Automated evaluation of hip abductor muscle quality and size in hip osteoarthritis: Localized muscle regions are strongly associated with overall muscle quality.

Limited information exists regarding abductor muscle quality variation across its length and which locations are most representative of overall muscle quality. This is exacerbated by time-intensive processes for manual muscle segmentation, which limits feasibility of large cohort analyses. The purpose of this study was to develop an automated and localized analysis pipeline that accurately estimates hip abductor muscle quality and size in individuals with mild-to-moderate hip osteoarthritis (OA) and identifies regions of each muscle which provide best estimates of overall muscle quality. Forty-four participants (age 52.7 ±â€¯16.1 years, BMI 23.7 ±â€¯3.4 kg/m2, 14 males) with and without mild-to-moderate radiographic hip OA were recruited for this study. Unilateral hip magnetic resonance (MR) images were acquired on a 3.0 T MR scanner and included axial T1-weighted fast spin echo and 3D axial Iterative Decomposition of water and fat with Echo Asymmetry and Least-squares estimation (IDEAL-IQ) spoiled gradient-recalled echo (SPGR) with multi-peak fat spectrum modeling and single T2* correction. A three dimensional (3D) V-Net convolutional neural network was trained to automatically segment the gluteus medius (GMED), gluteus minimus (GMIN), and tensor fascia lata (TFL) on axial IDEAL-IQ. Agreement between manual and automatic segmentation and associations between axial fat fraction (FF) estimated from IDEAL-IQ and overall muscle FF were evaluated. Dice scores for automatic segmentation were 0.94, 0.87, and 0.91 for GMED, GMIN, and TFL, respectively. GMED, GMIN, and TFL volumetric and FF measures were strongly correlated (r: 0.92-0.99) between automatic and manual segmentations, with 95% limits of agreement of [-1.99%, 2.89%] and [-9.79 cm3, 17.43 cm3], respectively. Axial FF was significantly associated with overall FF with the strongest correlations at 50%, 50%, and 65% the length of the GMED, GMIN, and TFL muscles, respectively (r: 0.93-0.97). An automated and localized analysis can provide efficient and accurate estimates of hip abductor muscle quality and size across muscle length. Specific regions of the muscle may be used to estimate overall muscle quality in an abbreviated evaluation of muscle quality.

Machine learning-based automated scan prescription of lumbar spine MRI acquisitions.

PURPOSE: High quality scan prescription that optimally covers the area of interest with scan planes aligned to relevant anatomical structures is crucial for error-free radiologic interpretation. The goal of this project was to develop a machine learning pipeline for oblique scan prescription that could be trained on localizer images and metadata from previously acquired MR exams. METHODS: A novel Multislice Rotational Region-based Convolutional Neural Network (MS-R2CNN) architecture was developed. Based on this architecture, models for automated prescription sagittal lumbar spine acquisitions from axial, sagittal, and coronal localizer slices were trained. The automated prescription pipeline was integrated with the scanner console software and evaluated in experiments with healthy volunteers (N = 3) and patients with lower-back pain (N = 20). RESULTS: Experiments in healthy volunteers demonstrated high accuracy of automated prescription in all subjects. There was good agreement between alignment and coverage of manual and automated prescriptions, as well as consistent views of the lumbar spine at different positions of the subjects within the scanner bore. In patients with lower-back pain, the generated prescription was applied in 18 cases (90% of the total number). None of the cases required major adjustment, while in 11 cases (55%) there were minor manual adjustments to the generated prescription. CONCLUSIONS: This study demonstrates the ability of oriented object detection-based models to be trained to prescribe oblique lumbar spine MRI acquisitions without the need of manual annotation or feature engineering and the feasibility of using machine learning-based pipelines on the scanner for automated prescription of MRI acquisitions.

Thresholding approaches for estimating paraspinal muscle fat infiltration using T1- and T2-weighted MRI: Comparative analysis using water-fat MRI.

Background: Paraspinal muscle fat infiltration is associated with spinal degeneration and low back pain, however, quantifying muscle fat using clinical magnetic resonance imaging (MRI) techniques continues to be a challenge. Advanced MRI techniques, including chemical-shift encoding (CSE) based water-fat MRI, enable accurate measurement of muscle fat, but such techniques are not widely available in routine clinical practice. Methods: To facilitate assessment of paraspinal muscle fat using clinical imaging, we compared four thresholding approaches for estimating muscle fat fraction (FF) using T1- and T2-weighted images, with measurements from water-fat MRI as the ground truth: Gaussian thresholding, Otsu's method, K-mean clustering, and quadratic discriminant analysis. Pearson's correlation coefficients (r), mean absolute errors, and mean bias errors were calculated for FF estimates from T1- and T2-weighted MRI with water-fat MRI for the lumbar multifidus (MF), erector spinae (ES), quadratus lumborum (QL), and psoas (PS), and for all muscles combined. Results: We found that for all muscles combined, FF measurements from T1- and T2-weighted images were strongly positively correlated with measurements from the water-fat images for all thresholding techniques (r = 0.70-0.86, p < 0.0001) and that variations in inter-muscle correlation strength were much greater than variations in inter-method correlation strength. Conclusion: We conclude that muscle FF can be quantified using thresholded T1- and T2-weighted MRI images with relatively low bias and absolute error in relation to water-fat MRI, particularly in the MF and ES, and the choice of thresholding technique should depend on the muscle and clinical MRI sequence of interest.

The knee connectome: A novel tool for studying spatiotemporal change in cartilage thickness.

Cartilage thickness change is a well-documented biomarker of osteoarthritis pathogenesis. However, there is still much to learn about the spatial and temporal patterns of cartilage thickness change in health and disease. In this study, we develop a novel analysis method for elucidating such patterns using a functional connectivity approach. Descriptive statistics are reported for 1186 knees that did not develop osteoarthritis during the 8 years of observation, which we present as a model of cartilage thickness change related to healthy aging. Within the control population, patterns vary greatly between male and female subjects, while body mass index (BMI) has a more moderate impact. Finally, several differences are shown between knees that did and did not develop osteoarthritis. Some but not all significance appears to be accounted for by differences in sex, BMI, and knee alignment. With this work, we present the connectome as a novel tool for studying spatiotemporal dynamics of tissue change.

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

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

Arthritis Foundation/HSS Workshop on Hip Osteoarthritis, Part 2: Detecting Hips at Risk: Early Biomechanical and Structural Mechanisms.

Far more publications are available for osteoarthritis of the knee than of the hip. Recognizing this research gap, the Arthritis Foundation (AF), in partnership with the Hospital for Special Surgery (HSS), convened an in-person meeting of thought leaders to review the state of the science of and clinical approaches to hip osteoarthritis. This article summarizes the recommendations gleaned from 5 presentations given in the "early hip osteoarthritis" session of the 2023 Hip Osteoarthritis Clinical Studies Conference, which took place on February 17 and 18, 2023, in New York City. It also summarizes the workgroup recommendations from a small-group discussion on clinical research gaps.

Chondral Injury Associated With ACL Injury: Assessing Progressive Chondral Degeneration With Morphologic and Quantitative MRI Techniques.

BACKGROUND: Anterior cruciate ligament (ACL) injuries are associated with a risk of post-traumatic osteoarthritis due to chondral damage. Magnetic resonance imaging (MRI) techniques provide excellent visualization and assessment of cartilage and can detect subtle and early chondral damage. This is often preceding clinical and radiographic post-traumatic osteoarthritis. HYPOTHESIS: Morphologic and quantitative MRI techniques can assess early and progressive degenerative chondral changes after acute ACL injury. STUDY DESIGN: Prospective longitudinal cohort. LEVEL OF EVIDENCE: Level 3. METHODS: Sixty-five participants with acute unilateral ACL injuries underwent bilateral knee MRI scans within 1 month of injury. Fifty-seven participants presented at 6 months, while 54 were evaluated at 12 months. MRI morphologic evaluation using a modified Noyes score assessed cartilage signal alteration, chondral damage, and subchondral bone status. Quantitative T1ρ and T2 mapping at standardized anatomic locations in both knees was assessed. Participant-reported outcomes at follow-up time points were recorded. RESULTS: Baseline Noyes scores of MRI detectable cartilage damage were highest in the injured knee lateral tibial plateau (mean 2.5, standard error (SE) 0.20, P < 0.01), followed by lateral femoral condyle (mean 2.1, SE 0.18, P < 0.01), which progressed after 1 year. Longitudinal prolongation at 12 months in the injured knees was significant for T1ρ affecting the medial and lateral femoral condyles (P < 0.01) and trochlea (P < 0.01), whereas T2 values were prolonged for medial and lateral femoral condyles (P < 0.01) and trochlea (P < 0.01). The contralateral noninjured knees also demonstrated T1ρ and T2 prolongation in the medial and lateral compartment chondral subdivisions. Progressive chondral damage occurred despite improved patient-reported outcomes. CONCLUSION: After ACL injury, initial and sustained chondral damage predominantly affects the lateral tibiofemoral compartment, but longitudinal chondral degeneration also occurred in other compartments of the injured and contralateral knee. CLINICAL RELEVANCE: Early identification of chondral degeneration post-ACL injury using morphological and quantitative MRI techniques could enable interventions to be implemented early to prevent or delay PTOA.

Comparing bone shape models from deep learning processing of magnetic resonance imaging to computed tomography-based models.

Background: The purpose of this study was to develop a deep learning approach to automatically segment the scapular bone on magnetic resonance imaging (MRI) images and to compare the accuracy of these three-dimensional (3D) models with that of 3D computed tomography (CT). Methods: Fifty-five patients with high-resolution 3D fat-saturated T2 MRI were retrospectively identified. The underlying pathology included rotator cuff tendinopathy and tears, shoulder instability, and impingement. Two experienced musculoskeletal researchers manually segmented the scapular bone. Five cross-validation training and validation splits were generated to independently train two-dimensional (2D) and 3D models using a convolutional neural network approach. Model performance was evaluated using the Dice similarity coefficient (DSC). All models with DSC > 0.70 were ensembled and used for the test set, which consisted of four patients with matching high-resolution MRI and CT scans. Clinically relevant glenoid measurements, including glenoid height, width, and retroversion, were calculated for two of the patients. Paired t-tests and Wilcoxon signed-rank tests were used to compare the DSC of the models. Results: The 2D and 3D models achieved a best DSC of 0.86 and 0.82, respectively, with no significant difference observed. Augmentation of imaging data significantly improved 3D but not 2D model performance. In comparing clinical measurements of 3D MRI and CT, there was a mean difference ranging from 1.29 mm to 3.46 mm and 0.05° to 7.47°. Conclusion: We have presented a fully automatic, deep learning-based strategy for extracting scapular shape from a high-resolution MRI scan. Further developments of this technology have the potential to allow for surgeons to obtain all clinically relevant information from MRI scans and reduce the need for multiple imaging studies for patients with shoulder pathology.

Effects of T1p Characteristics of Load-Bearing Hip Cartilage on Bilateral Knee Patellar Cartilage Subregions: Subjects With None to Moderate Radiographic Hip Osteoarthritis.

BACKGROUND: The polyarticular nature of Osteoarthritis (OA) tends to manifest in multi-joints. Associations between cartilage health in connected joints can help identify early degeneration and offer the potential for biomechanical intervention. Such associations between hip and knee cartilages remain understudied. PURPOSE: To investigate T1p associations between hip-femoral and acetabular-cartilage subregions with Intra-limb and Inter-limb patellar cartilage; whole and deep-medial (DM), deep-lateral (DL), superficial-medial (SM), superficial-lateral (SL) subregions. STUDY TYPE: Prospective. SUBJECTS: Twenty-eight subjects (age 55.1 ± 12.8 years, 15 females) with none-to-moderate hip-OA while no radiographic knee-OA. FIELD STRENGTH/SEQUENCE: 3-T, bilateral hip, and knee: 3D-proton-density-fat-saturated (PDFS) Cube and Magnetization-Prepared-Angle-Modulated-Partitioned-k-Space-Spoiled-Gradient-Echo-Snapshots (MAPSS). ASSESSMENT: Ages of subjects were categorized into Group-1 (≤40), Group-2 (41-50), Group-3 (51-60), Group-4 (61-70), Group-5 (71-80), and Group-6 (≥81). Hip T1p maps, co-registered to Cube, underwent an atlas-based algorithm to quantify femoral and acetabular subregional (R2 -R7 ) cartilage T1p . For knee Cube, a combination of V-Net architectures was used to segment the patellar cartilage and subregions (DM, DL, SM, SL). T1p values were computed from co-registered MAPSS. STATISTICAL TESTS: For Intra-and-Inter-limb, 5 optimum predictors out of 13 (Hip subregional T1p , age group, gender) were selected by univariate linear-regression, to predict outcome (patellar T1p ). The top five predictors were stepwise added to six linear mixed-effect (LME) models. In all LME models, we assume the data come from the same subject sharing the same random effect. The best-performing models (LME-modelbest ) selected via ANOVA, were tested with DM, SM, SL, and DL subregional-mean T1p . LME assumptions were verified (normality of residuals, random-effects, and posterior-predictive-checks). RESULTS: LME-modelbest (Intra-limb) had significant negative and positive fixed-effects of femoral-R5 and acetabular-R2 T1p , respectively (conditional-R2 = 0.581). LME-modelbest (Inter-limb) had significant positive fixed-effects of femoral-R3 T1p (conditional-R2 = 0.26). DATA CONCLUSION: Significant positive and negative T1p associations were identified between load-bearing hip cartilage-subregions vs. ipsilateral and contralateral patellar cartilages respectively. The effects were localized on medial subregions of Inter-limb, in particular. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 1.

MRI Advancements in Musculoskeletal Clinical and Research Practice.

Over the past decades, MRI has become increasingly important for diagnosing and longitudinally monitoring musculoskeletal disorders, with ongoing hardware and software improvements aiming to optimize image quality and speed. However, surging demand for musculoskeletal MRI and increased interest to provide more personalized care will necessitate a stronger emphasis on efficiency and specificity. Ongoing hardware developments include more powerful gradients, improvements in wide-bore magnet designs to maintain field homogeneity, and high-channel phased-array coils. There is also interest in low-field-strength magnets with inherently lower magnetic footprints and operational costs to accommodate global demand in middle- and low-income countries. Previous approaches to decrease acquisition times by means of conventional acceleration techniques (eg, parallel imaging or compressed sensing) are now largely overshadowed by deep learning reconstruction algorithms. It is expected that greater emphasis will be placed on improving synthetic MRI and MR fingerprinting approaches to shorten overall acquisition times while also addressing the demand of personalized care by simultaneously capturing microstructural information to provide greater detail of disease severity. Authors also anticipate increased research emphasis on metal artifact reduction techniques, bone imaging, and MR neurography to meet clinical needs.

Associations between weight change, knee subcutaneous fat and cartilage thickness in overweight and obese individuals: 4-Year data from the osteoarthritis initiative.

OBJECTIVE: To assess (i) the impact of changes in body weight on changes in joint-adjacent subcutaneous fat (SCF) and cartilage thickness over 4 years and (ii) the relation between changes in joint-adjacent SCF and knee cartilage thickness. DESIGN: Individuals from the Osteoarthritis Initiative (total=399) with > 10% weight gain (n=100) and > 10% weight loss (n=100) over 4 years were compared to a matched control cohort with less than 3% change in weight (n=199). 3.0T Magnetic Resonance Imaging (MRI) of the right knee was performed at baseline and after 4 years to quantify joint-adjacent SCF and cartilage thickness. Linear regression models were used to evaluate the associations between the (i) weight change group and 4-year changes in both knee SCF and cartilage thickness, and (ii) 4-year changes in knee SCF and in cartilage thickness. Analyses were adjusted for age, sex, baseline body mass index (BMI), tibial diameter (and weight change group in analysis (ii)). RESULTS: Individuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p < 0.001) than controls; individuals who gained weight had significantly greater joint-adjacent SCF than controls (beta range: -1.4 to -3.9 mm, p < 0.001). No statistically significant associations were found between weight change and cartilage thickness change. However, increases in joint-adjacent SCF over 4 years were significantly associated with decreases in cartilage thickness (p = 0.04). CONCLUSIONS: Weight change was associated with joint-adjacent SCF, but not with change in cartilage thickness. However, 4-year increases in joint-adjacent SCF were associated with decreases in cartilage thickness independent of baseline BMI and weight change group.

Large-Scale Analysis of Meniscus Morphology as Risk Factor for Knee Osteoarthritis.

OBJECTIVE: Although it is established that structural damage of the meniscus is linked to knee osteoarthritis (OA) progression, the predisposition to future development of OA because of geometric meniscal shapes is plausible and unexplored. This study aims to identify common variations in meniscal shape and determine their relationships to tissue morphology, OA onset, and longitudinal changes in cartilage thickness. METHODS: A total of 4,790 participants from the Osteoarthritis Initiative data set were studied. A statistical shape model was developed for the meniscus, and shape scores were evaluated between a control group and an OA incidence group. Shape features were then associated with cartilage thickness changes over 8 years to localize the relationship between meniscus shape and cartilage degeneration. RESULTS: Seven shape features between the medial and lateral menisci were identified to be different between knees that remain normal and those that develop OA. These include length-width ratios, horn lengths, root attachment angles, and concavity. These "at-risk" shapes were linked to unique cartilage thickness changes that suggest a relationship between meniscus geometry and decreased tibial coverage and rotational imbalances. Additionally, strong associations were found between meniscal shape and demographic subpopulations, future tibial extrusion, and meniscal and ligamentous tears. CONCLUSION: This automatic method expanded upon known meniscus characteristics that are associated with the onset of OA and discovered novel shape features that have yet to be investigated in the context of OA risk.

Synthetic Inflammation Imaging with PatchGAN Deep Learning Networks.

Background: Gadolinium (Gd)-enhanced Magnetic Resonance Imaging (MRI) is crucial in several applications, including oncology, cardiac imaging, and musculoskeletal inflammatory imaging. One use case is rheumatoid arthritis (RA), a widespread autoimmune condition for which Gd MRI is crucial in imaging synovial joint inflammation, but Gd administration has well-documented safety concerns. As such, algorithms that could synthetically generate post-contrast peripheral joint MR images from non-contrast MR sequences would have immense clinical utility. Moreover, while such algorithms have been investigated for other anatomies, they are largely unexplored for musculoskeletal applications such as RA, and efforts to understand trained models and improve trust in their predictions have been limited in medical imaging. Methods: A dataset of 27 RA patients was used to train algorithms that synthetically generated post-Gd IDEAL wrist coronal T1-weighted scans from pre-contrast scans. UNets and PatchGANs were trained, leveraging an anomaly-weighted L1 loss and global generative adversarial network (GAN) loss for the PatchGAN. Occlusion and uncertainty maps were also generated to understand model performance. Results: UNet synthetic post-contrast images exhibited stronger normalized root mean square error (nRMSE) than PatchGAN in full volumes and the wrist, but PatchGAN outperformed UNet in synovial joints (UNet nRMSEs: volume = 6.29 ± 0.88, wrist = 4.36 ± 0.60, synovial = 26.18 ± 7.45; PatchGAN nRMSEs: volume = 6.72 ± 0.81, wrist = 6.07 ± 1.22, synovial = 23.14 ± 7.37; n = 7). Occlusion maps showed that synovial joints made substantial contributions to PatchGAN and UNet predictions, while uncertainty maps showed that PatchGAN predictions were more confident within those joints. Conclusions: Both pipelines showed promising performance in synthesizing post-contrast images, but PatchGAN performance was stronger and more confident within synovial joints, where an algorithm like this would have maximal clinical utility. Image synthesis approaches are therefore promising for RA and synthetic inflammatory imaging.

Persistent underloading of patellofemoral joint following hamstring autograft ACL reconstruction is associated with cartilage health.

OBJECTIVE: To determine the longitudinal changes of patellofemoral joint (PFJ) contact pressure following anterior cruciate ligament reconstruction (ACLR). To identify the associations between PFJ contact pressure and cartilage health. DESIGN: Forty-nine subjects with hamstring autograft ACLR (27 males; age 28.8 [standard deviation, 8.3] years) and 19 controls (12 males; 30.7 [4.6] years) participated. A sagittal plane musculoskeletal model was used to estimate PFJ contact pressure. A combined T1ρ/T2 magnetic resonance sequence was obtained. Assessments were performed preoperatively, at 6 months, 1, 2, and 3 years postoperatively in ACLR subjects and once for controls. Repeated Analysis of Variance (ANOVA) was used to compare peak PFJ contact pressure between ACLR and contralateral knees, and t-tests to compare with control knees. Statistical parametric mapping was used to evaluate the associations between PFJ contact pressure and cartilage relaxation concurrently and longitudinally. RESULTS: No changes in peak PFJ contact pressure were found within ACLR knees over 3 years (preoperative to 3 years, 0.36 [CI, -0.08, 0.81] MPa), but decreased over time in the contralateral knees (0.75 [0.32, 1.18] MPa). When compared to the controls, ACLR knees exhibited lower PFJ contact pressure at all time points (at baseline, -0.64 [-1.25, -0.03] MPa). Within ACLR knees, lower PFJ contact pressure at 6 months was associated with elevated T2 times (r = -0.47 to -0.49, p = 0.021-0.025). CONCLUSIONS: Underloading of the PFJ following ACLR persists for up to 3 years and has concurrent and future consequences in cartilage health. The non-surgical knees exhibited normal contact pressure initially but decreased over time achieving limb symmetry.

Technology and Tool Development for BACPAC: Qualitative and Quantitative Analysis of Accelerated Lumbar Spine MRI with Deep-Learning Based Image Reconstruction at 3T.

OBJECTIVES: To evaluate whether combining fast acquisitions with deep-learning reconstruction can provide diagnostically useful images and quantitative assessment comparable to standard-of-care acquisitions for lumbar spine magnetic resonance imaging (MRI). METHODS: Eighteen patients were imaged with both standard protocol and fast protocol using reduced signal averages, each protocol including sagittal fat-suppressed T2-weighted, sagittal T1-weighted, and axial T2-weighted 2D fast spin-echo sequences. Fast-acquisition data was additionally reconstructed using vendor-supplied deep-learning reconstruction with three different noise reduction factors. For qualitative analysis, standard images as well as fast images with and without deep-learning reconstruction were graded by three radiologists on five different categories. For quantitative analysis, convolutional neural networks were applied to sagittal T1-weighted images to segment intervertebral discs and vertebral bodies, and disc heights and vertebral body volumes were derived. RESULTS: Based on noninferiority testing on qualitative scores, fast images without deep-learning reconstruction were inferior to standard images for most categories. However, deep-learning reconstruction improved the average scores, and noninferiority was observed over 24 out of 45 comparisons (all with sagittal T2-weighted images while 4/5 comparisons with sagittal T1-weighted and axial T2-weighted images). Interobserver variability increased with 50 and 75% noise reduction factors. Deep-learning reconstructed fast images with 50% and 75% noise reduction factors had comparable disc heights and vertebral body volumes to standard images (r2≥ 0.86 for disc heights and r2≥ 0.98 for vertebral body volumes). CONCLUSIONS: This study demonstrated that deep-learning-reconstructed fast-acquisition images have the potential to provide noninferior image quality and comparable quantitative assessment to standard clinical images.

Deep learning for automated, interpretable classification of lumbar spinal stenosis and facet arthropathy from axial MRI.

OBJECTIVES: To evaluate a deep learning model for automated and interpretable classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy from lumbar spine MRI. METHODS: T2-weighted axial MRI studies of the lumbar spine acquired between 2008 and 2019 were retrospectively selected (n = 200) and graded for central canal stenosis, neural foraminal stenosis, and facet arthropathy. Studies were partitioned into patient-level train (n = 150), validation (n = 20), and test (n = 30) splits. V-Net models were first trained to segment the dural sac and the intervertebral disk, and localize facet and foramen using geometric rules. Subsequently, Big Transfer (BiT) models were trained for downstream classification tasks. An interpretable model for central canal stenosis was also trained using a decision tree classifier. Evaluation metrics included linearly weighted Cohen's kappa score for multi-grade classification and area under the receiver operator characteristic curve (AUROC) for binarized classification. RESULTS: Segmentation of the dural sac and intervertebral disk achieved Dice scores of 0.93 and 0.94. Localization of foramen and facet achieved intersection over union of 0.72 and 0.83. Multi-class grading of central canal stenosis achieved a kappa score of 0.54. The interpretable decision tree classifier had a kappa score of 0.80. Pairwise agreement between readers (R1, R2), (R1, R3), and (R2, R3) was 0.86, 0.80, and 0.74. Binary classification of neural foraminal stenosis and facet arthropathy achieved AUROCs of 0.92 and 0.93. CONCLUSION: Deep learning systems can be performant as well as interpretable for automated evaluation of lumbar spine MRI including classification of central canal stenosis, neural foraminal stenosis, and facet arthropathy. KEY POINTS: • Interpretable deep-learning systems can be developed for the evaluation of clinical lumbar spine MRI. Multi-grade classification of central canal stenosis with a kappa of 0.80 was comparable to inter-reader agreement scores (0.74, 0.80, 0.86). Binary classification of neural foraminal stenosis and facet arthropathy achieved favorable and accurate AUROCs of 0.92 and 0.93, respectively. • While existing deep-learning systems are opaque, leading to clinical deployment challenges, the proposed system is accurate as well as interpretable, providing valuable information to a radiologist in clinical practice.

Trajectory of knee health in runners with and without heightened osteoarthritis risk: the TRAIL prospective cohort study protocol.

INTRODUCTION: Running is one of the most popular recreational activities worldwide, due to its low cost and accessibility. However, little is known about the impact of running on knee joint health in runners with and without a history of knee surgery. The primary aim of this longitudinal cohort study is to compare knee joint structural features on MRI and knee symptoms at baseline and 4-year follow-up in runners with and without a history of knee surgery. Secondary aims are to explore the relationships between training load exposures (volume and/or intensity) and changes in knee joint structure and symptoms over 4 years; explore the relationship between baseline running biomechanics, and changes in knee joint structure and symptoms over 4 years. In addition, we will explore whether additional variables confound, modify or mediate these associations, including sex, baseline lower-limb functional performance, knee muscle strength, psychological and sociodemographic factors. METHODS AND ANALYSIS: A convenience sample of at least 200 runners (sex/gender balanced) with (n=100) and without (n=100) a history of knee surgery will be recruited. Primary outcomes will be knee joint health (MRI) and knee symptoms (baseline; 4 years). Exposure variables for secondary outcomes include training load exposure, obtained daily throughout the study from wearable devices and three-dimensional running biomechanics (baseline). Additional variables include lower limb functional performance, knee extensor and flexor muscle strength, biomarkers, psychological and sociodemographic factors (baseline). Knowledge and beliefs about osteoarthritis will be obtained through predefined questions and semi-structured interviews with a subset of participants. Multivariable logistic and linear regression models, adjusting for potential confounding factors, will explore changes in knee joint structural features and symptoms, and the influence of potential modifiers and mediators. ETHICS AND DISSEMINATION: Approved by the La Trobe University Ethics Committee (HEC-19524). Findings will be disseminated to stakeholders, peer-review journals and conferences.

K2S Challenge: From Undersampled K-Space to Automatic Segmentation.

Magnetic Resonance Imaging (MRI) offers strong soft tissue contrast but suffers from long acquisition times and requires tedious annotation from radiologists. Traditionally, these challenges have been addressed separately with reconstruction and image analysis algorithms. To see if performance could be improved by treating both as end-to-end, we hosted the K2S challenge, in which challenge participants segmented knee bones and cartilage from 8× undersampled k-space. We curated the 300-patient K2S dataset of multicoil raw k-space and radiologist quality-checked segmentations. 87 teams registered for the challenge and there were 12 submissions, varying in methodologies from serial reconstruction and segmentation to end-to-end networks to another that eschewed a reconstruction algorithm altogether. Four teams produced strong submissions, with the winner having a weighted Dice Similarity Coefficient of 0.910 ± 0.021 across knee bones and cartilage. Interestingly, there was no correlation between reconstruction and segmentation metrics. Further analysis showed the top four submissions were suitable for downstream biomarker analysis, largely preserving cartilage thicknesses and key bone shape features with respect to ground truth. K2S thus showed the value in considering reconstruction and image analysis as end-to-end tasks, as this leaves room for optimization while more realistically reflecting the long-term use case of tools being developed by the MR community.

The effect of interactions between BMI and sustained depressive symptoms on knee osteoarthritis over 4 years: data from the osteoarthritis initiative.

BACKGROUND: To assess the compound effects of BMI and sustained depressive symptoms on changes in knee structure, cartilage composition, and knee pain over 4 years using statistical interaction analyses. METHODS: One thousand eight hundred forty-four individuals from the Osteoarthritis Initiative Database were analyzed at baseline and 4-year follow-up. Individuals were categorized according to their BMI and presence of depressive symptoms (based on the Center for Epidemiological Studies Depression Scale (threshold≥16)) at baseline and 4-year follow-up. 3 T MRI was used to quantify knee cartilage T2 over 4 years, while radiographs were used to assess joint space narrowing (JSN). Mixed effects models examined the effect of BMI-depressive symptoms interactions on outcomes of cartilage T2, JSN, and knee pain over 4-years. RESULTS: The BMI-depressive symptoms interaction was significantly associated with knee pain (p < 0.001) changes over 4 years, but not with changes in cartilage T2 (p = 0.27). In women, the BMI-depressive symptoms interaction was significantly associated with JSN (p = 0.01). In a group-based analysis, participants with obesity and depression had significantly greater 4-year changes in knee pain (coeff.(obesity + depression vs. no_obesity + no_depression) = 4.09, 95%CI = 3.60-4.58, p < 0.001), JSN (coeff. = 0.60, 95%CI = 0.44-0.77, p < 0.001), and cartilage T2 (coeff. = 1.09, 95%CI = 0.68-1.49, p < 0.001) than participants without depression and normal BMI. CONCLUSIONS: The compound effects of obesity and depression have greater impact on knee pain and JSN progression compared to what would be expected based on their individual effects.

The Back Pain Consortium (BACPAC) Research Program: Structure, Research Priorities, and Methods.

In 2019, the National Health Interview survey found that nearly 59% of adults reported pain some, most, or every day in the past 3 months, with 39% reporting back pain, making back pain the most prevalent source of pain, and a significant issue among adults. Often, identifying a direct, treatable cause for back pain is challenging, especially as it is often attributed to complex, multifaceted issues involving biological, psychological, and social components. Due to the difficulty in treating the true cause of chronic low back pain (cLBP), an over-reliance on opioid pain medications among cLBP patients has developed, which is associated with increased prevalence of opioid use disorder and increased risk of death. To combat the rise of opioid-related deaths, the National Institutes of Health (NIH) initiated the Helping to End Addiction Long-TermSM (HEAL) initiative, whose goal is to address the causes and treatment of opioid use disorder while also seeking to better understand, diagnose, and treat chronic pain. The NIH Back Pain Consortium (BACPAC) Research Program, a network of 14 funded entities, was launched as a part of the HEAL initiative to help address limitations surrounding the diagnosis and treatment of cLBP. This paper provides an overview of the BACPAC research program's goals and overall structure, and describes the harmonization efforts across the consortium, define its research agenda, and develop a collaborative project which utilizes the strengths of the network. The purpose of this paper is to serve as a blueprint for other consortia tasked with the advancement of pain related science.