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This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs. [ABSTRACT FROM AUTHOR]

Femoroacetabular impingement (FAI) is a cause of hip pain and can lead to hip osteoarthritis. Radiological measurements obtained from radiographs or magnetic resonance imaging (MRI) are normally used for FAI diagnosis, but they require time‐consuming manual interaction, which limits accuracy and reproducibility. This study compares standard radiologic measurements against radiomics features automatically extracted from MRI for the identification of FAI patients versus healthy subjects. Three‐dimensional Dixon MRI of the pelvis were retrospectively collected for 10 patients with confirmed FAI and acquired for 10 healthy subjects. The femur and acetabulum were segmented bilaterally and associated radiomics features were extracted from the four MRI contrasts of the Dixon sequence (water‐only, fat‐only, in‐phase, and out‐of‐phase). A radiologist collected 21 radiological measurements typically used in FAI. The Gini importance was used to define 9 subsets with the most predictive radiomics features and one subset for the most diagnostically relevant radiological measurements. For each subset, 100 Random Forest machine learning models were trained with different data splits and fivefold cross‐validation to classify healthy subjects versus FAI patients. The average performance among the 100 models was computed for each subset and compared against the performance of the radiological measurements. One model trained using the radiomics features datasets yielded 100% accuracy in the detection of FAI, whereas all other radiomics features exceeded 80% accuracy. Radiological measurements yielded 74% accuracy, consistent with previous work. The results of this preliminary work highlight for the first time the potential of radiomics for fully automated FAI diagnosis. [ABSTRACT FROM AUTHOR]

Background: There is limited understanding of differences in the composition and structure of ligaments between healthy males and females, and individuals of different ages. Females present higher risk for ligament injuries than males and there are conflicting reports on its cause. This study looks into T1ρ parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues. Purpose: To investigate gender‐related and age‐related differences in T1ρ parameters in knee joint ligaments in healthy volunteers using a T1ρ‐prepared zero echo‐time (ZTE)‐based pointwise‐encoding time‐reduction with radial acquisition (T1ρ‐PETRA) sequence. Study Type: Prospective. Population: The study group consisted of 22 healthy subjects (11 females, ages: 41 ± 18 years, and 11 males, ages: 41 ± 14 years) with no known inflammation, trauma, or pain in the knee joint. Field Strength/Sequence: A T1ρ‐prepared 3D‐PETRA sequence was used to acquire fat‐suppressed images with varying spin‐lock lengths (TSLs) of the knee joint at 3T. Assessment: Monoexponential, biexponential, and stretched‐exponential 3D‐PETRA‐T1ρ parameters were measured in the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and patellar tendon (PT) by manually drawing ROIs over the entirety of the tissues. Statistical Tests: Mann–Whitney U‐tests were used to compare 3D‐PETRA‐T1ρ parameters in the ACL, PCL, and PT between males and females. Spearman correlation coefficients were used to determine the association between age and T1ρ parameters. Statistical significance was defined as P < 0.05. Results: Significant correlations with age were found the three ligaments with most of the measured T1ρ parameters (rs = 0.28–0.74) with the exception of the short fraction in the PCL (P = 0.18), and the short relaxation time in the ACL (P = 0.58) and in the PCL (P = 0.14). Data Conclusion: 3D‐PETRA‐T1ρ can detect age‐related differences in monoexponential, biexponential, and stretched‐exponential T1ρ parameters in three ligaments of healthy volunteers, which are thought to be related to changes in tissue composition and structure during the aging process. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Background: Three‐dimensional MR fingerprinting (3D‐MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. Purpose: To assess the intra‐day and inter‐day repeatabilities of knee cartilage T1, T2, and T1ρ maps using a 3D‐MRF sequence for simultaneous measurement. Study Type: Prospective. Subjects: Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. Field Strength/Sequence: 3 T/3D‐MRF, T1, T2, and T1ρ maps. Assessment: The acquisition of 3D‐MRF cartilage (simultaneous acquisition of T1, T2, and T1ρ maps) were acquired using a dictionary pattern‐matching approach. Conventional cartilage T1, T2, and T1ρ maps were acquired using variable flip angles and a modified 3D‐Turbo‐Flash sequence with different echo and spin‐lock times, respectively, and were fitted using mono‐exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. Statistical Tests: The mean and SD for cartilage T1, T2, and T1ρ were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra‐subject and inter‐subject repeatabilities were assessed using coefficient of variation (CV) and intra‐class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland–Altman analysis were performed to compare maps between the conventional and 3D‐MRF sequences. Results: The CV in all ROIs was lower than 7.4%, 8.4%, and 7.5% and the ICC was higher than 0.56, 0.51, and 0.52 for cartilage T1, T2, and T1ρ, respectively. The MRF results had a good agreement with the conventional methods with a linear regression slope >0.61 and R2 > 0.59. Conclusion: The 3D‐MRF sequence had high intra‐subject and inter‐subject repeatabilities for simultaneously measuring knee cartilage T1, T2, and T1ρ with good agreement with conventional sequences. Evidence Level: 1 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Objective: To identify the region of interest (ROI) to represent injury and observe between-limb diffusion tensor imaging (DTI) microstructural differences in muscle following hamstring strain injury. Materials and methods: Participants who sustained a hamstring strain injury prospectively underwent 3T-MRI of bilateral thighs using T1, T2, and diffusion-weighted imaging at time of injury (TOI), return to sport (RTS), and 12 weeks after RTS (12wks). ROIs were using the hyperintense region on a T2-weighted sequence: edema, focused edema, and primary muscle injured excluding edema (no edema). Linear mixed-effects models were used to compare diffusion parameters between ROIs and timepoints and limbs and timepoints. Results: Twenty-four participants (29 injuries) were included. A significant ROI-by-timepoint interaction was detected for all diffusivity measures. The edema and focused edema ROIs demonstrated increased diffusion at TOI compared to RTS for all diffusivity measures (p-values < 0.006), except λ1 (p-values = 0.058–0.12), and compared to 12wks (p-values < 0.02). In the no edema ROI, differences in diffusivity measures were not observed (p-values > 0.82). At TOI, no edema ROI diffusivity measures were lower than the edema ROI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.69). A significant limb-by-timepoint interaction was detected for all diffusivity measures with increased diffusion in the involved limb at TOI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.42). Significant differences in fractional anisotropy over time or between limbs were not detected. Conclusion: Hyperintensity on T2-weighted imaging used to define the injured region holds promise in describing muscle microstructure following hamstring strain injury by demonstrating between-limb differences at TOI but not at follow-up timepoints. [ABSTRACT FROM AUTHOR]

Background: Multiparameter characterization using MR fingerprinting (MRF) can quantify multiple relaxation parameters of intervertebral disc (IVD) simultaneously. These parameters may vary by age and sex. Purpose: To investigate age‐ and sex‐related differences in the relaxation parameters of the IVD of the lumbar spine using a multiparameter MRF technique. Study Type: Prospective. Subjects: 17 healthy subjects (8 male; mean age = 34 ± 10 years, range 20–60 years). Field Strength/Sequence: 3D‐MRF sequence for simultaneous acquisition of proton density, T1, T2, and T1ρ maps at 3.0T. Assessment: Global mean T1, T2, and T1ρ of all lumbar IVDs and mean T1, T2, and T1ρ of each individual IVD (L1–L5) were measured. Gray level co‐occurrence matrix was used to quantify textural features (median, contrast, correlation, energy, and homogeneity) from T1, T2, and T1ρ maps. Statistical Tests: Spearman rank correlations (R) evaluated the association between age and T1, T2, and T1ρ of IVD. Mann–Whitney U‐tests evaluated differences between males and females in T1, T2, and T1ρ of IVD. Statistical significance was defined as P‐value <0.05. Results: There was a significant negative correlation between age and global mean values of all IVDs for T1 (R = −0.637), T2 (R = −0.509), and T1ρ (R = −0.726). For individual IVDs, there was a significant negative correlation between age and mean T1 at all IVD segments (R range = −0.530 to −0.708), between age and mean T2 at L2‐L3, L3‐L4, and L4‐L5 (R range = −0.493 to 0.640), and between age and mean T1ρ at all segments except L1‐L2 (R range = −0.632 to −0.763). There were no significant differences between sexes in global mean T1, T2, and T1ρ (P‐value = 0.23–0.76) The texture features with the highest significant correlations with age for all IVDs were global T1ρ mean (R = −0.726), T1 energy (R = −0.681), and T1 contrast (R = 0.709). Conclusion: This study showed that the 3D‐MRF technique has potential to characterize age‐related differences in T1, T2, or T1ρ of IVD in healthy subjects. Level of Evidence: 2 Technical Efficacy: Stage 1 [ABSTRACT FROM AUTHOR]

Objective: To determine if MRI-based radiomics from hamstring muscles are related to injury and if the features could be used to perform a time to return to sport (RTS) classification. We hypothesize that radiomics from hamstring muscles, especially T2-weighted and diffusion tensor imaging-based features, are related to injury and can be used for RTS classification. Subjects and methods: MRI data from 32 athletes at the University of Wisconsin-Madison that sustained a hamstring strain injury were collected. Diffusion tensor imaging and T1- and T2-weighted images were processed, and diffusion maps were calculated. Radiomics features were extracted from the four hamstring muscles in each limb and for each MRI modality, individually. Feature selection was performed and multiple support vector classifiers were cross-validated to differentiate between involved and uninvolved limbs and perform binary (≤ or > 25 days) and multiclass (< 14 vs. 14—42 vs. > 42 days) classification of RTS. Result: The combination of radiomics features from all diffusion tensor imaging and T2-weighted images provided the most accurate differentiation between involved and uninvolved limbs (AUC ≈ 0.84 ± 0.16). For the binary RTS classification, the combination of all extracted radiomics offered the most accurate classification (AUC ≈ 0.95 ± 0.15). While for the multiclass RTS classification, the combination of features from all the diffusion tensor imaging maps provided the most accurate classification (weighted one vs. rest AUC ≈ 0.81 ± 0.16). Conclusion: This pilot study demonstrated that radiomics features from hamstring muscles are related to injury and have the potential to predict RTS. [ABSTRACT FROM AUTHOR]

Currently no disease-modifying osteoarthritis drug has been approved for the treatment of osteoarthritis (OA) that can reverse, hold, or slow the progression of structural damage of OA-affected joints. The reasons for failure are manifold and include the heterogeneity of structural disease of the OA joint at trial inclusion, and the sensitivity of biomarkers used to measure a potential treatment effect. This article discusses the role and potential of different imaging biomarkers in OA research. We review the current role of radiography, as well as advances in quantitative three-dimensional morphological cartilage assessment and semiquantitative whole-organ assessment of OA. Although magnetic resonance imaging has evolved as the leading imaging method in OA research, recent developments in computed tomography are also discussed briefly. Finally, we address the experience from the Foundation for the National Institutes of Health Biomarker Consortium biomarker qualification study and the future role of artificial intelligence. [ABSTRACT FROM AUTHOR]

The objective of the current study was to investigate age‐ and gender‐related differences in lumbar intervertebral disk (IVD) strain with the use of static mechanical loading and continuous three‐dimensional (3D) golden‐angle radial sparse parallel (GRASP) MRI. A continuous 3D‐GRASP stack‐of‐stars trajectory of the lumbar spine was performed on a 3‐T scanner with static mechanical loading. Compressed sensing reconstruction, motion deformation maps, and Lagrangian strain maps during loading and recovery in the X‐, Y‐, and Z‐directions were calculated for segmented IVD segments from L1/L2 to L5/S1. Mean IVD height was measured at rest. Spearman coefficients were used to evaluate the associations between age and global IVD height and global IVD strain. Mann–Whitney tests were used to compare global IVD height and global IVD strain in males and females. The prospective study enrolled 20 healthy human volunteers (10 males, 10 females; age 34.6 ± 11.4 [mean ± SD], range 22–56 years). Significant increases in compressive strain were observed with age, as evidenced by negative correlations between age and global IVD strain during loading (ρ = −0.76, p = 0.0046) and recovery (ρ = −0.68, p = 0.0251) in the loading X‐direction. There was no significant correlation between age and global IVD height, global IVD strain during loading and recovery in the Y‐direction, and global IVD strain during loading and recovery in the Z‐direction. There were no significant differences between males and females in global IVD height and global IVD strain during loading and recovery in the X‐, Y‐, and Z‐directions. It was concluded that our study demonstrated the significant role aging plays in internal dynamic strains in the lumbar IVD during loading and recovery. Older healthy individuals have reduced IVD stiffness and greater IVD compression during static mechanical loading of the lumbar spine. The GRASP‐MRI technique demonstrates the feasibility to identify changes in IVD mechanical properties with early IVD degeneration due to aging. [ABSTRACT FROM AUTHOR]

Accurately detecting and characterizing articular cartilage defects is critical in assessing patients with osteoarthritis. While radiography is the first-line imaging modality, magnetic resonance imaging (MRI) is the most accurate for the noninvasive assessment of articular cartilage. Multiple semiquantitative grading systems for cartilage lesions in MRI were developed. The Outerbridge and modified Noyes grading systems are commonly used in clinical practice and for research. Other useful grading systems were developed for research, many of which are joint-specific. Both two-dimensional (2D) and three-dimensional (3D) pulse sequences are used to assess cartilage morphology and biochemical composition. MRI techniques for morphological assessment of articular cartilage can be categorized into 2D and 3D FSE/TSE spin-echo and gradient-recalled echo sequences. T2 mapping is most commonly used to qualitatively assess articular cartilage microstructural composition and integrity, extracellular matrix components, and water content. Quantitative techniques may be able to label articular cartilage alterations before morphological defects are visible. Accurate detection and characterization of shallow low-grade partial and small articular cartilage defects are the most challenging for any technique, but where high spatial resolution 3D MRI techniques perform best. This review article provides a practical overview of commonly used 2D and 3D MRI techniques for articular cartilage assessments in osteoarthritis. [ABSTRACT FROM AUTHOR]

Deep learning (DL) is one of the most exciting new areas in medical imaging. This article will provide a review of current applications of DL in osteoarthritis (OA) imaging, including methods used for cartilage lesion detection, OA diagnosis, cartilage segmentation, and OA risk assessment. DL techniques have been shown to have similar diagnostic performance as human readers for detecting and grading cartilage lesions within the knee on MRI. A variety of DL methods have been developed for detecting and grading the severity of knee OA and various features of knee OA on X-rays using standardized classification systems with diagnostic performance similar to human readers. Multiple DL approaches have been described for fully automated segmentation of cartilage and other knee tissues and have achieved higher segmentation accuracy than currently used methods with substantial reductions in segmentation times. Various DL models analyzing baseline X-rays and MRI have been developed for OA risk assessment. These models have shown high diagnostic performance for predicting a wide variety of OA outcomes, including the incidence and progression of radiographic knee OA, the presence and progression of knee pain, and future total knee replacement. The preliminary results of DL applications in OA imaging have been encouraging. However, many DL techniques require further technical refinement to maximize diagnostic performance. Furthermore, the generalizability of DL approaches needs to be further investigated in prospective studies using large image datasets acquired at different institutions with different imaging hardware before they can be implemented in clinical practice and research studies. [ABSTRACT FROM AUTHOR]

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

Background: Magnetic resonance fingerprinting (MRF) techniques have been recently described for simultaneous multiparameter cartilage mapping of the knee although investigation of their ability to detect early cartilage degeneration remains limited. Purpose: To investigate age‐dependent changes in knee cartilage T1, T2, and T1p relaxation times measured using a three‐dimensional (3D) MRF sequence in healthy volunteers. Study Type: Prospective. Subjects: The study group consisted of 24 healthy asymptomatic human volunteers (15 males with mean age 34.9 ± 14.4 years and 9 females with mean age 44.5 ± 13.1 years). Field Strength/Sequence: A 3.0 T gradient‐echo‐based 3D‐MRF sequence was used to simultaneously create proton density‐weighted images and T1, T2, and T1p maps of knee cartilage. Assessment: Mean global cartilage and regional cartilage (lateral femur, lateral tibia, medial femur, medial tibia, and patella) T1, T2, and T1ρ relaxation times of the knee were measured. Statistical Tests: Kruskal–Wallis tests were used to compared cartilage T1, T2, and T1ρ relaxation times between different age groups, while Spearman correlation coefficients was used to determine the association between age and cartilage T1, T2, and T1ρ relaxation times. The value of P < 0.05 was considered statistically significant. Results: Higher age groups showed higher global and regional cartilage T1, T2, and T1ρ. There was a significant difference between age groups in global cartilage T2 and T1ρ but no significant difference (P = 0.13) in global cartilage T1. Significant difference was also present between age groups in cartilage T2 and T1ρ for medial femur cartilage and medial tibia cartilage. There were significant moderate correlations between age and T2 and T1ρ for global cartilage (R2 = 0.63–0.64), medial femur cartilage (R2 = 0.50–0.56), and medial tibia cartilage (R2 = 0.54–0.66). Conclusion: Cartilage T2 and T1p relaxation times simultaneously measured using a 3D‐MRF sequence in healthy volunteers showed age‐dependent changes in knee cartilage, primarily within the medial compartment. [ABSTRACT FROM AUTHOR]

Current methods for assessing knee osteoarthritis (OA) do not provide comprehensive information to make robust and accurate outcome predictions. Deep learning (DL) risk assessment models were developed to predict the progression of knee OA to total knee replacement (TKR) over a 108-month follow-up period using baseline knee MRI. Participants of our retrospective study consisted of 353 case–control pairs of subjects from the Osteoarthritis Initiative with and without TKR over a 108-month follow-up period matched according to age, sex, ethnicity, and body mass index. A traditional risk assessment model was created to predict TKR using baseline clinical risk factors. DL models were created to predict TKR using baseline knee radiographs and MRI. All DL models had significantly higher (p < 0.001) AUCs than the traditional model. The MRI and radiograph ensemble model and MRI ensemble model (where TKR risk predicted by several contrast-specific DL models were averaged to get the ensemble TKR risk prediction) had the highest AUCs of 0.90 (80% sensitivity and 85% specificity) and 0.89 (79% sensitivity and 86% specificity), respectively, which were significantly higher (p < 0.05) than the AUCs of the radiograph and multiple MRI models (where the DL models were trained to predict TKR risk using single contrast or 2 contrasts together as input). DL models using baseline MRI had a higher diagnostic performance for predicting TKR than a traditional model using baseline clinical risk factors and a DL model using baseline knee radiographs. [ABSTRACT FROM AUTHOR]

Artificial intelligence and deep learning (DL) offer musculoskeletal radiology exciting possibilities in multiple areas, including image reconstruction and transformation, tissue segmentation, workflow support, and disease detection. Novel DL-based image reconstruction algorithms correcting aliasing artifacts, signal loss, and noise amplification with previously unobtainable effectiveness are prime examples of how DL algorithms deliver promised value propositions in musculoskeletal radiology. The speed of DL-based tissue segmentation promises great efficiency gains that may permit the inclusion of tissue compositional-based information routinely into radiology reports. Similarly, DL algorithms give rise to a myriad of opportunities for workflow improvements, including intelligent and adaptive hanging protocols, speech recognition, report generation, scheduling, precertification, and billing. The value propositions of disease-detecting DL algorithms include reduced error rates and increased productivity. However, more studies using authentic clinical workflow settings are necessary to fully understand the value of DL algorithms for disease detection in clinical practice. Successful workflow integration and management of multiple algorithms are critical for translating the value propositions of DL algorithms into clinical practice but represent a major roadblock for which solutions are critically needed. While there is no consensus about the most sustainable business model, radiology departments will need to carefully weigh the benefits and disadvantages of each commercially available DL algorithm. Although more studies are needed to understand the value and impact of DL algorithms on clinical practice, DL technology will likely play an important role in the future of musculoskeletal imaging. [ABSTRACT FROM AUTHOR]

Objective: To develop and evaluate deep learning (DL) risk assessment models for predicting pain progression in subjects with or at risk of knee osteoarthritis (OA). Materials and methods: The incidence and progression cohorts of the Osteoarthritis Initiative, a multi-center longitudinal study involving 9348 knees in 4674 subjects with or at risk of knee OA that began in 2004 and is ongoing, were used to conduct this retrospective analysis. A subset of knees without and with pain progression (defined as a 9-point or greater increase in pain score between baseline and two or more follow-up time points over the first 48 months) was randomly stratified into training (4200 knees with a mean age of 61.0 years and 60% female) and hold-out testing (500 knees with a mean age of 60.8 years and 60% female) datasets. A DL model was developed to predict pain progression using baseline knee radiographs. An artificial neural network was used to develop a traditional risk assessment model to predict pain progression using demographic, clinical, and radiographic risk factors. A combined model was developed to combine demographic, clinical, and radiographic risk factors with DL analysis of baseline knee radiographs. Area under the curve (AUC) analysis was performed using the hold-out testing dataset to evaluate model performance. Results: The traditional model had an AUC of 0.692 (66.9% sensitivity and 64.1% specificity). The DL model had an AUC of 0.770 (76.7% sensitivity and 70.5% specificity), which was significantly higher (p < 0.001) than the traditional model. The combined model had an AUC of 0.807 (72.3% sensitivity and 80.9% specificity), which was significantly higher (p < 0.05) than the traditional and DL models. Conclusions: DL models using baseline knee radiographs had higher diagnostic performance for predicting pain progression than traditional models using demographic, clinical, and radiographic risk factors. [ABSTRACT FROM AUTHOR]

This issue of Seminars in Musculoskeletal Radiology explores the use of biomarkers in musculoskeletal imaging. Biomarkers are quantifiable indicators of biological processes or responses, and imaging biomarkers provide valuable anatomical, pathologic, and functional information using various imaging modalities. The articles in this issue cover a range of topics, including the evaluation of cartilage, tumors, bone, body composition, and peripheral nerves. The authors discuss the role of biomarkers in disease diagnosis, prognosis, prediction, and therapeutic monitoring, as well as the technical advances and standardization efforts in the field. Overall, this issue provides a comprehensive overview of the role of imaging biomarkers in musculoskeletal applications. [Extracted from the article]

Purpose: To develop a model‐guided self‐supervised deep learning MRI reconstruction framework called reference‐free latent map extraction (RELAX) for rapid quantitative MR parameter mapping. Methods: Two physical models are incorporated for network training in RELAX, including the inherent MR imaging model and a quantitative model that is used to fit parameters in quantitative MRI. By enforcing these physical model constraints, RELAX eliminates the need for full sampled reference data sets that are required in standard supervised learning. Meanwhile, RELAX also enables direct reconstruction of corresponding MR parameter maps from undersampled k‐space. Generic sparsity constraints used in conventional iterative reconstruction, such as the total variation constraint, can be additionally included in the RELAX framework to improve reconstruction quality. The performance of RELAX was tested for accelerated T1 and T2 mapping in both simulated and actually acquired MRI data sets and was compared with supervised learning and conventional constrained reconstruction for suppressing noise and/or undersampling‐induced artifacts. Results: In the simulated data sets, RELAX generated good T1/T2 maps in the presence of noise and/or undersampling artifacts, comparable to artifact/noise‐free ground truth. The inclusion of a spatial total variation constraint helps improve image quality. For the in vivo T1/T2 mapping data sets, RELAX achieved superior reconstruction quality compared with conventional iterative reconstruction, and similar reconstruction performance to supervised deep learning reconstruction. Conclusion: This work has demonstrated the initial feasibility of rapid quantitative MR parameter mapping based on self‐supervised deep learning. The RELAX framework may also be further extended to other quantitative MRI applications by incorporating corresponding quantitative imaging models. [ABSTRACT FROM AUTHOR]

Osteoarthritis, characterized by the breakdown of articular cartilage and other joint structures, is one of the most prevalent and disabling chronic diseases in the United States. Magnetic resonance imaging is a commonly used imaging modality to evaluate patients with joint pain. Both two-dimensional fast spin-echo sequences (2D-FSE) and three-dimensional (3D) sequences are used in clinical practice to evaluate articular cartilage. The 3D sequences have many advantages compared with 2D-FSE sequences, such as their high in-plane spatial resolution, thin continuous slices that reduce the effects of partial volume averaging, and ability to create multiplanar reformat images following a single acquisition. This article reviews the different 3D imaging techniques available for evaluating cartilage morphology, illustrates the strengths and weaknesses of 3D approaches compared with 2D-FSE approaches for cartilage imaging, and summarizes the diagnostic performance of 2D-FSE and 3D sequences for detecting cartilage lesions within the knee and hip joints. [ABSTRACT FROM AUTHOR]

Background: Graft placement is a modifiable and often discussed surgical factor in anterior cruciate ligament (ACL) reconstruction (ACLR). However, the sensitivity of functional knee mechanics to variability in graft placement is not well understood. Purpose: To (1) investigate the relationship of ACL graft tunnel location and graft angle with tibiofemoral kinematics in patients with ACLR, (2) compare experimentally measured relationships with those observed with a computational model to assess the predictive capabilities of the model, and (3) use the computational model to determine the effect of varying ACL graft tunnel placement on tibiofemoral joint mechanics during walking. Study Design: Controlled laboratory study. Methods: Eighteen participants who had undergone ACLR were tested. Bilateral ACL footprint location and graft angle were assessed using magnetic resonance imaging (MRI). Bilateral knee laxity was assessed at the completion of rehabilitation. Dynamic MRI was used to measure tibiofemoral kinematics and cartilage contact during active knee flexion-extension. Additionally, a total of 500 virtual ACLR models were created from a nominal computational knee model by varying ACL footprint locations, graft stiffness, and initial tension. Laxity tests, active knee extension, and walking were simulated with each virtual ACLR model. Linear regressions were performed between internal knee mechanics and ACL graft tunnel locations and angles for the patients with ACLR and the virtual ACLR models. Results: Static and dynamic MRI revealed that a more vertical graft in the sagittal plane was significantly related (P <.05) to a greater laxity compliance index (R 2 = 0.40) and greater anterior tibial translation and internal tibial rotation during active knee extension (R 2 = 0.22 and 0.23, respectively). Similarly, knee extension simulations with the virtual ACLR models revealed that a more vertical graft led to greater laxity compliance index, anterior translation, and internal rotation (R 2 = 0.56, 0.26, and 0.13). These effects extended to simulations of walking, with a more vertical ACL graft inducing greater anterior tibial translation, ACL loading, and posterior migration of contact on the tibial plateaus. Conclusion: This study provides clinical evidence from patients who underwent ACLR and from complementary modeling that functional postoperative knee mechanics are sensitive to graft tunnel locations and graft angle. Of the factors studied, the sagittal angle of the ACL was particularly influential on knee mechanics. Clinical Relevance: Early-onset osteoarthritis from altered cartilage loading after ACLR is common. This study shows that postoperative cartilage loading is sensitive to graft angle. Therefore, variability in graft tunnel placement resulting in small deviations from the anatomic ACL angle might contribute to the elevated risk of osteoarthritis after ACLR. [ABSTRACT FROM AUTHOR]

Deep learning is one of the most exciting new areas in medical imaging. This review article provides a summary of the current clinical applications of deep learning for lesion detection, progression, and prediction of musculoskeletal disease on radiographs, computed tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Deep‐learning methods have shown success for estimating pediatric bone age, detecting fractures, and assessing the severity of osteoarthritis on radiographs. In particular, the high diagnostic performance of deep‐learning approaches for estimating pediatric bone age and detecting fractures suggests that the new technology may soon become available for use in clinical practice. Recent studies have also documented the feasibility of using deep‐learning methods for identifying a wide variety of pathologic abnormalities on CT and MRI including internal derangement, metastatic disease, infection, fractures, and joint degeneration. However, the detection of musculoskeletal disease on CT and especially MRI is challenging, as it often requires analyzing complex abnormalities on multiple slices of image datasets with different tissue contrasts. Thus, additional technical development is needed to create deep‐learning methods for reliable and repeatable interpretation of musculoskeletal CT and MRI examinations. Furthermore, the diagnostic performance of all deep‐learning methods for detecting and characterizing musculoskeletal disease must be evaluated in prospective studies using large image datasets acquired at different institutions with different imaging parameters and different imaging hardware before they can be implemented in clinical practice. Level of Evidence: 5 Technical Efficacy Stage: 2 J. MAGN. RESON. IMAGING 2020;52:1607–1619. [ABSTRACT FROM AUTHOR]

The purpose of this study is to determine the feasibility of using a single scan ramped hybrid‐encoding (RHE) method for rapid bicomponent T2* analysis of the human knee joint. The proposed method utilizes RHE to acquire ultrashort echo time (UTE) and subsequent gradient echo images at 16 different echo times ranging between 40 μs and 30 ms in a single scan. In the proposed RHE technique, UTE imaging was followed by acquisition of 14 gradient recalled echo images, where an additional UTE image was obtained within the first readout by oversampling single point imaging (SPI) encoding. The single scan RHE method with a 9‐minute scan time was performed on human cadaveric knee joints from six donors and in vivo knee joints from four healthy volunteers at 3 T. A bicomponent signal model was used to characterize the short T2* and long T2* water components. Mean bicomponent T2* parameters for patellar tendon, anterior cruciate ligament (ACL), posterior cruciate ligament (PCL) and meniscus were calculated. In the experimental results, the RHE technique provided bicomponent T2* parameter estimations of tendon, ACL, PCL and meniscus, which were similar to previously reported values in the literature. In conclusion, the proposed single scan RHE technique provides rapid bicomponent T2* analysis of the human knee joint with a total scan time of less than 9 minutes. [ABSTRACT FROM AUTHOR]

Objectives: The relationship between structural abnormalities and inflammatory changes in the synovial fluid following ACL injury has not been well defined. The purpose of this study is to compare MRI findings and concentration of inflammatory biomarkers in the synovial fluid (SF) of acute and chronic anterior cruciate ligament (ACL) injury patients. Methods: Patients undergoing surgery for ACL injury from October 2011 to December 2021 were included in a database of patients who had SF samples collected at the time of surgery. From this cohort, 82 patients with acute ACL injury and 28 patients with chronic ACL injury were identified. All patients underwent a preoperative MRI examination of the knee and SF was aspirated from the operative knee prior to the surgical incision and concentrations of inflammatory biomarkers were quantified. MRI examinations were reviewed by a fellowship-trained musculoskeletal radiologist to assess radiographic features of acute and chronic knee injury using the validated ACLOAS system. Nonparametric tests were used to compare demographic factors, MRI findings, clinical symptoms, and SF inflammatory biomarkers between acute versus chronic ACL injury patients. A generalized linear model was used to assess the relationship of each SF biomarker with the ACLOAs subscores. Results: For acute ACL injury patients, mean time from injury to MRI and injury to surgery was 14 ± 35 days and 49 ± 38 days, respectively. The mean time from injury to MRI and injury to surgery was 3.2 ± 3.7 years and 3.4 ± 3.8 years, respectively, for those with chronic ACL injury. There were no significant demographic differences between groups; age (acute: 31.62 ± 8.02 years, chronic: 33.70 ± 10.60 years, p=0.130), sex (acute: 51.2% males, chronic: 57.1%, p=0.588), or body mass index (acute: 5.5 ± 4.4 kg/mm2, chronic: 25.3 ± 3.6 kg/mm2, p=0.931). Acute ACL injury patients had higher bone marrow edema severity scores (p<0.001), Hoffa's synovitis score (p=0.002), and effusion severity scores (p<0.001). Chronic ACL injury patients had higher cartilage degeneration severity scores (p<0.001), prevalence of meniscus tears (p=0.003), and meniscus tear severity scores (p<0.001). Acute ACL injury patients had significantly higher synovial fluid levels of IL-6 (p=0.032), VEGF (p=0.002), TIMP1 (p<0.001), IL1-RA (p=0.003), and MMP3 (p<0.001) than chronic ACL injury patients. The generalized linear model revealed that after adjusting for age, sex, BMI, and time from injury to surgery, increased VEGF (p=0.003) and MMP3 (p=0.012) were associated with higher effusion score. MMP-3 was associated with a higher bone marrow edema score (p=0.034), and RANTES was associated with both a higher meniscus severity score (p=0.027) and increased cartilage lesions in the chronic ACL injury cohort. Conclusions: The findings from this study suggest that despite having more severe cartilage degeneration and a higher prevalence of meniscus pathology, chronic ACL injury patients had lower levels of synovial fluid inflammatory biomarkers at time of surgery compared to acute ACL injury patients. Additionally, we found that in chronic injuries, RANTES was associated with worse ACLOAS cartilage scores. [ABSTRACT FROM AUTHOR]

Objectives: To Investigate the association between MRI findings following anterior cruciate ligament (ACL) injury and synovial fluid biomarker profile at the time of surgery Methods: Patients undergoing surgery for ACL injury from October 2011 to December 2021 were included in a database of subjects who had synovial fluid samples collected at the time of surgery. From this cohort, 82 subjects with acute ACL injury were retrospectively selected who met the following inclusion criteria: no history of prior knee trauma to suggest chronic ACL injury, history of acute non- contact knee injury, MRI examination of the knee performed within 3 weeks of knee injury, subsequent knee surgery performed between 4 and 12 weeks of knee injury, and no additional ligament tear requiring surgical treatment. All subjects underwent a preoperative MRI examination of the knee using multi-planar, multi-contrast fast spin-echo sequences. Synovial fluid was aspirated from the operative knee prior to surgical incision. Concentrations of inflammatory biomarkers including regulation on activation normal T cell expressed and secreted (RANTES), interleukin 6 (IL-6), vascular endothelial growth factor (VEGF), tissue inhibitor matrix metalloproteinase 1 (TIMP1), interleukin 1 receptor agonist (IL1-RA) and matrix metalloproteinase 3 (MMP-3) were measured. The MRI examinations of all subjects were reviewed by a fellowship trained musculoskeletal radiologist to assess the presence and severity of various MRI features of acute and chronic knee injury using the validated Anterior Cruciate Ligament Osteoarthritis Score (ACLOAS) system. Nonparametric Wilcoxon tests and Fisher's exact test were used to compare demographic factors, MRI findings, clinical symptoms, and synovial fluid inflammatory biomarkers between clusters. K-means cluster analysis was used to identify 2 distinct clusters of subjects with acute ACL injury based on biomarkers concentrations in synovial fluid. Results: The mean age of the cohort was 31.6 ± 8 years, average body mass index (BMI) was 25.5 ± 4.4 kg/m2. There 42.7% (n=35) of the cohort was female and 85.4% (n=70) were non-smokers. Two distinct clusters were identified: cluster 1 included 45 subjects and was marked by older subjects (33.2 ± 9.1 years, p=.038) with lower levels of all inflammatory synovial fluid biomarkers. Cluster 2 included 37 subjects and was marked by younger patients (29.7 ± 6.1 years, p=.038) with a more pro-inflammatory synovial fluid biomarker profile, higher ACLOAS Hoffa's synovitis score (p=.001), and a higher incidence of subchondral bone fractures (54%, p=.002). There were no differences between clusters in time from injury to surgery (p=), BMI (p=.141), or ACLOAS meniscus (p=.086), cartilage (p=.730), or effusion scores (p=.202) Conclusions: Given the variable outcomes seen following ACL reconstruction (ACLR) surgery, a better understanding of the pathophysiology following ACL injury is needed. This study found distinct cluster of patients who exhibited a higher inflammatory reaction at the time of surgery as well as more severe findings on preoperative MRI. These findings could be used to identified clinical parameters associated with more inflammation at the time of surgery and their effect on clinical outcomes of ACLR. [ABSTRACT FROM AUTHOR]

Background: Despite the advantages of 3D MRI in evaluation of cruciate ligament injuries, its use in clinical practice is still a matter of debate due to controversy regarding its diagnostic performance.Purpose: To evaluate the diagnostic performance of 3D MRI for detecting cruciate ligament injuries, using surgery or arthroscopy as the reference standard.Study Type: Meta-analysis.Population: Patients with knee pain.Field Strength/sequence: 3D and 2D MRI.Assessment: Four databases were reviewed according to PRISMA guidelines.Statistical Tests: Pooled values of sensitivity, specificity, and diagnostic odds ratio (DOR) were calculated using a random-effects model. To investigate the effect of relevant covariates on the diagnostic performance of 3D MRI, sensitivity analysis was performed using meta-regression to calculate relative DOR.Results: Of 731 initially identified reports, 22 (1298 3D MRI examinations) met our criteria and were included. Pooled estimates of sensitivity and specificity for 3D sequences were 91.4% (95% confidence interval [CI]: 87.4-94.2%) and 96.1% (95% CI: 93.8-97.6%), respectively. Fourteen studies also reported the results of 2D MRI, with pooled sensitivity of 90.6% (95% CI: 84.1-94.6%) and specificity of 97.1% (95% CI: 94.7-98.4%), which were not significantly different from 3D sequences. 3D MRI sequences performed using 3T scanners had significantly higher DOR compared with 3D sequences performed on 1.5T or lower scanners (relative DOR: 6.04, P = 0.01).Data Conclusion: 3D MRI is equivalent to 2D MRI in the diagnosis of cruciate ligament injuries. The use of 3T scanners improves the performance of 3D MRI for detecting cruciate ligament injuries.Level Of Evidence: 2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;50:1545-1560. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a novel deep learning–based reconstruction framework called SANTIS (Sampling‐Augmented Neural neTwork with Incoherent Structure) for efficient MR image reconstruction with improved robustness against sampling pattern discrepancy. Methods: With a combination of data cycle–consistent adversarial network, end‐to‐end convolutional neural network mapping, and data fidelity enforcement for reconstructing undersampled MR data, SANTIS additionally utilizes a sampling‐augmented training strategy by extensively varying undersampling patterns during training, so that the network is capable of learning various aliasing structures and thereby removing undersampling artifacts more effectively and robustly. The performance of SANTIS was demonstrated for accelerated knee imaging and liver imaging using a Cartesian trajectory and a golden‐angle radial trajectory, respectively. Quantitative metrics were used to assess its performance against different references. The feasibility of SANTIS in reconstructing dynamic contrast‐enhanced images was also demonstrated using transfer learning. Results: Compared to conventional reconstruction that exploits image sparsity, SANTIS achieved consistently improved reconstruction performance (lower errors and greater image sharpness). Compared to standard learning‐based methods without sampling augmentation (e.g., training with a fixed undersampling pattern), SANTIS provides comparable reconstruction performance, but significantly improved robustness, against sampling pattern discrepancy. SANTIS also achieved encouraging results for reconstructing liver images acquired at different contrast phases. Conclusion: By extensively varying undersampling patterns, the sampling‐augmented training strategy in SANTIS can remove undersampling artifacts more robustly. The novel concept behind SANTIS can particularly be useful for improving the robustness of deep learning–based image reconstruction against discrepancy between training and inference, an important, but currently less explored, topic. [ABSTRACT FROM AUTHOR]

Purpose: To develop and evaluate a novel deep learning‐based image reconstruction approach called MANTIS (Model‐Augmented Neural neTwork with Incoherent k‐space Sampling) for efficient MR parameter mapping. Methods: MANTIS combines end‐to‐end convolutional neural network (CNN) mapping, incoherent k‐space undersampling, and a physical model as a synergistic framework. The CNN mapping directly converts a series of undersampled images straight into MR parameter maps using supervised training. Signal model fidelity is enforced by adding a pathway between the undersampled k‐space and estimated parameter maps to ensure that the parameter maps produced synthesized k‐space consistent with the acquired undersampling measurements. The MANTIS framework was evaluated on the T2 mapping of the knee at different acceleration rates and was compared with 2 other CNN mapping methods and conventional sparsity‐based iterative reconstruction approaches. Global quantitative assessment and regional T2 analysis for the cartilage and meniscus were performed to demonstrate the reconstruction performance of MANTIS. Results: MANTIS achieved high‐quality T2 mapping at both moderate (R = 5) and high (R = 8) acceleration rates. Compared to conventional reconstruction approaches that exploited image sparsity, MANTIS yielded lower errors (normalized root mean square error of 6.1% for R = 5 and 7.1% for R = 8) and higher similarity (structural similarity index of 86.2% at R = 5 and 82.1% at R = 8) to the reference in the T2 estimation. MANTIS also achieved superior performance compared to direct CNN mapping and a 2‐step CNN method. Conclusion: The MANTIS framework, with a combination of end‐to‐end CNN mapping, signal model‐augmented data consistency, and incoherent k‐space sampling, is a promising approach for efficient and robust estimation of quantitative MR parameters. [ABSTRACT FROM AUTHOR]

Purpose: To describe and evaluate a new segmentation method using deep convolutional neural network (CNN), 3D fully connected conditional random field (CRF), and 3D simplex deformable modeling to improve the efficiency and accuracy of knee joint tissue segmentation. Methods: A segmentation pipeline was built by combining a semantic segmentation CNN, 3D fully connected CRF, and 3D simplex deformable modeling. A convolutional encoder‐decoder network was designed as the core of the segmentation method to perform high resolution pixel‐wise multi‐class tissue classification for 12 different joint structures. The 3D fully connected CRF was applied to regularize contextual relationship among voxels within the same tissue class and between different classes. The 3D simplex deformable modeling refined the output from 3D CRF to preserve the overall shape and maintain a desirable smooth surface for joint structures. The method was evaluated on 3D fast spin‐echo (3D‐FSE) MR image data sets. Quantitative morphological metrics were used to evaluate the accuracy and robustness of the method in comparison to the ground truth data. Results: The proposed segmentation method provided good performance for segmenting all knee joint structures. There were 4 tissue types with high mean Dice coefficient above 0.9 including the femur, tibia, muscle, and other non‐specified tissues. There were 7 tissue types with mean Dice coefficient between 0.8 and 0.9 including the femoral cartilage, tibial cartilage, patella, patellar cartilage, meniscus, quadriceps and patellar tendon, and infrapatellar fat pad. There was 1 tissue type with mean Dice coefficient between 0.7 and 0.8 for joint effusion and Baker's cyst. Most musculoskeletal tissues had a mean value of average symmetric surface distance below 1 mm. Conclusion: The combined CNN, 3D fully connected CRF, and 3D deformable modeling approach was well‐suited for performing rapid and accurate comprehensive tissue segmentation of the knee joint. The deep learning‐based segmentation method has promising potential applications in musculoskeletal imaging. [ABSTRACT FROM AUTHOR]

Purpose: To describe and evaluate a new fully automated musculoskeletal tissue segmentation method using deep convolutional neural network (CNN) and three-dimensional (3D) simplex deformable modeling to improve the accuracy and efficiency of cartilage and bone segmentation within the knee joint. Methods: A fully automated segmentation pipeline was built by combining a semantic segmentation CNN and 3D simplex deformable modeling. A CNN technique called SegNet was applied as the core of the segmentation method to perform high resolution pixel-wise multi-class tissue classification. The 3D simplex deformable modeling refined the output from SegNet to preserve the overall shape and maintain a desirable smooth surface for musculoskeletal structure. The fully automated segmentation method was tested using a publicly available knee image data set to compare with currently used state-of-the-art segmentation methods. The fully automated method was also evaluated on two different data sets, which include morphological and quantitative MR images with different tissue contrasts. Results: The proposed fully automated segmentation method provided good segmentation performance with segmentation accuracy superior to most of state-of-the-art methods in the publicly available knee image data set. The method also demonstrated versatile segmentation performance on both morphological and quantitative musculoskeletal MR images with different tissue contrasts and spatial resolutions. Conclusion: The study demonstrates that the combined CNN and 3D deformable modeling approach is useful for performing rapid and accurate cartilage and bone segmentation within the knee joint. The CNN has promising potential applications in musculoskeletal imaging. [ABSTRACT FROM AUTHOR]

Background: Although knees that have undergone anterior cruciate ligament reconstruction (ACLR) often exhibit normal laxity on clinical examination, abnormal kinematic patterns have been observed when the joint is dynamically loaded during whole body activity. This study investigated whether abnormal knee kinematics arise with loading under isolated dynamic movements. Hypothesis: Tibiofemoral and patellofemoral kinematics of ACLR knees will be similar to those of the contralateral uninjured control knee during passive flexion-extension, with bilateral differences emerging when an inertial load is applied. Study Design: Controlled laboratory study. Methods: The bilateral knees of 18 subjects who had undergone unilateral ACLR within the past 4 years were imaged by use of magnetic resonance imaging (MRI). Their knees were cyclically (0.5 Hz) flexed passively. Subjects then actively flexed and extended their knees against an inertial load that induced stretch-shortening quadriceps contractions, as seen during the load acceptance phase of gait. A dynamic, volumetric, MRI sequence was used to track tibiofemoral and patellofemoral kinematics through 6 degrees of freedom. A repeated-measures analysis of variance was used to compare secondary tibiofemoral and patellofemoral kinematics between ACLR and healthy contralateral knees during the passive and active extension phases of the cyclic motion. Results: Relative to the passive motion, inertial loading induced significant shifts in anterior and superior tibial translation, internal tibial rotation, and all patellofemoral degrees of freedom. As hypothesized, tibiofemoral and patellofemoral kinematics were bilaterally symmetric during the passive condition. However, inertial loading induced bilateral differences, with the ACLR knees exhibiting a significant shift toward external tibial rotation. A trend toward greater medial and anterior tibial translation was seen in the ACLR knees. Conclusion: This study demonstrates that abnormal knee kinematic patterns in ACLR knees emerge during a simple, active knee flexion-extension task that can be performed in an MRI scanner. Clinical Relevance: It is hypothesized that abnormal knee kinematics may alter cartilage loading patterns and thereby contribute to increased risk for osteoarthritis. Recent advances in quantitative MRI can be used to detect early cartilage degeneration in ACLR knees. This study demonstrates the feasibility of identifying abnormal ACLR kinematics by use of dynamic MRI, supporting the combined use of dynamic and quantitative MRI to investigate the proposed link between knee motion, cartilage contact, and early biomarkers of cartilage degeneration. [ABSTRACT FROM AUTHOR]

Purpose: To compare bicomponent ultrashort echo time (UTE) T2* parameters of patellar tendon between healthy volunteers and patients with patellar tendinopathy.Materials and Methods: This study was performed with Institutional Review Board approval and with all subjects signing informed consent. A UTE- T2* mapping sequence was performed at 3.0T on the knees of 10 healthy volunteers and in 11 patients with patellar tendinopathy. The UTE- T2* relaxation times of the fast relaxing macromolecular bound water component ( T2*F) and the slow relaxing bulk water component ( T2*S) and the fraction of the fast relaxing macromolecular bound water component (FF ) of patellar tendon were measured in all subjects. Wilcoxon rank-sum tests were used to compare UTE- T2* parameters between healthy volunteers and patients with patellar tendinopathy.Results: Mean T2*F, T2*S, and FF of the patellar tendon was 1.5 msec, 23.1 msec, and 79.5%, respectively, for healthy volunteers and 1.9 msec, 22.3 msec, and 75.5%, respectively, for patients with patellar tendinopathy. There were statistically significant differences between groups of subjects for T2*F (P = 0.01) and FF (P = 0.007) but not T2*S (P = 0.10) of the patellar tendon.Conclusion: Patients with patellar tendinopathy had significantly higher T2*F and significantly lower FF of patellar tendon than healthy volunteers, which suggests that bicomponent UTE- T2* parameters can detect changes in the composition and microstructure of degenerative tendon.Level Of Evidence: 1 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2017;46:1441-1447. [ABSTRACT FROM AUTHOR]

Objective: To compare magnetic resonance imaging (MRI) characteristics of torn and untorn post-operative menisci.Methods: The study group consisted of 140 patients with 148 partially resected menisci who were evaluated with a repeat knee MRI examination and subsequent repeat arthroscopic knee surgery. Two musculoskeletal radiologists retrospectively assessed the following MRI characteristics of the post-operative meniscus: contour (smooth or irregular), T2 line through the meniscus (no line, intermediate signal line, intermediate-to-high signal line, and high fluid-like signal line), displaced meniscus fragment, and change in signal pattern through the meniscus compared with baseline MRI. Positive predictive values (PPV) and negative predictive values (NPV) were calculated using arthroscopy as the reference standard.Results: All 36 post-operative menisci with no T2 line were untorn at surgery (100% NPV), whereas 46 of the 79 post-operative menisci with intermediate T2 line, 16 of the 18 post-operative menisci with intermediate-to-high T2 line, and 14 of the 15 post-operative menisci with high T2 line were torn at surgery (58.2%, 88.9%, and 93.3% PPV respectively). Additional MRI characteristics associated with torn post-operative meniscus at surgery were irregular meniscus contour (PPV 85.7%), displaced meniscus fragment (PPV 100%), and change in signal pattern through the meniscus (PPV 99.4%).Conclusions: Post-operative menisci with no T2 signal line were untorn at surgery. The most useful MRI characteristics for predicting torn post-operative menisci at surgery were change in signal pattern through the meniscus compared with baseline MRI, and displaced meniscus fragment followed by high T2 line through the meniscus, intermediate-to-high T2 line through the meniscus, and irregular meniscus contour. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of using compressed sensing (CS) to accelerate three-dimensional fast spin-echo (3D-FSE) imaging of the knee.Materials and Methods: A 3D-FSE sequence was performed at 3T with CS (CUBE-CS with 3:16-minute scan time) and without CS (CUBE with 4:44-minute scan time) twice on the knees of 10 healthy volunteers to assess signal-to-noise ratio (SNR) using the addition-subtraction method and once on the knees of 50 symptomatic patients to assess diagnostic performance. SNR of cartilage, muscle, synovial fluid, and bone marrow on CUBE and CUBE-CS images were measured in the 10 healthy volunteers. The CUBE and CUBE-CS sequences of all 50 symptomatic patients were independently reviewed twice by two musculoskeletal radiologists. The radiologists used CUBE and CUBE-CS during each individual review to determine the presence or absence of knee joint pathology. Student's t-tests were used to compare SNR values between sequences, while the kappa statistic was used to determine agreement between sequences for detecting knee joint pathology. Sensitivity and specificity of CUBE and CUBE-CS for detecting knee joint pathology was also calculated in the 18 symptomatic patients who underwent subsequent arthroscopic knee surgery.Results: CUBE and CUBE-CS had similar SNR (P = 0.15-0.67) of cartilage, muscle, synovial fluid, and bone marrow. There was near-perfect to perfect agreement between CUBE and CUBE-CS for both radiologists for detecting cartilage and bone marrow edema lesions, medial and lateral meniscus tears, anterior cruciate ligament tears, effusions, and intra-articular bodies. CUBE and CUBE-CS had similar sensitivity (75.0-100%) and specificity (87.5-100%) for detecting 60 cartilage lesions, 20 meniscus tears, four anterior cruciate ligament tears, and four intra-articular bodies confirmed at surgery.Conclusion: CS provided a 30% reduction in scan time for 3D-FSE imaging of the knee without a corresponding decrease in SNR or diagnostic performance.Level Of Evidence: 1 J. MAGN. RESON. IMAGING 2017;45:1712-1722. [ABSTRACT FROM AUTHOR]

We present MRiLab, a new comprehensive simulator for large-scale realistic MRI simulations on a regular PC equipped with a modern graphical processing unit (GPU). MRiLab combines realistic tissue modeling with numerical virtualization of an MRI system and scanning experiment to enable assessment of a broad range of MRI approaches including advanced quantitative MRI methods inferring microstructure on a sub-voxel level. A flexible representation of tissue microstructure is achieved in MRiLab by employing the generalized tissue model with multiple exchanging water and macromolecular proton pools rather than a system of independent proton isochromats typically used in previous simulators. The computational power needed for simulation of the biologically relevant tissue models in large 3D objects is gained using parallelized execution on GPU. Three simulated and one actual MRI experiments were performed to demonstrate the ability of the new simulator to accommodate a wide variety of voxel composition scenarios and demonstrate detrimental effects of simplified treatment of tissue micro-organization adapted in previous simulators. GPU execution allowed $\sim 200\times $ improvement in computational speed over standard CPU. As a cross-platform, open-source, extensible environment for customizing virtual MRI experiments, MRiLab streamlines the development of new MRI methods, especially those aiming to infer quantitatively tissue composition and microstructure. [ABSTRACT FROM AUTHOR]

Purpose To compare the reliability and stability of the multicomponent-driven equilibrium single pulse observation of T1 and T2 (mcDESPOT) and Carl-Purcell-Meiboom-Gill (CPMG) approaches to parameter estimation. Methods The stability and reliability of mcDESPOT and CPMG-derived parameter estimates were compared through examination of energy surfaces, evaluation of model sloppiness, and Monte Carlo simulations. Comparisons were performed on an equal time basis and assuming a two-component system. Parameter estimation bias, reflecting accuracy, and dispersion, reflecting precision, were derived for a range of signal-to-noise ratios (SNRs) and relaxation parameters. Results The energy surfaces for parameters incorporated into the mcDESPOT signal model exhibit flatness, a complex structure of local minima, and instability to noise to a much greater extent than the corresponding surfaces for CPMG. Although both mcDESPOT and CPMG performed well at high SNR, the CPMG approach yielded parameter estimates of considerably greater accuracy and precision at lower SNR. Conclusion mcDESPOT and CPMG both permit high-quality parameter estimates under SNR that are clinically achievable under many circumstances, depending upon available hardware and resolution and acquisition time constraints. At moderate to high SNR, the mcDESPOT approach incorporating two-step phase increments can yield accurate parameter estimates while providing values for longitudinal relaxation times that are not available through CPMG. However, at low SNR, the CPMG approach is more stable and provides superior parameter estimates. Magn Reson Med 75:2406-2420, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the use of a three-pool model to account for the confounding effects of synovial fluid on multicomponent T2 analysis of articular cartilage using Multicomponent Driven Equilibrium Single Shot Observation of T1 and T2 (mcDESPOT). Materials and Methods: mcDESPOT was performed on the knee of eight asymptomatic volunteers and eight patients with osteoarthritis at 3.0T with multicomponent T2 maps created using the two-pool model and a three-pool model containing a nonexchanging synovial fluid water pool. The fraction of the fast-relaxing water component (FF ) and the T2 relaxation times for the fast-relaxing (T2F ) and slow-relaxing (T2S ) water components were measured in the superficial and deep layers of patellar cartilage using the two-pool and three-pool models in asymptomatic volunteers and patients with osteoarthritis and were compared using Wilcoxon signed rank tests. Results: Within the superficial layer of patellar cartilage, FF was 22.5% and 25.6% for asymptomatic volunteers and 21.3% and 22.8% for patients with osteoarthritis when using the two-pool and three-pool models, respectively, while T2S was 73.9 msec and 62.0 msec for asymptomatic volunteers and 72.0 msec and 63.1 msec for patients with osteoarthritis when using the two-pool and three-pool models, respectively. For both asymptomatic volunteers and patients with osteoarthritis, the two-pool model provided significantly (P < 0.05) lower FF and higher T2S than the three-pool model, likely due to the effects of synovial fluid partial volume averaging. Conclusion: The effects of partial volume averaging between superficial cartilage and synovial fluid may result in biased multicomponent T2 measurements that can be corrected using an mcDESPOT three-pool model containing a nonexchanging synovial fluid water pool. [ABSTRACT FROM AUTHOR]

Purpose: To study the effects of magnetization transfer (MT) on multicomponent T2 parameters obtained using mcDESPOT in macromolecule-rich tissues and to propose a new method called mcRISE to correct MT-induced biases. Methods: The two-pool mcDESPOT model was modified by the addition of an exchanging macromolecule proton pool to model the MT effect in cartilage. The mcRISE acquisition scheme was developed to provide sensitivity to all pools. An incremental fitting was applied to estimate MT and relaxometry parameters with minimized coupling. The interaction between MT and relaxometry parameters, efficacy of MT correction, and feasibility of mcRISE in vivo were investigated in simulations and in healthy volunteers. Results: The MT effect caused significant errors in multicomponent T1/T2 values and in fast-relaxing water fraction fF, which is consistent with previous experimental observations. fF increased significantly with macromolecule content if MT was ignored. mcRISE resulted in a multifold reduction of MT biases and yielded decoupled multicomponent T1/T2 relaxometry and quantitative MT parameters. Conclusion: mcRISE is an efficient approach for correcting MT biases in multicomponent relaxometry based on steady state sequences. Improved specificity of mcRISE may help to elucidate the sources of the previously described high sensitivity of noncorrected mcDESPOT parameters to diseaserelated changes in cartilage and the brain. [ABSTRACT FROM AUTHOR]

Purpose Chemical shift based fat/water decomposition methods such as IDEAL are frequently used in challenging imaging environments with large B0 inhomogeneity. However, they do not account for the signal modulations introduced by a balanced steady state free precession (bSSFP) acquisition. Here we demonstrate improved performance when the bSSFP frequency response is properly incorporated into the multipeak spectral fat model used in the decomposition process. Theory and Methods Balanced SSFP allows for rapid imaging but also introduces a characteristic frequency response featuring periodic nulls and pass bands. Fat spectral components in adjacent pass bands will experience bulk phase offsets and magnitude modulations that change the expected constructive and destructive interference between the fat spectral components. A bSSFP signal model was incorporated into the fat/water decomposition process and used to generate images of a fat phantom, and bilateral breast and knee images in four normal volunteers at 1.5 Tesla. Results Incorporation of the bSSFP signal model into the decomposition process improved the performance of the fat/water decomposition. Conclusion Incorporation of this model allows rapid bSSFP imaging sequences to use robust fat/water decomposition methods such as IDEAL. While only one set of imaging parameters were presented, the method is compatible with any field strength or repetition time. Magn Reson Med 75:1269-1277, 2016. © 2015 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: To compare multicomponent T2 parameters of menisci measured using Multicomponent Driven Equilibrium Single Pulse Observation of T1 and T2 (mcDESPOT) in asymptomatic volunteers and osteoarthritis (OA) patients with intact and torn menisci.Materials and Methods: The prospective study was performed with Institutional Review Board approval and with all subjects signing written informed consent. mcDESPOT was performed on the knee joint of 12 asymptomatic volunteers and 14 patients with knee OA. Single-component T2 relaxation time (T2Single ), T2 relaxation time of the fast relaxing water component (T2F ), and the slow relaxing water component (T2S ), and fraction of the fast relaxing water component (FF ) of the medial and lateral menisci were measured. Multivariate linear regression models were used to compare mcDESPOT parameters between normal menisci in asymptomatic volunteers, intact menisci in OA patients, and torn menisci in OA patients with adjustment for differences in age between subjects.Results: The mean mcDESPOT parameters for normal menisci in asymptomatic volunteers, intact menisci in OA patients, and torn menisci in OA patients were respectively 16.1 msec, 18.8 msec, and 22.7 msec for T2Single ; 9.0 msec, 10.0 msec, and 11.1 msec for T2F ; 24.4 msec, 27.7 msec, and 31.4 msec for T2S ; and 34%, 32%, 27% for FF . There were significant differences (P < 0.05) in T2Single , T2F , T2S , and FF between the three groups of menisci.Conclusion: The menisci of OA patients had significantly higher T2Single , T2F , and T2S and significantly lower FF than normal menisci in asymptomatic volunteers with greater changes in multicomponent T2 parameters noted in torn than intact menisci in OA patients. [ABSTRACT FROM AUTHOR]

Purpose: The collagen structure throughout the patella has not been thoroughly investigated by 3D imaging, where the majority of the existing data come from histological cross sections. It is important to have a better understanding of the architecture in normal tissues, where this could then be applied to imaging of diseased states. Methods: To address this shortcoming, we investigated the combined use of collagen-specific Second-Harmonic Generation (SHG) imaging and measurement of bulk optical properties to characterize collagen fiber orientations of the histologically defined zones of bovine articular cartilage. Forward and backward SHG intensities of sections from superficial, middle and deep zones were collected as a function of depth and analyzed by Monte Carlo simulations to extract the SHG creation direction, which is related to the fibrillar assembly. Results: Our results revealed differences in SHG forward-backward response between the three zones, where these are consistent with a previously developed model of SHG emission. Some of the findings are consistent with that from other modalities; however, SHG analysis showed the middle zone had the most organized fibril assembly. While not distinct, we also report bulk optical property values for these different zones within the patella. Conclusions: Collectively, these results provide quantitative measurements of structural changes at both the fiber and fibril assembly of the different cartilage zones and reveals structural information not possible by other microscope modalities. This can provide quantitative insight to the collagen fiber network in normal cartilage, which may ultimately be developed as a biomarker for osteoarthritis. [ABSTRACT FROM AUTHOR]

Purpose To compare a balanced steady-state free-precession sequence with a radial k-space trajectory and alternating repetition time fat suppression (Radial-ATR) with other currently used fat-suppressed 3D sequences for evaluating the articular cartilage of the knee joint at 3.0T. Materials and Methods Radial-ATR, fast spin-echo (FSE-Cube), gradient recall-echo acquired in the steady-state (GRASS), and spoiled gradient recall-echo (SPGR) sequences with similar voxel volumes and identical scan times were performed at 3.0T on both knee joints of five volunteers. Signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) measurements were performed for all sequences using a double acquisition method and compared using Mann-Whitney Wilcoxon tests. Radial-ATR sequences with 0.3 mm and 0.4 mm isotropic resolution were also performed on the knee joints of seven volunteers and three patients with osteoarthritis. Results Average SNR values for cartilage, synovial fluid, and bone marrow were 54.7, 153.3, and 12.9, respectively, for Radial ATR, 30.8, 44.1, and 1.9, respectively, for FSE-Cube, 13.3, 46.9, and 3.3, respectively, for GRASS, and 19.1, 8.1, and 2.1, respectively, for SPGR. Average CNR values between cartilage and synovial fluid and between cartilage and bone marrow were 98.6 and 41.8, respectively, for VIPR-ATR, 13.4 and 28.8, respectively, for FSE-Cube, 33.6 and 10.0, respectively, for GRASS, and 11.0 and 16.9, respectively, for SPGR. Radial-ATR had significantly higher ( P < 0.001) cartilage, synovial fluid, and bone marrow SNR and significantly higher ( P < 0.01) CNR between cartilage and synovial fluid and between cartilage and bone marrow than FSE-Cube, GRASS, and SPGR. Radial-ATR provided excellent visualization of articular cartilage at high isotropic resolution with no image degradation due to off-resonance banding artifacts. Conclusion Radial-ATR had superior SNR efficiency to other fat-suppressed 3D cartilage imaging sequences and produced high isotropic resolution images of the knee joint which could be used for evaluating articular cartilage at 3.0T. J. Magn. Reson. Imaging 2014;40:796-803. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Purpose: To optimize acquisition parameters for three dimensional fast spin-echo (3D FSE) imaging of the knee.Materials and Methods: The knees of eight healthy volunteers were imaged in a 3 Tesla MRI scanner using an eight-channel knee coil. A total of 146 intermediate weighted isotropic resolution 3D FSE (3D-FSE-Cube)images with varied acquisition parameter settings were acquired with an additional reference scan performed for subjective image quality assessment. Images were graded for overall quality, parallel imaging artifact severity and blurring. Cartilage, muscle, and fluid signal-to-noise ratios and fluid-cartilage contrast-to-noise ratios were quantified by acquiring scans without radio frequency excitation and custom-reconstructing the k-space data.Mixed effects regression modeling was used to determine statistically significant effects of different parameters on image quality.Results: Changes in receiver bandwidth, repetition time and echo train length significantly affected all measurements of image quality (P < 0.05). Reducing band width improved all metrics of image quality with the exception of blurring. Reader agreement was slight to fair for subjective metrics, but overall trends in quality ratings were apparent.Conclusion: We used a systematic approach to optimize 3D-FSE-Cube parameters for knee imaging. Image quality was overall improved using a receiver bandwidth of 631.25 kHz, and blurring increased with lower band width and longer echo trains. [ABSTRACT FROM AUTHOR]

Purpose To optimize acquisition parameters for three-dimensional fast spin-echo (3D FSE) imaging of the knee. Materials and Methods The knees of eight healthy volunteers were imaged in a 3 Tesla MRI scanner using an eight-channel knee coil. A total of 146 intermediate-weighted isotropic resolution 3D FSE (3D-FSE-Cube) images with varied acquisition parameter settings were acquired with an additional reference scan performed for subjective image quality assessment. Images were graded for overall quality, parallel imaging artifact severity and blurring. Cartilage, muscle, and fluid signal-to-noise ratios and fluid-cartilage contrast-to-noise ratios were quantified by acquiring scans without radiofrequency excitation and custom-reconstructing the k-space data. Mixed effects regression modeling was used to determine statistically significant effects of different parameters on image quality. Results Changes in receiver bandwidth, repetition time and echo train length significantly affected all measurements of image quality ( P < 0.05). Reducing bandwidth improved all metrics of image quality with the exception of blurring. Reader agreement was slight to fair for subjective metrics, but overall trends in quality ratings were apparent. Conclusion We used a systematic approach to optimize 3D-FSE-Cube parameters for knee imaging. Image quality was overall improved using a receiver bandwidth of ±31.25 kHz, and blurring increased with lower bandwidth and longer echo trains. J. Magn. Reson. Imaging 2014;39:1417-1425. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Objectives: This study determined whether injection with hypertonic dextrose and morrhuate sodium (prolotherapy) using a pragmatic, clinically determined injection schedule for knee osteoarthritis (KOA) results in improved knee pain, function, and stiffness compared to baseline status. Design: This was a prospective three-arm uncontrolled study with 1-year follow-up. Setting: The setting was outpatient. Participants: The participants were 38 adults who had at least 3 months of symptomatic KOA and who were in the control groups of a prior prolotherapy randomized controlled trial (RCT) (Prior-Control), were ineligible for the RCT (Prior-Ineligible), or were eligible but declined the RCT (Prior-Declined). Intervention: The injection sessions at occurred at 1, 5, and 9 weeks with as-needed treatment at weeks 13 and 17. Extra-articular injections of 15% dextrose and 5% morrhuate sodium were done at peri-articular tendon and ligament insertions. A single intra-articular injection of 6 mL 25% dextrose was performed through an inferomedial approach. Outcome measures: The primary outcome measure was the validated Western Ontario McMaster University Osteoarthritis Index (WOMAC). The secondary outcome measure was the Knee Pain Scale and postprocedure opioid medication use and participant satisfaction. Results: The Prior-Declined group reported the most severe baseline WOMAC score ( p=0.02). Compared to baseline status, participants in the Prior-Control group reported a score change of 12.4±3.5 points (19.5%, p=0.002). Prior-Decline and Prior-Ineligible groups improved by 19.4±7.0 (42.9%, p=0.05) and 17.8±3.9 (28.4%, p=0.008) points, respectively; 55.6% of Prior-Control, 75% of Prior-Decline, and 50% of Prior-Ineligible participants reported score improvement in excess of the 12-point minimal clinical important difference on the WOMAC measure. Postprocedure opioid medication resulted in rapid diminution of prolotherapy injection pain. Satisfaction was high and there were no adverse events. Conclusions: Prolotherapy using dextrose and morrhuate sodium injections for participants with mild-to-severe KOA resulted in safe, significant, sustained improvement of WOMAC-based knee pain, function, and stiffness scores compared to baseline status. [ABSTRACT FROM AUTHOR]

Purpose To determine the feasibility of using multicomponent-driven equilibrium single-shot observation of T1 and T2 (mcDESPOT) for evaluating the human knee joint at 3.0T and to investigate depth-dependent and regional-dependent variations in multicomponent T2 parameters within articular cartilage. Materials and Methods mcDESPOT was performed on the knee joint of 10 asymptomatic volunteers at 3.0T. Single-component T2 relaxation time (T2single), multicomponent T2 relaxation time for water tightly bound to proteoglycan (T2PG) and bulk water loosely bound to the macromolecular matrix (T2BW), and fraction of water tightly bound to proteoglycan (FPG) were measured in eight cartilage subsections and within the superficial and deep layers of patellar cartilage. Statistical analysis was used to investigate depth-dependent and regional-dependent variations in parameters. Results There was lower ( P = 0.001) T2single and T2PG and higher ( P < 0.001) FPG in the deep than superficial layer of patellar cartilage. There was higher ( P < 0.001) FPG on the weight-bearing surfaces than nonweight-bearing surfaces. There was higher ( P < 0.001) T2single, T2PG, and T2BW on the trochlea and posterior medial and lateral femoral condyles than the patella, central medial and lateral femoral condyles, and medial and lateral tibia plateaus. Conclusion Multicomponent T2 parameters of the articular cartilage of the human knee joint can be measured at 3.0T using mcDESPOT and show depth-dependent and regional-dependent variations. J. Magn. Reson. Imaging 2014;39:1191-1197. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

Radial trajectories facilitate high-resolution balanced steady state free precession (bSSFP) because the efficient gradients provide more time to extend the trajectory in k-space. A number of radial bSSFP methods that support fat-water separation have been developed; however, most of these methods require an environment with limited B0 inhomogeneity. In this work, high-resolution bSSFP with fat-water separation is achieved in more challenging B0 environments by combining a 3D radial trajectory with the IDEAL chemical species separation method. A method to maintain very high resolution within the timing constraints of bSSFP and IDEAL is described using a dual-pass pulse sequence. The sampling of a unique set of radial lines at each echo time is investigated as a means to circumvent the longer scan time that IDEAL incurs as a multiecho acquisition. The manifestation of undersampling artifacts in this trajectory and their effect on chemical species separation are investigated in comparison to the case in which each echo samples the same set of radial lines. This new bSSFP method achieves 0.63 mm isotropic resolution in a 5-min scan and is demonstrated in difficult in vivo imaging environments, including the breast and a knee with ACL reconstruction hardware at 1.5 T. Magn Reson Med 71:95-104, 2014. © 2013 Wiley Periodicals, Inc. [ABSTRACT FROM AUTHOR]

STUDY DESIGN: Single-cohort descriptive and correlational study. OBJECTIVES: To investigate the relationships between tendon pathology, biomechanical measures, and self-reported pain and function in individuals with chronic lateral epicondylosis. BACKGROUND: Lateral epicondylosis has a multifactorlal etiology and its pathophysiology is not well understood. Consequently, treatment remains challenging, and lateral epicondylosis is prone to recurrence. While tendon pathology, pain system changes, and motor impairments due to lateral epicondylosis are considered related, their relationships have not been thoroughly investigated METHODS: Twenty-six participants with either unilateral (n = 11) or bilateral (n = 15) chronic lateral epicondylosis participated in this study. Biomechanical measures (grip strength, rate of force development, and electromechanical delay) and measures of tendon pathology (magnetic resonance imaging and ultrasound) and self-reported pain and function (Patient-Rated Tennis Elbow Evaluation) were performed. Partial Spearman correlations, adjusting for covariates (age, gender, weight, and height), were used to evaluate the relationship between self-reported pain, function, and biomechanical and tendon pathology measures. RESULTS: Statistically significant correlations between biomechanical measures and the Patient-Rated Tennis Elbow Evaluation ranged in magnitude from 0.44 to 0.68 (P<.05); however, no significant correlation was observed between tendon pathology (magnetic resonance imaging and ultrasound) measures and the Patient-Rated Tennis Elbow Evaluation (r = -0.02 to 0.31, P>.05). Rate of force development had a stronger correlation (r = 0.54-0.68, P<.05) with self-reported function score than with grip strength (r = 0.35- 0.47 P<.05) or electromechanical delay (r = 0.5, P<.05). CONCLUSION: Biomechanical measures (pain-free grip strength, rate of force development, electromechanical delay) have the potential to be used as outcome measures to monitor progress in lateral epicondylosis. In comparison, the imaging measures (magnetic resonance imaging and ultrasound) were useful for visualizing the pathophysiology of lateral epicondylosis. However, the severity of the pathophysiology was not related to pain and function, indicating that imaging measures may not provide the best clinical assessment. [ABSTRACT FROM AUTHOR]