Reproducibility of Quantitative Double-Echo Steady-State T2 Mapping of Knee Cartilage.

BACKGROUND: Cartilage T2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T2 is essential to profiling the technical performance of this biomarker. PURPOSE: To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T2 using two different regional analysis schemes: 1) manual segmentation of subregions loaded during common activities and 2) automatic subregional segmentation. STUDY TYPE: Prospective. SUBJECTS: 11 uninjured participants (age: 28 ± 3 years; 8 (73%) female). FIELD STRENGTH/SEQUENCE: 3-T, qDESS. ASSESSMENT: Test-retest T2 maps were acquired twice on the same day and with a 1-week interval between scans. For each acquisition, average cartilage T2 was calculated in four manually segmented regions encompassing tibiofemoral contact areas during common activities and 12 automatically segmented regions from the deep-learning open-source framework for musculoskeletal MRI analysis (DOSMA) encompassing medial and lateral anterior, central, and posterior tibiofemoral regions. Test-retest T2 values from matching regions were used to evaluate reproducibility. STATISTICAL TESTS: Coefficients of variation (%CV), root-mean-square-average-CV (%RMSA-CV), and intraclass correlation coefficients (ICCs) assessed test-retest T2 reproducibility. The median of test-retest standard deviations was used for T2 precision. Bland-Altman (BA) analyses examined test-retest biases. The smallest detectable difference (SDD) was defined as the BA limit of agreement of largest magnitude. Significance was accepted for P < 0.05. RESULTS: All cartilage regions across both segmentation schemes demonstrated intraday and interday qDESS T2 CVs and RMSA-CVs of ≤5%. T2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test-retest T2 precision was <1.3 msec. The T2 SDD was 3.8 msec. DATA CONCLUSION: Excellent CV and RMSA-CV reproducibility may suggest that qDESS T2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition. TECHNICAL EFFICACY: Stage 2.

ShapeMed-Knee: A Dataset and Neural Shape Model Benchmark for Modeling 3D Femurs.

Analyzing anatomic shapes of tissues and organs is pivotal for accurate disease diagnostics and clinical decision-making. One prominent disease that depends on anatomic shape analysis is osteoarthritis, which affects 30 million Americans. To advance osteoarthritis diagnostics and prognostics, we introduce ShapeMed-Knee, a 3D shape dataset with 9,376 high-resolution, medical-imaging-based 3D shapes of both femur bone and cartilage. Besides data, ShapeMed-Knee includes two benchmarks for assessing reconstruction accuracy and five clinical prediction tasks that assess the utility of learned shape representations. Leveraging ShapeMed-Knee, we develop and evaluate a novel hybrid explicit-implicit neural shape model which achieves up to 40% better reconstruction accuracy than a statistical shape model and implicit neural shape model. Our hybrid models achieve state-of-the-art performance for preserving cartilage biomarkers; they're also the first models to successfully predict localized structural features of osteoarthritis, outperforming shape models and convolutional neural networks applied to raw magnetic resonance images and segmentations. The ShapeMed-Knee dataset provides medical evaluations to reconstruct multiple anatomic surfaces and embed meaningful disease-specific information. ShapeMed-Knee reduces barriers to applying 3D modeling in medicine, and our benchmarks highlight that advancements in 3D modeling can enhance the diagnosis and risk stratification for complex diseases. The dataset, code, and benchmarks will be made freely accessible.

A deep learning approach for fast muscle water T2 mapping with subject specific fat T2 calibration from multi-spin-echo acquisitions.

This work presents a deep learning approach for rapid and accurate muscle water T2 with subject-specific fat T2 calibration using multi-spin-echo acquisitions. This method addresses the computational limitations of conventional bi-component Extended Phase Graph fitting methods (nonlinear-least-squares and dictionary-based) by leveraging fully connected neural networks for fast processing with minimal computational resources. We validated the approach through in vivo experiments using two different MRI vendors. The results showed strong agreement of our deep learning approach with reference methods, summarized by Lin's concordance correlation coefficients ranging from 0.89 to 0.97. Further, the deep learning method achieved a significant computational time improvement, processing data 116 and 33 times faster than the nonlinear least squares and dictionary methods, respectively. In conclusion, the proposed approach demonstrated significant time and resource efficiency improvements over conventional methods while maintaining similar accuracy. This methodology makes the processing of water T2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T2 map of muscle in clinical and research studies.

Multimodal positron emission tomography (PET) imaging in non-oncologic musculoskeletal radiology.

Musculoskeletal (MSK) disorders are associated with large impacts on patient's pain and quality of life. Conventional morphological imaging of tissue structure is limited in its ability to detect pain generators, early MSK disease, and rapidly assess treatment efficacy. Positron emission tomography (PET), which offers unique capabilities to evaluate molecular and metabolic processes, can provide novel information about early pathophysiologic changes that occur before structural or even microstructural changes can be detected. This sensitivity not only makes it a powerful tool for detection and characterization of disease, but also a tool able to rapidly assess the efficacy of therapies. These benefits have garnered more attention to PET imaging of MSK disorders in recent years. In this narrative review, we discuss several applications of multimodal PET imaging in non-oncologic MSK diseases including arthritis, osteoporosis, and sources of pain and inflammation. We also describe technical considerations and recent advancements in technology and radiotracers as well as areas of emerging interest for future applications of multimodal PET imaging of MSK conditions. Overall, we present evidence that the incorporation of PET through multimodal imaging offers an exciting addition to the field of MSK radiology and will likely prove valuable in the transition to an era of precision medicine.

Changes in tissue sodium concentration and sodium relaxation times during the maturation of human knee cartilage: Ex vivo 23 Na MRI study at 10.5 T.

PURPOSE: To evaluate the influence of skeletal maturation on sodium (23 Na) MRI relaxation parameters and the accuracy of tissue sodium concentration (TSC) quantification in human knee cartilage. METHODS: Twelve pediatric knee specimens were imaged with whole-body 10.5 T MRI using a density-adapted 3D radial projection sequence to evaluate 23 Na parameters: B1 + , T1 , biexponential T 2 * $$ {\mathrm{T}}_2^{\ast } $$ , and TSC. Water, collagen, and sulfated glycosaminoglycan (sGAG) content were calculated from osteochondral biopsies. The TSC was corrected for B1 + , relaxation, and water content. The literature-based TSC (TSCLB ) used previously published values for corrections, whereas the specimen-specific TSC (TSCSP ) used measurements from individual specimens. 23 Na parameters were evaluated in eight cartilage compartments segmented on proton images. Associations between 23 Na parameters, TSCLB - TSCSP difference, biochemical content, and age were determined. RESULTS: From birth to 12 years, cartilage water content decreased by 18%; collagen increased by 59%; and sGAG decreased by 36% (all R2 ≥ 0.557). The short T 2 * $$ {\mathrm{T}}_2^{\ast } $$ ( T 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ ) decreased by 72%, and the signal fraction relaxing with T 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ ( fT 2 * S $$ {{\mathrm{fT}}_2^{\ast}}_{\mathrm{S}} $$ ) increased by 55% during the first 5 years but remained relatively stable after that. TSCSP was significantly correlated with sGAG content from biopsies (R2 = 0.739). Depending on age, TSCLB showed higher or lower values than TSCSP . The TSCLB - TSCSP difference was significantly correlated with T 2 * S $$ {{\mathrm{T}}_2^{\ast}}_{\mathrm{S}} $$ (R2 = 0.850), fT 2 * S $$ {{\mathrm{fT}}_2^{\ast}}_{\mathrm{S}} $$ (R2 = 0.651), and water content (R2 = 0.738). CONCLUSION: TSC and relaxation parameters measured with 23 Na MRI provide noninvasive information about changes in sGAG content and collagen matrix during cartilage maturation. Cartilage TSC quantification assuming fixed relaxation may be feasible in children older than 5 years.

Multiparametric MRI of Knees in Collegiate Basketball Players: Associations With Morphological Abnormalities and Functional Deficits.

Background: Rates of cartilage degeneration in asymptomatic elite basketball players are significantly higher compared with the general population due to excessive loads on the knee. Compositional quantitative magnetic resonance imaging (qMRI) techniques can identify local biochemical changes of macromolecules observed in cartilage degeneration. Purpose/Hypothesis: The purpose of this study was to utilize multiparametric qMRI to (1) quantify how T1ρ and T2 relaxation times differ based on the presence of anatomic abnormalities and (2) correlate T1ρ and T2 with self-reported functional deficits. It was hypothesized that prolonged relaxation times will be associated with knees with MRI-graded abnormalities and knees belonging to basketball players with greater self-reported functional deficits. Study Design: Cross-sectional study; Level of evidence, 3. Methods: A total of 75 knees from National Collegiate Athletic Association Division I basketball players (40 female, 35 male) were included in this multicenter study. All players completed the Knee injury and Osteoarthritis Outcome Score (KOOS) and had bilateral knee MRI scans taken. T1ρ and T2 were calculated on a voxel-by-voxel basis. The cartilage surfaces were segmented into 6 compartments: lateral femoral condyle, lateral tibia, medial femoral condyle, medial tibia (MT), patella (PAT), and trochlea (TRO). Lesions from the MRI scans were graded for imaging abnormalities, and statistical parametric mapping was performed to study cross-sectional differences based on MRI scan grading of anatomic knee abnormalities. Pearson partial correlations between relaxation times and KOOS subscore values were computed, obtaining r value statistical parametric mappings and P value clusters. Results: Knees without patellar tendinosis displayed significantly higher T1ρ in the PAT compared with those with patellar tendinosis (average percentage difference, 10.4%; P = .02). Significant prolongation of T1ρ was observed in the MT, TRO, and PAT of knees without compared with those with quadriceps tendinosis (average percentage difference, 12.7%, 13.3%, and 13.4%, respectively; P ≤ .05). A weak correlation was found between the KOOS-Symptoms subscale values and T1ρ/T2. Conclusion: Certain tissues that bear the brunt of impact developed tendinosis but spared cartilage degeneration. Whereas participants reported minimal functional deficits, their high-impact activities resulted in structural damage that may lead to osteoarthritis after their collegiate careers.

Accelerated Musculoskeletal Magnetic Resonance Imaging.

With a substantial growth in the use of musculoskeletal MRI, there has been a growing need to improve MRI workflow, and faster imaging has been suggested as one of the solutions for a more efficient examination process. Consequently, there have been considerable advances in accelerated MRI scanning methods. This article aims to review the basic principles and applications of accelerated musculoskeletal MRI techniques including widely used conventional acceleration methods, more advanced deep learning-based techniques, and new approaches to reduce scan time. Specifically, conventional accelerated MRI techniques, including parallel imaging, compressed sensing, and simultaneous multislice imaging, and deep learning-based accelerated MRI techniques, including undersampled MR image reconstruction, super-resolution imaging, artifact correction, and generation of unacquired contrast images, are discussed. Finally, new approaches to reduce scan time, including synthetic MRI, novel sequences, and new coil setups and designs, are also reviewed. We believe that a deep understanding of these fast MRI techniques and proper use of combined acceleration methods will synergistically improve scan time and MRI workflow in daily practice. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 1.

Advanced MRI Approaches for Evaluating Common Lower Extremity Injuries in Basketball Players: Current and Emerging Techniques.

Magnetic resonance imaging (MRI) can provide accurate and non-invasive diagnoses of lower extremity injuries in athletes. Sport-related injuries commonly occur in and around the knee and can affect the articular cartilage, patellar tendon, hamstring muscles, and bone. Sports medicine physicians utilize MRI to evaluate and diagnose injury, track recovery, estimate return to sport timelines, and assess the risk of recurrent injury. This article reviews the current literature and describes novel developments of quantitative MRI tools that can further advance our understanding of sports injury diagnosis, prevention, and treatment while minimizing injury risk and rehabilitation time. Innovative approaches for enhancing the early diagnosis and treatment of musculoskeletal injuries in basketball players span a spectrum of techniques. These encompass the utilization of T2 , T1ρ , and T2 * quantitative MRI, along with dGEMRIC and Na-MRI to assess articular cartilage injuries, 3D-Ultrashort echo time MRI for patellar tendon injuries, diffusion tensor imaging for acute myotendinous injuries, and sagittal short tau inversion recovery and axial long-axis T1 -weighted, and 3D Cube sequences for bone stress imaging. Future studies should further refine and validate these MR-based quantitative techniques while exploring the lifelong cumulative impact of basketball on players' knees. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.

Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.

We systematically evaluate the training methodology and efficacy of two inpainting-based pretext tasks of context prediction and context restoration for medical image segmentation using self-supervised learning (SSL). Multiple versions of self-supervised U-Net models were trained to segment MRI and CT datasets, each using a different combination of design choices and pretext tasks to determine the effect of these design choices on segmentation performance. The optimal design choices were used to train SSL models that were then compared with baseline supervised models for computing clinically-relevant metrics in label-limited scenarios. We observed that SSL pretraining with context restoration using 32 × 32 patches and Poission-disc sampling, transferring only the pretrained encoder weights, and fine-tuning immediately with an initial learning rate of 1 × 10-3 provided the most benefit over supervised learning for MRI and CT tissue segmentation accuracy (p < 0.001). For both datasets and most label-limited scenarios, scaling the size of unlabeled pretraining data resulted in improved segmentation performance. SSL models pretrained with this amount of data outperformed baseline supervised models in the computation of clinically-relevant metrics, especially when the performance of supervised learning was low. Our results demonstrate that SSL pretraining using inpainting-based pretext tasks can help increase the robustness of models in label-limited scenarios and reduce worst-case errors that occur with supervised learning.

Generalizability of Deep Learning Segmentation Algorithms for Automated Assessment of Cartilage Morphology and MRI Relaxometry.

BACKGROUND: Deep learning (DL)-based automatic segmentation models can expedite manual segmentation yet require resource-intensive fine-tuning before deployment on new datasets. The generalizability of DL methods to new datasets without fine-tuning is not well characterized. PURPOSE: Evaluate the generalizability of DL-based models by deploying pretrained models on independent datasets varying by MR scanner, acquisition parameters, and subject population. STUDY TYPE: Retrospective based on prospectively acquired data. POPULATION: Overall test dataset: 59 subjects (26 females); Study 1: 5 healthy subjects (zero females), Study 2: 8 healthy subjects (eight females), Study 3: 10 subjects with osteoarthritis (eight females), Study 4: 36 subjects with various knee pathology (10 females). FIELD STRENGTH/SEQUENCE: A 3-T, quantitative double-echo steady state (qDESS). ASSESSMENT: Four annotators manually segmented knee cartilage. Each reader segmented one of four qDESS datasets in the test dataset. Two DL models, one trained on qDESS data and another on Osteoarthritis Initiative (OAI)-DESS data, were assessed. Manual and automatic segmentations were compared by quantifying variations in segmentation accuracy, volume, and T2 relaxation times for superficial and deep cartilage. STATISTICAL TESTS: Dice similarity coefficient (DSC) for segmentation accuracy. Lin's concordance correlation coefficient (CCC), Wilcoxon rank-sum tests, root-mean-squared error-coefficient-of-variation to quantify manual vs. automatic T2 and volume variations. Bland-Altman plots for manual vs. automatic T2 agreement. A P value < 0.05 was considered statistically significant. RESULTS: DSCs for the qDESS-trained model, 0.79-0.93, were higher than those for the OAI-DESS-trained model, 0.59-0.79. T2 and volume CCCs for the qDESS-trained model, 0.75-0.98 and 0.47-0.95, were higher than respective CCCs for the OAI-DESS-trained model, 0.35-0.90 and 0.13-0.84. Bland-Altman 95% limits of agreement for superficial and deep cartilage T2 were lower for the qDESS-trained model, ±2.4 msec and ±4.0 msec, than the OAI-DESS-trained model, ±4.4 msec and ±5.2 msec. DATA CONCLUSION: The qDESS-trained model may generalize well to independent qDESS datasets regardless of MR scanner, acquisition parameters, and subject population. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

A method for measuring B0 field inhomogeneity using quantitative double-echo in steady-state.

PURPOSE: To develop and validate a method for B 0 $$ {B}_0 $$ mapping for knee imaging using the quantitative Double-Echo in Steady-State (qDESS) exploiting the phase difference ( Δ Î¸ $$ \Delta \theta $$ ) between the two echoes acquired. Contrary to a two-gradient-echo (2-GRE) method, Δ Î¸ $$ \Delta \theta $$ depends only on the first echo time. METHODS: Bloch simulations were applied to investigate robustness to noise of the proposed methodology and all imaging studies were validated with phantoms and in vivo simultaneous bilateral knee acquisitions. Two phantoms and five healthy subjects were scanned using qDESS, water saturation shift referencing (WASSR), and multi-GRE sequences. Δ B 0 $$ \Delta {B}_0 $$ maps were calculated with the qDESS and the 2-GRE methods and compared against those obtained with WASSR. The comparison was quantitatively assessed exploiting pixel-wise difference maps, Bland-Altman (BA) analysis, and Lin's concordance coefficient ( ρ c $$ {\rho}_c $$ ). For in vivo subjects, the comparison was assessed in cartilage using average values in six subregions. RESULTS: The proposed method for measuring Δ B 0 $$ \Delta {B}_0 $$ inhomogeneities from a qDESS acquisition provided Δ B 0 $$ \Delta {B}_0 $$ maps that were in good agreement with those obtained using WASSR. Δ B 0 $$ \Delta {B}_0 $$ ρ c $$ {\rho}_c $$ values were ≥ $$ \ge $$ 0.98 and 0.90 in phantoms and in vivo, respectively. The agreement between qDESS and WASSR was comparable to that of a 2-GRE method. CONCLUSION: The proposed method may allow B0 correction for qDESS T 2 $$ {T}_2 $$ mapping using an inherently co-registered Δ B 0 $$ \Delta {B}_0 $$ map without requiring an additional B0 measurement sequence. More generally, the method may help shorten knee imaging protocols that require an auxiliary Δ B 0 $$ \Delta {B}_0 $$ map by exploiting a qDESS acquisition that also provides T 2 $$ {T}_2 $$ measurements and high-quality morphological imaging.

Personalization improves the biomechanical efficacy of foot progression angle modifications in individuals with medial knee osteoarthritis.

Modifying the foot progression angle during walking can reduce the knee adduction moment, a surrogate measure of medial knee loading. However, not all individuals reduce their knee adduction moment with the same modification. This study evaluates whether a personalized approach to prescribing foot progression angle modifications increases the proportion of individuals with medial knee osteoarthritis who reduce their knee adduction moment, compared to a non-personalized approach. Individuals with medial knee osteoarthritis (N=107) walked with biofeedback instructing them to toe-in and toe-out by 5° and 10° relative to their self-selected angle. We selected individuals' personalized foot progression angle as the modification that maximally reduced their larger knee adduction moment peak. Additionally, we used lasso regression to identify which secondary kinematic changes made a 10° toe-in gait modification more effective at reducing the first knee adduction moment peak. Seventy percent of individuals reduced their larger knee adduction moment peak by at least 5% with a personalized foot progression angle modification, which was more than (p≤0.002) the 23-57% of individuals who reduced it with a uniformly assigned 5° or 10° toe-in or toe-out modification. When toeing-in, greater reductions in the first knee adduction moment peak were related to an increased frontal-plane tibia angle (knee more medial than ankle), a more valgus knee abduction angle, reduced contralateral pelvic drop, and a more medialized center of pressure in the foot reference frame. In summary, personalization increases the proportion of individuals with medial knee osteoarthritis who may benefit from a foot progression angle modification.

Contrast solution properties and scan parameters influence the apparent diffusivity of computed tomography contrast agents in articular cartilage.

The inability to detect early degenerative changes to the articular cartilage surface that commonly precede bulk osteoarthritic degradation is an obstacle to early disease detection for research or clinical diagnosis. Leveraging a known artefact that blurs tissue boundaries in clinical arthrograms, contrast agent (CA) diffusivity can be derived from computed tomography arthrography (CTa) scans. We combined experimental and computational approaches to study protocol variations that may alter the CTa-derived apparent diffusivity. In experimental studies on bovine cartilage explants, we examined how CA dilution and transport direction (absorption versus desorption) influence the apparent diffusivity of untreated and enzymatically digested cartilage. Using multiphysics simulations, we examined mechanisms underlying experimental observations and the effects of image resolution, scan interval and early scan termination. The apparent diffusivity during absorption decreased with increasing CA concentration by an amount similar to the increase induced by tissue digestion. Models indicated that osmotically-induced fluid efflux strongly contributed to the concentration effect. Simulated changes to spatial resolution, scan spacing and total scan time all influenced the apparent diffusivity, indicating the importance of consistent protocols. With careful control of imaging protocols and interpretations guided by transport models, CTa-derived diffusivity offers promise as a biomarker for early degenerative changes.

Automatic estimation of knee effusion from limited MRI data.

Knee effusion is a common comorbidity in osteoarthritis. To quantify the amount of effusion, semi quantitative assessment scales have been developed that classify fluid levels on an integer scale from 0 to 3. In this work, we investigated the use of a neural network (NN) that used MRI Osteoarthritis Knee Scores effusion-synovitis (MOAKS-ES) values to distinguish physiologic fluid levels from higher fluid levels in MR images of the knee. We evaluate its effectiveness on low-resolution images to examine its potential in low-field, low-cost MRI. We created a dense NN (dNN) for detecting effusion, defined as a nonzero MOAKS-ES score, from MRI scans. Both the training and performance evaluation of the network were conducted using public radiological data from the Osteoarthritis Initiative (OAI). The model was trained using sagittal turbo-spin-echo (TSE) MR images from 1628 knees. The accuracy was compared to VGG16, a commonly used convolutional classification network. Robustness of the dNN was assessed by adding zero-mean Gaussian noise to the test images with a standard deviation of 5-30% of the maximum test data intensity. Also, inference was performed on a test data set of 163 knees, which includes a smaller test set of 36 knees that was also assessed by a musculoskeletal radiologist and the performance of the dNN and the radiologist compared. For the larger test data set, the dNN performed with an average accuracy of 62%. In addition, the network proved robust to noise, classifying the noisy images with minimal degradation to accuracy. When given MRI scans with 5% Gaussian noise, the network performed similarly, with an average accuracy of 61%. For the smaller 36-knee test data set, assessed both by the dNN and by a radiologist, the network performed better than the radiologist on average. Classifying knee effusion from low-resolution images with a similar accuracy as a human radiologist using neural networks is feasible, suggesting automatic assessment of images from low-cost, low-field scanners as a potentially useful assessment tool.

Validation of watershed-based segmentation of the cartilage surface from sequential CT arthrography scans.

BACKGROUND: This study investigated the utility of a 2-dimensional watershed algorithm for identifying the cartilage surface in computed tomography (CT) arthrograms of the knee up to 33 minutes after an intra-articular iohexol injection as boundary blurring increased. METHODS: A 2D watershed algorithm was applied to CT arthrograms of 3 bovine stifle joints taken 3, 8, 18, and 33 minutes after iohexol injection and used to segment tibial cartilage. Thickness measurements were compared to a reference standard thickness measurement and the 3-minute time point scan. RESULTS: 77.2% of cartilage thickness measurements were within 0.2 mm (1 voxel) of the thickness calculated in the reference scan at the 3-minute time point. 42% fewer voxels could be segmented from the 33-minute scan than the 3-minute scan due to diffusion of the contrast agent out of the joint space and into the cartilage, leading to blurring of the cartilage boundary. The traced watershed lines were closer to the location of the cartilage surface in areas where tissues were in direct contact with each other (cartilage-cartilage or cartilage-meniscus contact). CONCLUSIONS: The use of watershed dam lines to guide cartilage segmentation shows promise for identifying cartilage boundaries from CT arthrograms in areas where soft tissues are in direct contact with each other.

Imaging of Synovial Inflammation in Osteoarthritis, From the AJR Special Series on Inflammation.

Synovitis, inflammation of the synovial membrane, is a common manifestation of osteoarthritis (OA) and is recognized to play a role in the complex pathophysiology of OA. Increased recognition of the importance of synovitis in the OA disease process and its potential as a target for treatment has increased the need for noninvasive detection and characterization of synovitis using medical imaging. Numerous imaging methods can assess synovitis involvement in OA with varying sensitivity, specificity, and complexity. This article reviews the role of contrast-enhanced MRI, conventional MRI, novel unenhanced MRI, gray-scale ultrasound (US), and power Doppler US in the assessment of synovitis in patients with OA. The role of imaging in disease evaluation and the challenges of conventional imaging methods are discussed. We also provide an overview of the potential utility of emerging techniques for imaging of early inflammation and molecular inflammatory markers of synovitis, including quantitative MRI, superb microvascular imaging, and PET. The development of therapeutic treatments targeting inflammatory features, particularly in early OA, would greatly increase the importance of these imaging methods for clinical decision-making and evaluation of therapeutic efficacy.

Gadolinium-free assessment of synovitis using diffusion tensor imaging.

The dynamic contrast-enhanced (DCE)-MRI parameter Ktrans can quantify the intensity of synovial inflammation (synovitis) in knees with osteoarthritis (OA), but requires the use of gadolinium-based contrast agent (GBCA). Diffusion tensor imaging (DTI) measures the diffusion of water molecules with parameters mean diffusivity (MD) and fractional anisotropy (FA), and has been proposed as a method to detect synovial inflammation without the use of GBCA. The purpose of this study is to (1) determine the ability of DTI to quantify the intensity of synovitis in OA by comparing MD and FA with our imaging gold standard Ktrans within the synovium and (2) compare DTI and DCE-MRI measures with the semi-quantitative grading of OA severity with the Kellgren-Lawrence (KL) and MRI Osteoarthritis Knee Score (MOAKS) systems, in order to assess the relationship between synovitis intensity and OA severity. Within the synovium, MD showed a significant positive correlation with Ktrans (r = 0.79, p < 0.001), while FA showed a significant negative correlation with Ktrans (r = -0.72, p = 0.0026). These results show that DTI is able to quantify the intensity of synovitis within the whole synovium without the use of exogenous contrast agent. Additionally, MD, FA, and Ktrans values did not vary significantly when knees were separated by KL grade (p = 0.15, p = 0.32, p = 0.41, respectively), while MD (r = 0.60, p = 0.018) and Ktrans (r = 0.62, p = 0.013) had a significant positive correlation and FA (r = -0.53, p = 0.043) had a negative correlation with MOAKS. These comparisons indicate that quantitative measures of the intensity of synovitis may provide information in addition to morphological assessment to evaluate OA severity. Using DTI to quantify the intensity of synovitis without GBCA may be helpful to facilitate a broader clinical assessment of the severity of OA.

Synthesizing Quantitative T2 Maps in Right Lateral Knee Femoral Condyles from Multicontrast Anatomic Data with a Conditional Generative Adversarial Network.

PURPOSE: To develop a proof-of-concept convolutional neural network (CNN) to synthesize T2 maps in right lateral femoral condyle articular cartilage from anatomic MR images by using a conditional generative adversarial network (cGAN). MATERIALS AND METHODS: In this retrospective study, anatomic images (from turbo spin-echo and double-echo in steady-state scans) of the right knee of 4621 patients included in the 2004-2006 Osteoarthritis Initiative were used as input to a cGAN-based CNN, and a predicted CNN T2 was generated as output. These patients included men and women of all ethnicities, aged 45-79 years, with or at high risk for knee osteoarthritis incidence or progression who were recruited at four separate centers in the United States. These data were split into 3703 (80%) for training, 462 (10%) for validation, and 456 (10%) for testing. Linear regression analysis was performed between the multiecho spin-echo (MESE) and CNN T2 in the test dataset. A more detailed analysis was performed in 30 randomly selected patients by means of evaluation by two musculoskeletal radiologists and quantification of cartilage subregions. Radiologist assessments were compared by using two-sided t tests. RESULTS: The readers were moderately accurate in distinguishing CNN T2 from MESE T2, with one reader having random-chance categorization. CNN T2 values were correlated to the MESE values in the subregions of 30 patients and in the bulk analysis of all patients, with best-fit line slopes between 0.55 and 0.83. CONCLUSION: With use of a neural network-based cGAN approach, it is feasible to synthesize T2 maps in femoral cartilage from anatomic MRI sequences, giving good agreement with MESE scans.See also commentary by Yi and Fritz in this issue.Keywords: Cartilage Imaging, Knee, Experimental Investigations, Quantification, Vision, Application Domain, Convolutional Neural Network (CNN), Deep Learning Algorithms, Machine Learning Algorithms© RSNA, 2021.

Open Source Software for Automatic Subregional Assessment of Knee Cartilage Degradation Using Quantitative T2 Relaxometry and Deep Learning.

OBJECTIVE: We evaluated a fully automated femoral cartilage segmentation model for measuring T2 relaxation values and longitudinal changes using multi-echo spin-echo (MESE) magnetic resonance imaging (MRI). We open sourced this model and developed a web app available at https://kl.stanford.edu into which users can drag and drop images to segment them automatically. DESIGN: We trained a neural network to segment femoral cartilage from MESE MRIs. Cartilage was divided into 12 subregions along medial-lateral, superficial-deep, and anterior-central-posterior boundaries. Subregional T2 values and four-year changes were calculated using a radiologist's segmentations (Reader 1) and the model's segmentations. These were compared using 28 held-out images. A subset of 14 images were also evaluated by a second expert (Reader 2) for comparison. RESULTS: Model segmentations agreed with Reader 1 segmentations with a Dice score of 0.85 ± 0.03. The model's estimated T2 values for individual subregions agreed with those of Reader 1 with an average Spearman correlation of 0.89 and average mean absolute error (MAE) of 1.34 ms. The model's estimated four-year change in T2 for individual subregions agreed with Reader 1 with an average correlation of 0.80 and average MAE of 1.72 ms. The model agreed with Reader 1 at least as closely as Reader 2 agreed with Reader 1 in terms of Dice score (0.85 vs. 0.75) and subregional T2 values. CONCLUSIONS: Assessments of cartilage health using our fully automated segmentation model agreed with those of an expert as closely as experts agreed with one another. This has the potential to accelerate osteoarthritis research.