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OBJECTIVE:To investigate changes in bone metabolism by positron emission tomography (PET), as well as spatial relationships between bone metabolism and magnetic resonance imaging (MRI) quantitative markers of early cartilage degradation, in anterior cruciate ligament (ACL)-reconstructed knees. DESIGN:Both knees of 15 participants with unilateral reconstructed ACL tears and unaffected contralateral knees were scanned using a simultaneous 3.0T PET-MRI system following injection of 18F-sodium fluoride (18F-NaF). The maximum pixel standardized uptake value (SUVmax) in the subchondral bone and the average T2 relaxation time in cartilage were measured in each knee in eight knee compartments. We tested differences in SUVmax and cartilage T2 relaxation times between the ACL-injured knee and the contralateral control knee as well as spatial relationships between these bone and cartilage changes. RESULTS:Significantly increased subchondral bone 18F-NaF SUVmax and cartilage T2 times were observed in the ACL-reconstructed knees (median [inter-quartile-range (IQR)]: 5.0 [5.8], 36.8 [3.6] ms) compared to the contralateral knees (median [IQR]: 1.9 [1.4], 34.4 [3.8] ms). A spatial relationship between the two markers was also seen. Using the contralateral knee as a control, we observed a significant correlation of r = 0.59 between the difference in subchondral bone SUVmax (between injured and contralateral knees) and the adjacent cartilage T2 (between the two knees) [P

Imaging of hip in osteoarthritis (OA) has seen considerable progress in the past decade, with the introduction of new techniques that may be more sensitive to structural disease changes. The purpose of this expert opinion, consensus driven recommendation is to provide detail on how to apply hip imaging in disease modifying clinical trials. It includes information on acquisition methods/techniques (including guidance on positioning for radiography, sequence/protocol recommendations/hardware for magnetic resonance imaging (MRI)); commonly encountered problems (including positioning, hardware and coil failures, artifacts associated with various MRI sequences); quality assurance/control procedures; measurement methods; measurement performance (reliability, responsiveness, and validity); recommendations for trials; and research recommendations.

Purpose: To determine whether bone metabolic activity corresponds to bone and cartilage damage in patients with patellofemoral pain.Materials and Methods: We acquired magnetic resonance imaging (MRI) and (18) F-NaF positron emission tomography (PET) / computed tomography (CT) scans of the knees of 22 subjects. We compared locations of increased tracer uptake on the (18) F-NaF PET images to bone marrow edema and cartilage damage visualized on MRI.Results: We found that increased bone activity on (18) F-NaF PET does not always correspond to structural damage in the bone or cartilage as seen on MRI.Conclusion: Our results suggest that (18) F-NaF PET/CT may provide additional information in patellofemoral pain patients compared to MRI. [ABSTRACT FROM AUTHOR]

Objective: A relationship between T1ρ relaxation time and glycosaminoglycan (GAG) content has been demonstrated in chemically degraded bovine cartilage, but has not been demonstrated with quantitative biochemistry in human cartilage. A relationship has also been established between T2 relaxation time in cartilage and osteoarthritis (OA) severity. We hypothesized that T1ρ relaxation time would be associated with GAG content in human cartilage with normal T2 relaxation times.Methods: T2 relaxation time, T1ρ relaxation time, and glycosaminoglycan as a percentage of wet weight (sGAG) were measured for top and bottom regions at 7 anatomical locations in 21 human cadaver patellae. For our analysis, T2 relaxation time was classified as normal or elevated based on a threshold defined by the mean plus one standard deviation of the T2 relaxation time for all samples.Results: In the normal T2 relaxation time subset, T1ρ relaxation time correlated with sGAG content in the full-thickness and bottom regions, but only marginally in the top region alone. sGAG content decreased significantly with age in all regions.Conclusion: In the subset of cartilage specimens with normal T2 relaxation time, T1ρ relaxation time was inversely associated with sGAG content, as hypothesized. A predictive model, which accounts for T2 relaxation time and the effects of age, might be able to determine longitudinal trends in GAG content in the same person based on T1ρ relaxation time maps. [ABSTRACT FROM AUTHOR]

PURPOSE: Patellofemoral (PF) pain is common among athletes and may be caused by increased subchondral bone stress as a result of increased stress in the cartilage of the femur or patella. This article presents a modeling pipeline to estimate in vivo cartilage stress in the PF joint. METHODS: The modeling pipeline uses the finite element method to calculate stresses and strains in the PF joint cartilage. Model inputs include an accurate geometrical representation of the bones and cartilage from magnetic resonance imaging (MRI), cartilage material properties, and an estimate of muscle forces from an EMG-driven musculoskeletal model. Validation is performed using PF joint contact area and patellar orientation measured from upright, weight-bearing MRI. Preliminary data from an active, pain-free subject illustrate the modeling pipeline to calculate cartilage stress during a static squat. RESULTS: The quasistatic finite element simulation reproduced the orientation of the patella to within 2.1 mm and predicted the PF joint contact area to within 2.3%. Octahedral shear stresses were highest in the central, lateral aspect of the patella cartilage with a peak of 2.5 MPa. The corresponding stresses in the femoral cartilage reached only 2.0 MPa. However, peak hydrostatic pressures were higher within the femoral cartilage (3.5 MPa) than the patellar cartilage (2.3 MPa). CONCLUSION: The methods presented in this article offer a novel approach to calculate PF joint cartilage stress in vivo. Future efforts will use this modeling pipeline to further our knowledge of PF pain and potential rehabilitation strategies. [ABSTRACT FROM AUTHOR]

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., (© 2023 International Society for Magnetic Resonance in Medicine.)

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.

In computed tomography (CT) imaging, optimizing the balance between radiation dose and image quality is crucial due to the potentially harmful effects of radiation on patients. Although subjective assessments by radiologists are considered the gold standard in medical imaging, these evaluations can be time-consuming and costly. Thus, objective methods, such as the peak signal-to-noise ratio and structural similarity index measure, are often employed as alternatives. However, these metrics, initially developed for natural images, may not fully encapsulate the radiologists' assessment process. Consequently, interest in developing deep learning-based image quality assessment (IQA) methods that more closely align with radiologists' perceptions is growing. A significant barrier to this development has been the absence of open-source datasets and benchmark models specific to CT IQA. Addressing these challenges, we organized the Low-dose Computed Tomography Perceptual Image Quality Assessment Challenge in conjunction with the Medical Image Computing and Computer Assisted Intervention 2023. This event introduced the first open-source CT IQA dataset, consisting of 1,000 CT images of various quality, annotated with radiologists' assessment scores. As a benchmark, this challenge offers a comprehensive analysis of six submitted methods, providing valuable insight into their performance. This paper presents a summary of these methods and insights. This challenge underscores the potential for developing no-reference IQA methods that could exceed the capabilities of full-reference IQA methods, making a significant contribution to the research community with this novel dataset. The dataset is accessible at https://zenodo.org/records/7833096., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Jang-Hwan Choi reports financial support was provided by the National Research Foundation of Korea. Jang-Hwan Choi reports was provided by the Korea Medical Device Development Fund grant funded by the Korea government. Adrian Galdran reports financial support was provided by the Marie Sklodowska-Curie Fellowship. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024. Published by Elsevier B.V.)

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., (© 2024. The Author(s), under exclusive licence to International Skeletal Society (ISS).)

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 T 2 , T 1ρ , and T 2 * 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 T 1 -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., (© 2023 International Society for Magnetic Resonance in Medicine.)

Background: Cartilage T 2 can detect joints at risk of developing osteoarthritis. The quantitative double-echo steady state (qDESS) sequence is attractive for knee cartilage T 2 mapping because of its acquisition time of under 5 minutes. Understanding the reproducibility errors associated with qDESS T 2 is essential to profiling the technical performance of this biomarker., Purpose: To examine the combined acquisition and segmentation reproducibility of knee cartilage qDESS T 2 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 T 2 maps were acquired twice on the same day and with a 1-week interval between scans. For each acquisition, average cartilage T 2 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 T 2 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 T 2 reproducibility. The median of test-retest standard deviations was used for T 2 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 T 2 CVs and RMSA-CVs of ≤5%. T 2 ICC values >0.75 were observed in the majority of regions but were more variable in interday tibial comparisons. Test-retest T 2 precision was <1.3 msec. The T 2 SDD was 3.8 msec., Data Conclusion: Excellent CV and RMSA-CV reproducibility may suggest that qDESS T 2 increases or decreases >5% (3.8 msec) could represent changes to cartilage composition., Technical Efficacy: Stage 2., (© 2024 International Society for Magnetic Resonance in Medicine.)

This work presents a deep learning approach for rapid and accurate muscle water T 2 with subject-specific fat T 2 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 T 2 data faster and easier for the user and will facilitate the utilization of the use of a quantitative water T 2 map of muscle in clinical and research studies., (© 2024. The Author(s).)

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 T 1ρ and T 2 relaxation times differ based on the presence of anatomic abnormalities and (2) correlate T 1ρ and T 2 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. T 1ρ and T 2 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 T 1ρ in the PAT compared with those with patellar tendinosis (average percentage difference, 10.4%; P  = .02). Significant prolongation of T 1ρ 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 T 1ρ /T 2 ., 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., Competing Interests: One or more of the authors has declared the following potential conflict of interest or source of funding: Research support was received from the GE/NBA research consortium. M.F.K. has received consulting fees from EpiBone. G.E.G. has received consulting fees from Canon Medical Systems and Stryker, nonconsulting fees from GE Healthcare, royalties from Medtronic, and acquisitions from Medtronic. H.G.P. has received research support from GE Healthcare and Siemens Medical Solutions; consulting fees from Stryker, Arthrex, Cytex Therapeutics, NHL, and Ortho Regenerative Technologies; and nonconsulting fees from Arthrex and is an equity holder for Imagen technologies, a cofounder of Imagen Technologies, and a paid associate editor for Sports Health. S.M. has received institutional research support from GE Healthcare and Siemens Healthineers. AOSSM checks author disclosures against the Open Payments Database (OPD). AOSSM has not conducted an independent investigation on the OPD and disclaims any liability or responsibility relating thereto. Ethical approval for this study was obtained from the University of California - San Francisco (ref No. 351632)., (© The Author(s) 2023.)

Purpose: [Formula: see text] mapping is a powerful tool for studying osteoarthritis (OA) changes and bilateral imaging may be useful in investigating the role of between-knee asymmetry in OA onset and progression. The quantitative double-echo in steady-state (qDESS) can provide fast simultaneous bilateral knee [Formula: see text] and high-resolution morphometry for cartilage and meniscus. The qDESS uses an analytical signal model to compute [Formula: see text] relaxometry maps, which require knowledge of the flip angle (FA). In the presence of [Formula: see text] inhomogeneities, inconsistencies between the nominal and actual FA can affect the accuracy of [Formula: see text] measurements. We propose a pixel-wise [Formula: see text] correction method for qDESS [Formula: see text] mapping exploiting an auxiliary [Formula: see text] map to compute the actual FA used in the model., Methods: The technique was validated in a phantom and in vivo with simultaneous bilateral knee imaging. [Formula: see text] measurements of femoral cartilage (FC) of both knees of six healthy participants were repeated longitudinally to investigate the association between [Formula: see text] variation and [Formula: see text]., Results: The results showed that applying the [Formula: see text] correction mitigated [Formula: see text] variations that were driven by [Formula: see text] inhomogeneities. Specifically, [Formula: see text] left-right symmetry increased following the [Formula: see text] correction ([Formula: see text] = 0.74 > [Formula: see text] = 0.69). Without the [Formula: see text] correction, [Formula: see text] values showed a linear dependence with [Formula: see text]. The linear coefficient decreased using the [Formula: see text] correction (from 24.3 ± 1.6 ms to 4.1 ± 1.8) and the correlation was not statistically significant after the application of the Bonferroni correction (p value > 0.01)., Conclusion: The study showed that [Formula: see text] correction could mitigate variations driven by the sensitivity of the qDESS [Formula: see text] mapping method to [Formula: see text], therefore, increasing the sensitivity to detect real biological changes. The proposed method may improve the robustness of bilateral qDESS [Formula: see text] mapping, allowing for an accurate and more efficient evaluation of OA pathways and pathophysiology through longitudinal and cross-sectional studies., (© 2023. The Author(s), under exclusive licence to European Society for Magnetic Resonance in Medicine and Biology (ESMRMB).)

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., (© 2022 International Society for Magnetic Resonance in Medicine.)

Mandibular distraction osteogenesis (DO) is mediated by skeletal stem cells (SSCs) in mice, which enact bone regeneration via neural crest re-activation. As peripheral nerves are essential to progenitor function during development and in response to injury, we questioned if denervation impairs mandibular DO. C57Bl6 mice were divided into two groups: DO with a segmental defect in the inferior alveolar nerve (IAN) at the time of mandibular osteotomy ("DO Den") and DO with IAN intact ("DO Inn"). DO Den demonstrated significantly reduced histological and radiological osteogenesis relative to DO Inn. Denervation preceding DO results in reduced SSC amplification and osteogenic potential in mice. Single cell RNA sequencing analysis revealed that there was a predominance of innervated SSCs in clusters dominated by pathways related to bone formation. A rare human patient specimen was also analyzed and suggested that histological, radiological, and transcriptional alterations seen in mouse DO may be conserved in the setting of denervated human mandible distraction. Fibromodulin (FMOD) transcriptional and protein expression were reduced in denervated relative to innervated mouse and human mandible regenerate. Finally, when exogenous FMOD was added to DO-Den and DO-Inn SSCs undergoing in vitro osteogenic differentiation, the osteogenic potential of DO-Den SSCs was increased in comparison to control untreated DO-Den SSCs, modeling the superior osteogenic potential of DO-Inn SSCs., (© 2023. The Author(s).)

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.

Objective: Altered joint function is a hallmark of osteoarthritis (OA). Imaging techniques for joint function are limited, but [ 18 F]sodium fluoride (NaF) PET-MRI may assess the acute joint response to loading stresses. [ 18 F]NaF PET-MRI was used to study the acute joint response to exercise in OA knees, and compare relationships between regions of increased uptake after loading and structural OA progression two years later., Methods: In this prospective study, 10 participants with knee OA (59 ± 8 years; 8 female) were scanned twice consecutively using a PET-MR system and performed a one-legged squat exercise between scans. Changes in tracer uptake measures in 9 bone regions were compared between knees that did and did not exercise with a mixed-effects model. Areas of focally large changes in uptake between scans (ROI focal , ΔSUV max > 3) were identified and the presence of structural MRI features was noted. Five participants returned two years later to assess structural change on MRI., Results: There was a significant increase in [ 18 F]NaF uptake in OA exercised knees (SUV P < 0.001, K i P = 0.002, K 1 P < 0.001) that differed by bone region., Conclusion: There were regional differences in the acute bone metabolic response to exercise and areas of focally large changes in the metabolic bone response that might be representative of whole-joint dysfunction., (Copyright © 2022 Osteoarthritis Research Society International. Published by Elsevier Ltd. All rights reserved.)

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., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that influence the work reported in this paper., (Copyright © 2022 Elsevier Ltd. All rights reserved.)

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.

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.

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., (© 2022. The Author(s).)

The dynamic contrast-enhanced (DCE)-MRI parameter K trans 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 K trans 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 K trans (r = 0.79, p < 0.001), while FA showed a significant negative correlation with K trans (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 K trans 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 K trans (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., (© 2021 John Wiley & Sons, Ltd.)

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., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://dx.doi.org/10.21037/qims-20-1062). GEG serves as an unpaid editorial board member of Quantitative Imaging in Medicine and Surgery. MEH reports that this research was supported by grant # R01AR065248 from the NIAMS of the U.S. National Institutes of Health and a graduate student fellowship from Stanford Bio-X. MSB reports that Natural Sciences and Engineering Research Council of Canada (NSERC) graduate student fellowship received for duration of work conducted. GEG reports that this research was supported by grant # R01AR065248 from the NIAMS of the U.S. National Institutes of Health. MEL reports that this research was supported by grant # R01AR065248 from the NIAMS of the U.S. National Institutes of Health., (2022 Quantitative Imaging in Medicine and Surgery. All rights reserved.)

Objectives: To determine whether synovitis graded by radiologists using hybrid quantitative double-echo in steady-state (qDESS) images can be utilized as a non-contrast approach to assess synovitis in the knee, compared against the reference standard of contrast-enhanced MRI (CE-MRI)., Methods: Twenty-two knees (11 subjects) with moderate to severe osteoarthritis (OA) were scanned using CE-MRI, qDESS with a high diffusion weighting (qDESS High ), and qDESS with a low diffusion weighting (qDESS Low ). Four radiologists graded the overall impression of synovitis, their diagnostic confidence, and regional grading of synovitis severity at four sites (suprapatellar pouch, intercondylar notch, and medial and lateral peripatellar recesses) in the knee using a 4-point scale. Agreement between CE-MRI and qDESS, inter-rater agreement, and intra-rater agreement were assessed using a linearly weighted Gwet's AC2., Results: Good agreement was seen between CE-MRI and both qDESS Low (AC2 = 0.74) and qDESS High (AC2 = 0.66) for the overall impression of synovitis, but both qDESS sequences tended to underestimate the severity of synovitis compared to CE-MRI. Good inter-rater agreement was seen for both qDESS sequences (AC2 = 0.74 for qDESS Low , AC2 = 0.64 for qDESS High ), and good intra-rater agreement was seen for both sequences as well (qDESS Low AC2 = 0.78, qDESS High AC2 = 0.80). Diagnostic confidence was moderate to high for qDESS Low (mean = 2.36) and slightly less than moderate for qDESS High (mean = 1.86), compared to mostly high confidence for CE-MRI (mean = 2.73)., Conclusions: qDESS shows potential as an alternative MRI technique for assessing the severity of synovitis without the use of a gadolinium-based contrast agent., Key Points: The use of the quantitative double-echo in steady-state (qDESS) sequence for synovitis assessment does not require the use of a gadolinium-based contrast agent. Preliminary results found that low diffusion-weighted qDESS (qDESS Low ) shows good agreement to contrast-enhanced MRI for characterization of the severity of synovitis, with a relative bias towards underestimation of severity. Preliminary results also found that qDESS Low shows good inter- and intra-rater agreement for the depiction of synovitis, particularly for readers experienced with the sequence., (© 2021. European Society of Radiology.)

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.

Cartilage transmits and redistributes biomechanical loads in the knee joint during exercise. Exercise-induced loading alters cartilage hydration and is detectable using quantitative magnetic resonance imaging (MRI), where T 2 relaxation time (T 2 ) is influenced by cartilage collagen composition, fiber orientation, and changes in the extracellular matrix. This study characterized short-term transient responses of healthy knee cartilage to running-induced loading using bilateral scans and image registration. Eleven healthy female recreational runners (33.73 ± 4.22 years) and four healthy female controls (27.25 ± 1.38 years) were scanned on a 3T GE MRI scanner with quantitative 3D double-echo in steady-state before running over-ground (runner group) or resting (control group) for 40 min. Subjects were scanned immediately post-activity at 5-min intervals for 60 min. T 2 times were calculated for femoral, tibial, and patellar cartilage at each time point and analyzed using a mixed-effects model and Bonferroni post hoc. There were immediate decreases in T 2 (mean ± SEM) post-run in superficial femoral cartilage of at least 3.3% ± 0.3% (p = .002) between baseline and Time 0 that remained for 25 min, a decrease in superficial tibial cartilage T 2 of 2.9% ± 0.4% (p = .041) between baseline and Time 0, and a decrease in superficial patellar cartilage T 2 of 3.6% ± 0.3% (p = .020) 15 min post-run. There were decreases in the medial posterior region of superficial femoral cartilage T 2 of at least 5.3 ± 0.2% (p = .022) within 5 min post-run that remained at 60 min post-run. These results increase understanding of transient responses of healthy cartilage to repetitive, exercise-induced loading and establish preliminary recommendations for future definitive studies of cartilage response to running., (© 2021 Orthopaedic Research Society. Published by Wiley Periodicals LLC.)

Background: Injuries to the articular cartilage in the knee are common in jumping athletes, particularly high-level basketball players. Unfortunately, these are often diagnosed at a late stage of the disease process, after tissue loss has already occurred., Purpose/hypothesis: To evaluate longitudinal changes in knee articular cartilage and knee function in National Collegiate Athletic Association (NCAA) basketball players and their evolution over the competitive season and off-season., Study Type: Longitudinal, multisite cohort study., Population: Thirty-two NCAA Division 1 athletes: 22 basketball players and 10 swimmers., Field Strength/sequence: Bilateral magnetic resonance imaging (MRI) using a combined T 1ρ and T 2 magnetization-prepared angle-modulated portioned k-space spoiled gradient-echo snapshots (MAPSS) sequence at 3T., Assessment: We calculated T 2 and T 1ρ relaxation times to compare compositional cartilage changes between three timepoints: preseason 1, postseason 1, and preseason 2. Knee Osteoarthritis Outcome Scores (KOOS) were used to assess knee health., Statistical Tests: One-way variance model hypothesis test, general linear model, and chi-squared test., Results: In the femoral articular cartilage of all athletes, we saw a global decrease in T 2 and T 1ρ relaxation times during the competitive season (all P < 0.05) and an increase in T 2 and T 1ρ relaxation times during the off-season (all P < 0.05). In the basketball players' femoral cartilage, the anterior and central compartments respectively had the highest T 2 and T 1ρ relaxation times following the competitive season and off-season. The basketball players had significantly lower KOOS measures in every domain compared with the swimmers: Pain (P < 0.05), Symptoms (P < 0.05), Function in Daily Living (P < 0.05), Function in Sport/Recreation (P < 0.05), and Quality of Life (P < 0.05)., Conclusion: Our results indicate that T 2 and T 1ρ MRI can detect significant seasonal changes in the articular cartilage of basketball players and that there are regional differences in the articular cartilage that are indicative of basketball-specific stress on the femoral cartilage. This study demonstrates the potential of quantitative MRI to monitor global and regional cartilage health in athletes at risk of developing cartilage problems., Level of Evidence: 2 Technical Efficacy Stage: 2., (© 2021 International Society for Magnetic Resonance in Medicine.)

Artificial intelligence algorithms based on principles of deep learning (DL) have made a large impact on the acquisition, reconstruction, and interpretation of MRI data. Despite the large number of retrospective studies using DL, there are fewer applications of DL in the clinic on a routine basis. To address this large translational gap, we review the recent publications to determine three major use cases that DL can have in MRI, namely, that of model-free image synthesis, model-based image reconstruction, and image or pixel-level classification. For each of these three areas, we provide a framework for important considerations that consist of appropriate model training paradigms, evaluation of model robustness, downstream clinical utility, opportunities for future advances, as well recommendations for best current practices. We draw inspiration for this framework from advances in computer vision in natural imaging as well as additional healthcare fields. We further emphasize the need for reproducibility of research studies through the sharing of datasets and software. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 2., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: Provide a direct, non-invasive diagnostic measure of microscopic tissue texture in the size scale between tens of microns and the much larger scale measurable by clinical imaging. This paper presents a method and data demonstrating the ability to measure these microscopic pathologic tissue textures (histology) in the presence of subject motion in an MR scanner. This size range is vital to diagnosing a wide range of diseases., Theory/methods: MR micro-Texture (MRµT) resolves these textures by a combination of measuring a targeted set of k-values to characterize texture-as in diffraction analysis of materials, performing a selective internal excitation to isolate a volume of interest (VOI), applying a high k-value phase encode to the excited spins in the VOI, and acquiring each individual k-value data point in a single excitation-providing motion immunity and extended acquisition time for maximizing signal-to-noise ratio. Additional k-value measurements from the same tissue can be made to characterize the tissue texture in the VOI-there is no need for these additional measurements to be spatially coherent as there is no image to be reconstructed. This method was applied to phantoms and tissue specimens including human prostate tissue., Results: Data demonstrating resolution <50 µm, motion immunity, and clearly differentiating between normal and cancerous tissue textures are presented., Conclusion: The data reveal textural differences not resolvable by standard MR imaging. As MRµT is a pulse sequence, it is directly translatable to MRI scanners currently in clinical practice to meet the need for further improvement in cancer imaging., (© 2021 International Society for Magnetic Resonance in Medicine.)

Magnetic resonance imaging (MRI) is commonly used to evaluate the morphology of the knee in athletes with high-knee impact; however, complex repeated loading of the joint can lead to biochemical and structural degeneration that occurs before visible morphological changes. In this study, we utilized multiparametric quantitative MRI to compare morphology and composition of articular cartilage and subchondral bone shape between young athletes with high-knee impact (basketball players; n = 40) and non-knee impact (swimmers; n = 25). We implemented voxel-based relaxometry to register all cases to a single reference space and performed a localized compositional analysis of T 1ρ - and T 2 -relaxation times on a voxel-by-voxel basis. Additionally, statistical shape modeling was employed to extract differences in subchondral bone shape between the two groups. Evaluation of cartilage composition demonstrated a significant prolongation of relaxation times in the medial femoral and tibial compartments and in the posterolateral femur of basketball players in comparison to relaxation times in the same cartilage compartments of swimmers. The compositional analysis also showed depth-dependent differences with prolongation of the superficial layer in basketball players. For subchondral bone shape, three total modes were found to be significantly different between groups and related to the relative sizes of the tibial plateaus, intercondylar eminences, and the curvature and concavity of the patellar lateral facet. In summary, this study identified several characteristics associated with a high-knee impact which may expand our understanding of local degenerative patterns in this population., (© 2020 The Authors. Journal of Orthopaedic Research ® published by Wiley Periodicals LLC on behalf of Orthopaedic Research Society.)

Continuous health monitoring and integrated diagnostic devices, worn on the body and used in the home, will help to identify and prevent early manifestations of disease. However, challenges lie ahead in validating new health monitoring technologies and in optimizing data analytics to extract actionable conclusions from continuously obtained health data., (Copyright © 2021 The Authors, some rights reserved; exclusive licensee American Association for the Advancement of Science. No claim to original U.S. Government Works.)

BACKGROUND. Potential approaches for abbreviated knee MRI, including prospective acceleration with deep learning, have achieved limited clinical implementation. OBJECTIVE. The objective of this study was to evaluate the interreader agreement between conventional knee MRI and a 5-minute 3D quantitative double-echo steady-state (qDESS) sequence with automatic T2 mapping and deep learning super-resolutionaugmentation and to compare the diagnostic performance of the two methods regarding findings from arthroscopic surgery. METHODS. Fifty-one patients with knee pain underwent knee MRI that included an additional 3D qDESS sequence with automatic T2 mapping. Fourier interpolation was followed by prospective deep learning super resolution to enhance qDESS slice resolution twofold. A musculoskeletal radiologist and a radiology resident performed independent retrospective evaluations of articular cartilage, menisci, ligaments, bones, extensor mechanism, and synovium using conventional MRI. Following a 2-month washout period, readers reviewed qDESS images alone followed by qDESS with the automatic T2 maps. Interreader agreement between conventional MRI and qDESS was computed using percentage agreement and Cohen kappa. The sensitivity and specificity of conventional MRI, qDESS alone, and qDESS plus T2 mapping were compared with arthroscopic findings using exact McNemar tests. RESULTS. Conventional MRI and qDESS showed 92% agreement in evaluating all tissues. Kappa was 0.79 (95% CI, 0.76-0.81) across all imaging findings. In 43 patients who underwent arthroscopy, sensitivity and specificity were not significantly different ( p = .23 to > .99) between conventional MRI (sensitivity, 58-93%; specificity, 27-87%) and qDESS alone (sensitivity, 54-90%; specificity, 23-91%) for cartilage, menisci, ligaments, and synovium. For grade 1 cartilage lesions, sensitivity and specificity were 33% and 56%, respectively, for conventional MRI; 23% and 53% for qDESS ( p = .81); and 46% and 39% for qDESS with T2 mapping ( p = .80). For grade 2A lesions, values were 27% and 53% for conventional MRI, 26% and 52% for qDESS ( p = .02), and 58% and 40% for qDESS with T2 mapping ( p < .001). CONCLUSION. The qDESS method prospectively augmented with deep learning showed strong interreader agreement with conventional knee MRI and near-equivalent diagnostic performance regarding arthroscopy. The ability of qDESS to automatically generate T2 maps increases sensitivity for cartilage abnormalities. CLINICAL IMPACT. Using prospective artificial intelligence to enhance qDESS image quality may facilitate an abbreviated knee MRI protocol while generating quantitative T2 maps.

Background and purpose - Quantitative T2 mapping MRI of cartilage has proven value for the assessment of early osteoarthritis changes in research. We evaluated knee cartilage T2 relaxation times in a clinical population with knee complaints and its association with patients and disease characteristics and clinical symptoms.Patients and methods - In this cross-sectional study, T2 mapping knee scans of 109 patients with knee pain who were referred for an MRI by an orthopedic surgeon were collected. T2 relaxation times were calculated in 6 femoral and tibial regions of interest of full-thickness tibiofemoral cartilage. Its associations with age, sex, BMI, duration of complaints, disease onset (acute/chronic), and clinical symptoms were assessed with multivariate regression analysis. Subgroups were created of patients with abnormalities expected to cause predominantly medial or lateral tibiofemoral cartilage changes.Results - T2 relaxation times increased statistically significantly with higher age and BMI. In patients with expected medial cartilage damage, the medial femoral T2 values were significantly higher than the lateral; in patients with expected lateral cartilage damage the lateral tibial T2 values were significantly higher. A traumatic onset of knee complaints was associated with an acute elevation. No significant association was found with clinical symptoms.Interpretation - Our study demonstrates age, BMI, and type of injury-dependent T2 relaxation times and emphasizes the importance of acknowledging these variations when performing T2 mapping in a clinical population.

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., Competing Interests: Disclosures of Conflicts of Interest: B.S. Activities related to the present article: institution/author receives funding from the NIH (K99AG066815, PI B.S.); institution received grant from Defense Advanced Research Projects Agency (DARPA 2016D006054, PI M.S.R.). Activities not related to the present article: author is listed as inventor of US patents 9,389,294 and 10,775,463; royalties from these patents are paid to Stanford University, the patent owner, and Stanford University in turn forwards a part of the royalties to the author; the methods described in these patents are not used in the work presented; the author’s laboratory is engaged with GE Healthcare in a research project to reduce noise in medical images with the help of AI-based image reconstruction and the laboratory receives funds from GE Healthcare as a part of this (author is not involved with this project). Other relationships: disclosed no relevant relationships. A.S.C. Activities related to the present article: institution received grants from NIH, GE Healthcare, and Philips. Activities not related to the present article: author serves on the scientific advisory boards of BrainKey and Chondrometrics; author is a paid consultant for Skope MR, Subtle Medical, Chrondrometrics, Image Analysis Group, Edge Analytics, ICM, and Culvert Engineering; institution receives grants from GE Healthcare and Philips; author has stock or stock options in LVIS, BrainKey, and Subtle Medical. Other relationships: disclosed no relevant relationships. B.Z. disclosed no relevant relationships. N.K. Activities related to the present article: institution receives grants from DARPA and GE Healthcare; institution receives support for travel from DARPA and GE Healthcare. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. M.T. disclosed no relevant relationships. G.E.G. disclosed no relevant relationships. M.S.R. Activities related to the present article: institution receives grant from DARPA (PI: M.S.R.) and GE Healthcare (PI: M.S.R). Activities not related to the present article: author is a paid consultant to Synex (unrelated to the work in the manuscript). Other relationships: M.S.R. is a co-founder and equity holder of Hyperfine Research, Vizma Life Sciences, Intact Data Services, Q4ML, and BlinkAI. None of these companies are related to the present manuscript., (2021 by the Radiological Society of North America, Inc.)

Objective: The knee adduction moment (KAM) can inform treatment of medial knee osteoarthritis; however, measuring the KAM requires an expensive gait analysis laboratory. We evaluated the feasibility of predicting the peak KAM during natural and modified walking patterns using the positions of anatomical landmarks that could be identified from video analysis., Method: Using inverse dynamics, we calculated the KAM for 86 individuals (64 with knee osteoarthritis, 22 without) walking naturally and with foot progression angle modifications. We trained a neural network to predict the peak KAM using the 3-dimensional positions of 13 anatomical landmarks measured with motion capture (3D neural network). We also trained models to predict the peak KAM using 2-dimensional subsets of the dataset to simulate 2-dimensional video analysis (frontal and sagittal plane neural networks). Model performance was evaluated on a held-out, 8-person test set that included steps from all trials., Results: The 3D neural network predicted the peak KAM for all test steps with r 2 ( Murray et al., 2012) 2 = 0.78. This model predicted individuals' average peak KAM during natural walking with r 2 ( Murray et al., 2012) 2 = 0.86 and classified which 15° foot progression angle modifications reduced the peak KAM with accuracy = 0.85. The frontal plane neural network predicted peak KAM with similar accuracy (r 2 ( Murray et al., 2012) 2 = 0.85) to the 3D neural network, but the sagittal plane neural network did not (r 2 ( Murray et al., 2012) 2 = 0.14)., Conclusion: Using the positions of anatomical landmarks from motion capture, a neural network accurately predicted the peak KAM during natural and modified walking. This study demonstrates the feasibility of measuring the peak KAM using positions obtainable from 2D video analysis., (Copyright © 2021 Osteoarthritis Research Society International. All rights reserved.)

Diffusion tensor imaging (DTI) measures water diffusion in skeletal muscle tissue and allows for muscle assessment in a broad range of neuromuscular diseases. However, current DTI measurements, typically performed using pulsed gradient spin echo (PGSE) diffusion encoding, are limited to the assessment of non-contracted musculature, therefore providing limited insight into muscle contraction mechanisms and contraction abnormalities. In this study, we propose the use of an oscillating gradient spin echo (OGSE) diffusion encoding strategy for DTI measurements to mitigate the effect of signal voids in contracted muscle and to obtain reliable diffusivity values. Two OGSE sequences with encoding frequencies of 25 and 50 Hz were tested in the lower leg of five healthy volunteers with relaxed musculature and during active dorsiflexion and plantarflexion, and compared with a conventional PGSE approach. A significant reduction of areas of signal voids using OGSE compared with PGSE was observed in the tibialis anterior for the scans obtained in active dorsiflexion and in the soleus during active plantarflexion. The use of PGSE sequences led to unrealistically elevated axial diffusivity values in the tibialis anterior during dorsiflexion and in the soleus during plantarflexion, while the corresponding values obtained using the OGSE sequences were significantly reduced. Similar findings were seen for radial diffusivity, with significantly higher diffusivity measured in plantarflexion in the soleus muscle using the PGSE sequence. Our preliminary results indicate that DTI with OGSE diffusion encoding is feasible in human musculature and allows to quantitatively assess diffusion properties in actively contracting skeletal muscle. OGSE holds great potential to assess microstructural changes occurring in the skeletal muscle during contraction, and for non-invasive assessment of contraction abnormalities in patients with muscle diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Mazzoli, Moulin, Kogan, Hargreaves and Gold.)

Background: To assess diagnostic accuracy of quantitative double-echo in steady-state (qDESS) MRI for detecting synovitis in knee osteoarthritis (OA)., Methods: Patients with different degrees of radiographic knee OA were included prospectively. All underwent MRI with both qDESS and contrast-enhanced T1-weighted magnetic resonance imaging (CE-MRI). A linear combination of the two qDESS images can be used to create an image that displays contrast between synovium and the synovial fluid. Synovitis on both qDESS and CE-MRI was assessed semi-quantitatively, using a whole-knee synovitis sum score, indicating no/equivocal, mild, moderate, and severe synovitis. The correlation between sum scores of qDESS and CE-MRI (reference standard) was determined using Spearman's rank correlation coefficient and intraclass correlation coefficient for absolute agreement. Receiver operating characteristic analysis was performed to assess the diagnostic performance of qDESS for detecting different degrees of synovitis, with CE-MRI as reference standard., Results: In the 31 patients included, very strong correlation was found between synovitis sum scores on qDESS and CE-MRI (ρ = 0.96, p < 0.001), with high absolute agreement (0.84 (95%CI 0.14-0.95)). Mean sum score (SD) values on qDESS 5.16 (3.75) were lower than on CE-MRI 7.13 (4.66), indicating systematically underestimated synovitis severity on qDESS. For detecting mild synovitis or higher, high sensitivity and specificity were found for qDESS (1.00 (95%CI 0.80-1.00) and 0.909 (0.571-1.00), respectively). For detecting moderate synovitis or higher, sensitivity and specificity were good (0.727 (95%CI 0.393-0.927) and 1.00 (0.800-1.00), respectively)., Conclusion: qDESS MRI is able to, however with an underestimation, detect synovitis in patients with knee OA.

Purpose: To organize a multi-institute knee MRI segmentation challenge for characterizing the semantic and clinical efficacy of automatic segmentation methods relevant for monitoring osteoarthritis progression., Materials and Methods: A dataset partition consisting of three-dimensional knee MRI from 88 retrospective patients at two time points (baseline and 1-year follow-up) with ground truth articular (femoral, tibial, and patellar) cartilage and meniscus segmentations was standardized. Challenge submissions and a majority-vote ensemble were evaluated against ground truth segmentations using Dice score, average symmetric surface distance, volumetric overlap error, and coefficient of variation on a holdout test set. Similarities in automated segmentations were measured using pairwise Dice coefficient correlations. Articular cartilage thickness was computed longitudinally and with scans. Correlation between thickness error and segmentation metrics was measured using the Pearson correlation coefficient. Two empirical upper bounds for ensemble performance were computed using combinations of model outputs that consolidated true positives and true negatives., Results: Six teams ( T 1 - T 6 ) submitted entries for the challenge. No differences were observed across any segmentation metrics for any tissues ( P = .99) among the four top-performing networks ( T 2 , T 3 , T 4 , T 6 ). Dice coefficient correlations between network pairs were high (> 0.85). Per-scan thickness errors were negligible among networks T 1 - T 4 ( P = .99), and longitudinal changes showed minimal bias (< 0.03 mm). Low correlations (ρ < 0.41) were observed between segmentation metrics and thickness error. The majority-vote ensemble was comparable to top-performing networks ( P = .99). Empirical upper-bound performances were similar for both combinations (P = .99)., Conclusion: Diverse networks learned to segment the knee similarly, where high segmentation accuracy did not correlate with cartilage thickness accuracy and voting ensembles did not exceed individual network performance.See also the commentary by Elhalawani and Mak in this issue. Keywords: Cartilage, Knee, MR-Imaging, Segmentation © RSNA, 2020 Supplemental material is available for this article., Competing Interests: Disclosures of Conflicts of Interest: A.D.D. Activities related to the present article: grants and travel support from the National Science Foundation, the National Institute of Arthritis and Musculoskeletal and Skin Diseases, the National Institute of Biomedical Imaging and Bioengineering, GE Healthcare, and Philips. Activities not related to the present article: grants from the National Institutes of Health. Other relationships: disclosed no relevant relationships. F.C. disclosed no relevant relationships. C. Iriondo disclosed no relevant relationships. A.M. disclosed no relevant relationships. S.J. disclosed no relevant relationships. U.B. disclosed no relevant relationships. M.P. Activities related to the present article: grant from the Independent Research Fund Denmark. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. C. Igel Activities related to the present article: grant from the Danish Council for Independent Research. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. E.B.D. Activities related to the present article: disclosed no relevant relationships. Activities not related to the present article: stockholder in Biomediq and Cerebriu. Other relationships: disclosed no relevant relationships. S.G. disclosed no relevant relationships. M.Y. disclosed no relevant relationships. X.L. disclosed no relevant relationships. C.M.D. Activities related to the present article: grant from the National Institute of Arthritis and Musculoskeletal and Skin Diseases. Activities not related to the present article: disclosed no relevant relationships. Other relationships: disclosed no relevant relationships. V.J. disclosed no relevant relationships. R.R. disclosed no relevant relationships. G.E.G. Activities related to the present article: grants from the National Institutes of Health. Activities not related to the present article: board member for HeartVista; consultant for Canon; grants from GE Healthcare. Other relationships: disclosed no relevant relationships. B.A.H. Activities related to the present article: grant from the National Institutes of Health. Activities not related to the present article: royalties from patents licensed by Siemens and GE Healthcare; stockholder in LVIS. Other relationships: disclosed no relevant relationships. V.P. disclosed no relevant relationships. A.S.C. Activities related to the present article: grants from the National Institutes of Health, GE Healthcare, and Philips. Activities not related to the present article: board member for BrainKey and Chondrometrics; consultant for Skope, Subtle Medical, Chondrometrics, Image Analysis Group, Edge Analytics, ICM, and Culvert Engineering; stockholder in Subtle Medical, LVIS, and BrainKey; travel support from Paracelsus Medical Private University. Other relationships: disclosed no relevant relationships., (2021 by the Radiological Society of North America, Inc.)

This work presents an analytical relationship between gradient-spoiled and RF-spoiled steady-state signals. The two echoes acquired in double-echo in steady-state scans are shown to lie on a line in the signal plane, where the two axes represent the amplitudes of each echo. The location along the line depends on the amount of spoiling and the diffusivity. The line terminates in a point corresponding to an RF-spoiled signal. In addition to the main contribution of demonstrating this signal relationship, we also include the secondary contribution of preliminary results from an example application of the relationship, in the form of a heuristic denoising method when both types of scans are performed. This is investigated in simulations, phantom scans, and in vivo scans. For the signal model, the main topic of this study, simulations confirmed its accuracy and explored its dependency on signal parameters and image noise. For the secondary topic of its preliminary application to reduce noise, simulations demonstrated the denoising method giving a reduction in noise-induced standard deviation of about 30%. The relative effect of the method on the signals is shown to depend on the slope of the described line, which is demonstrated to be zero at the Ernst angle. The phantom scans show a similar effect as the simulations. In vivo scans showed a slightly lower average improvement of about 28%.

Osteoarthritis (OA) of the knee is a major source of disability that has no known treatment or cure. Morphological and compositional MRI is commonly used for assessing the bone and soft tissues in the knee to enhance the understanding of OA pathophysiology. However, it is challenging to extend these imaging methods and their subsequent analysis techniques to study large population cohorts due to slow and inefficient imaging acquisition and postprocessing tools. This can create a bottleneck in assessing early OA changes and evaluating the responses of novel therapeutics. The purpose of this review article is to highlight recent developments in tools for enhancing the efficiency of knee MRI methods useful to study OA. Advances in efficient MRI data acquisition and reconstruction tools for morphological and compositional imaging, efficient automated image analysis tools, and hardware improvements to further drive efficient imaging are discussed in this review. For each topic, we discuss the current challenges as well as potential future opportunities to alleviate these challenges. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY STAGE: 3., (© 2019 International Society for Magnetic Resonance in Medicine.)