretation of values.

Conclusion:

Quantitative assessment of cartilage using MR imaging techniques likely represents the best opportunity to identify early cartilage degeneration and to follow patients after treatment. Despite existing challenges, ongoing work and unique approaches have shown exciting and promising results.

Capturing fast relaxing spins with SWIFT adiabatic rotating frame spin-lattice relaxation (T1ρ) mapping

Rotating frame spin-lattice relaxation, with the characteristic time constant T1ρ, provides a means to access motion-restricted (slow) spin dynamics in MRI. As a result of their restricted motion, these spins are sometimes characterized by a short transverse relaxation time constant T2 and thus can be difficult to detect directly with conventional image acquisition techniques. Here, we introduce an approach for three-dimensional adiabatic T1ρ mapping based on a magnetization-prepared sweep imaging with Fourier transformation (MP-SWIFT) sequence, which captures signal from almost all water spin populations, including the extremely fast relaxing pool. A semi-analytical procedure for T1ρ mapping is described. Experiments on phantoms and musculoskeletal tissue specimens (tendon, articular and epiphyseal cartilages) were performed at 9.4 T for both the MP-SWIFT and fast spin echo (FSE) read outs. In the phantom with liquids having fast molecular tumbling and a single-valued T1ρ time constant, the measured T1ρ values obtained with MP-SWIFT and FSE were similar. Conversely, in normal musculoskeletal tissues, T1ρ values measured with MP-SWIFT were much shorter than the values obtained with FSE. Studies of biological tissue specimens demonstrated that T1ρ-weighted SWIFT provides higher contrast between normal and diseased tissues relative to conventional acquisitions. Adiabatic T1ρ mapping with SWIFT readout captures contributions from the otherwise undetected fast relaxing spins, allowing more informative T1ρ measurements of normal and diseased states.

Improved determination of the myelin water fraction in human brain using magnetic resonance imaging through Bayesian analysis of mcDESPOT

Myelin water fraction (MWF) mapping with magnetic resonance imaging has led to the ability to directly observe myelination and demyelination in both the developing brain and in disease. Multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) has been proposed as a rapid approach for multicomponent relaxometry and has been applied to map MWF in the human brain. However, even for the simplest two-pool signal model consisting of myelin-associated and non-myelin-associated water, the dimensionality of the parameter space for obtaining MWF estimates remains high. This renders parameter estimation difficult, especially at low-to-moderate signal-to-noise ratios (SNRs), due to the presence of local minima and the flatness of the fit residual energy surface used for parameter determination using conventional nonlinear least squares (NLLS)-based algorithms. In this study, we introduce three Bayesian approaches for analysis of the mcDESPOT signal model to determine MWF. Given the high-dimensional nature of the mcDESPOT signal model, and, therefore the high-dimensional marginalizations over nuisance parameters needed to derive the posterior probability distribution of the MWF, the Bayesian analyses introduced here use different approaches to reduce the dimensionality of the parameter space. The first approach uses normalization by average signal amplitude, and assumes that noise can be accurately estimated from signal-free regions of the image. The second approach likewise uses average amplitude normalization, but incorporates a full treatment of noise as an unknown variable through marginalization. The third approach does not use amplitude normalization and incorporates marginalization over both noise and signal amplitude. Through extensive Monte Carlo numerical simulations and analysis of in vivo human brain datasets exhibiting a range of SNR and spatial resolution, we demonstrated markedly improved accuracy and precision in the estimation of MWF using these Bayesian methods as compared to the stochastic region contraction (SRC) implementation of NLLS.

Incorporation of nonzero echo times in the SPGR and bSSFP signal models used in mcDESPOT

PURPOSE

To analyze the effect of neglecting nonzero echo times (TEs) in the conventional model of multicomponent driven equilibrium single pulse observation of T1 and T2 (mcDESPOT).

THEORY AND METHODS

Formulations of the two-component spoiled gradient recalled echo (SPGR) and balanced steady state free precession (bSSFP) models that incorporate nonzero TE effects are presented in the context of mcDESPOT and compared with the conventionally used SPGR and bSSFP models which ignore nonzero TEs. Relative errors in derived parameter estimates from conventional mcDESPOT, omitting TE effects, are assessed using simulations over a wide range of experimental and sample parameters.

RESULTS

The neglect of nonzero TE leads to an overestimate of the SPGR signal and an underestimate of the bSSFP signal. These effects can introduce large errors in parameter estimates derived from conventional mcDESPOT under realistic imaging conditions.

CONCLUSION

SPGR and bSSFP signal models accounting for nonzero TE effects should be incorporated into quantitative mcDESPOT analyses.