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Articular cartilage damage and degeneration are among hallmark manifestations of joint injuries and arthritis, classically osteoarthritis. Cartilage compositional MRI (Cart-C MRI), a quantitative technique, which aims to detect early-stage cartilage matrix changes that precede macroscopic alterations, began development in the 1990s. However, despite the significant advancements over the past three decades, Cart-C MRI remains predominantly a research tool, hindered by various technical and clinical hurdles. This paper will review the technical evolution of Cart-C MRI, delve into its clinical applications, and conclude by identifying the existing gaps and challenges that need to be addressed to enable even broader clinical application of Cart-C MRI.

BackgroundAdults undergoing spine surgery often have underlying osteoporosis, which may be a risk factor for postoperative complications. Although these associations have been described, osteoporosis remains profoundly underdiagnosed and undertreated in the spine surgery population. A thorough, comprehensive systematic review summarizing the relationships between bone mineral density (BMD) and specific complications of lumbar fusion surgery could be a valuable resource for raising awareness and supporting clinical practice changes.MethodsPubMed, Embase, and Web of Science databases were searched for original clinical research articles reporting on BMD, or surrogate measure, as a predictor of complications in adults undergoing elective lumbar fusion for degenerative disease or deformity. Endpoints included cage subsidence, screw loosening, pseudarthrosis, vertebral fracture, junctional complications, and reoperation.ResultsA total of 71 studies comprising 12,278 patients were included. Overall, considerable heterogeneity in study populations, methods of bone health assessment, and definition and evaluation of clinical endpoints precluded meta-analysis. Nevertheless, low BMD was associated with higher rates of implant failures like cage subsidence and screw loosening, which were often diagnosed with concomitant pseudarthrosis. Osteoporosis was also a significant risk factor for proximal junctional kyphosis, particularly due to fracture. Many studies found surgical site-specific BMD to best predict focal complications. Functional outcomes were inconsistently addressed.ConclusionsOur findings suggest osteoporosis is a significant risk factor for mechanical complications of lumbar fusion. These results emphasize the importance of preoperative osteoporosis screening, which allows for medical and surgical optimization of high-risk patients. This review also highlights current practical challenges facing bone health evaluation in patients undergoing elective surgery. Future prospective studies using standardized methods are necessary to strengthen existing evidence, identify optimal predictive thresholds, and establish specialty-specific practice guidelines. In the meantime, an awareness of the surgical implications of osteoporosis and utility of preoperative screening can provide for more informed, effective patient care.

BACKGROUND: Meniscal root tears can lead to early knee osteoarthritis and pain. This study aimed (1) to compare clinical and radiological outcomes between patients who underwent arthroscopic meniscal root repair after meniscal root tears and those who received non-surgical treatment, and (2) to identify whether baseline MRI findings could be potential predictors for future treatment strategies. METHODS: Patients with meniscal root tears were identified from our picture archiving and communication system from 2016 to 2020. Two radiologists reviewed radiographs and MRI studies using Kellgren-Lawrence (KL) grading and a modified Whole Organ MRI Scoring (WORMS) at baseline and follow-up. The median (interquartile range [IQR]) of follow-up radiographs and MRI studies were 134 (44-443) days and 502 (260-1176) days, respectively. MR images were assessed for root tear-related findings. Pain scores using visual analogue scale (VAS) and management strategies (non-surgical vs. arthroscopic root repair) were also collected. Chi-squared tests and independent t-tests were used to assess differences regarding clinical and imaging variables between treatment groups. Logistic regression analyses were performed to evaluate the associations between baseline MRI findings and each future treatment. RESULTS: Ninety patients were included. VAS pain scores were significantly (p

BackgroundObesity influences the development of osteoarthritis via low-grade inflammation. Progression of local inflammation (= synovitis) increased with weight gain in overweight and obese women compared to stable weight. Synovitis could be associated with subcutaneous fat (SCF) around the knee. Purpose of the study was to investigate the effect of weight loss on synovitis progression and to assess whether SCF around the knee mediates the relationship between weight loss and synovitis progression.MethodsWe included 234 overweight and obese participants (body mass index [BMI] ≥ 25 kg/m2) from the Osteoarthritis Initiative (OAI) with > 10% weight loss (n = 117) or stable overweight (

ObjectiveTo 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.DesignIndividuals 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)).ResultsIndividuals who lost weight over 4-years had significantly less joint-adjacent SCF (beta range, medial/lateral joint sides: 2.2-4.2 mm, p

Management of patients suffering from low back pain (LBP) is challenging and requires development of diagnostic techniques to identify specific patient subgroups and phenotypes in order to customize treatment and predict clinical outcome. The Back Pain Consortium (BACPAC) Research Program Spine Imaging Working Group has developed standard operating procedures (SOPs) for spinal imaging protocols to be used in all BACPAC studies. These SOPs include procedures to conduct spinal imaging assessments with guidelines for standardizing the collection, reading/grading (using structured reporting with semi-quantitative evaluation using ordinal rating scales), and storage of images. This article presents the approach to image acquisition and evaluation recommended by the BACPAC Spine Imaging Working Group. While the approach is specific to BACPAC studies, it is general enough to be applied at other centers performing magnetic resonance imaging (MRI) acquisitions in patients with LBP. The herein presented SOPs are meant to improve understanding of pain mechanisms and facilitate patient phenotyping by codifying MRI-based methods that provide standardized, non-invasive assessments of spinal pathologies. Finally, these recommended procedures may facilitate the integration of better harmonized MRI data of the lumbar spine across studies and sites within and outside of BACPAC studies.

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 (DL) has been proposed to automate image segmentation and provide accuracy, consistency, and efficiency. Accurate segmentation of lipomatous tumors (LTs) is critical for correct tumor radiomics analysis and localization. The major challenge of this task is data heterogeneity, including tumor morphological characteristics and multicenter scanning protocols. To mitigate the issue, we aimed to develop a DL-based Super Learner (SL) ensemble framework with different data correction and normalization methods. Pathologically proven LTs on pre-operative T1-weighted/proton-density MR images of 185 patients were manually segmented. The LTs were categorized by tumor locations as distal upper limb (DUL), distal lower limb (DLL), proximal upper limb (PUL), proximal lower limb (PLL), or Trunk (T) and grouped by 80%/9%/11% for training, validation and testing. Six configurations of correction/normalization were applied to data for fivefold-cross-validation trainings, resulting in 30 base learners (BLs). A SL was obtained from the BLs by optimizing SL weights. The performance was evaluated by dice-similarity-coefficient (DSC), sensitivity, specificity, and Hausdorff distance (HD95). For predictions of the BLs, the average DSC, sensitivity, and specificity from the testing data were 0.72 [Formula: see text] 0.16, 0.73 [Formula: see text] 0.168, and 0.99 [Formula: see text] 0.012, respectively, while for SL predictions were 0.80 [Formula: see text] 0.184, 0.78 [Formula: see text] 0.193, and 1.00 [Formula: see text] 0.010. The average HD95 of the BLs were 11.5 (DUL), 23.2 (DLL), 25.9 (PUL), 32.1 (PLL), and 47.9 (T) mm, whereas of SL were 1.7, 8.4, 15.9, 2.2, and 36.6 mm, respectively. The proposed method could improve the segmentation accuracy and mitigate the performance instability and data heterogeneity aiding the differential diagnosis of LTs in real clinical situations.

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 Cohens 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.

PurposeClinical management of disc degeneration in patients with chronic low back pain (cLBP) is hampered by the challenge of distinguishing pathologic changes relating to pain from physiologic changes related to aging. The goal of this study was to use imaging biomarkers of disc biochemical composition to distinguish degenerative changes associated with cLBP from normal aging.MethodsT1ρ MRI data were acquired from 133 prospectively enrolled subjects for this observational study (80 cLBP, 53 controls; mean ± SD age = 43.9 ± 13.4 years; 61 females, 72 males). The mean T1ρ relaxation time in the nucleus pulposus (NP-T1ρ; n = 650 discs) was used as a quantitative biomarker of disc biochemical composition. Linear regression was used to assess associations between NP-T1ρ and age, sex, spinal level, and study group, and their interactions.ResultsNP-T1ρ values were lower in cLBP patients than controls (70.8 ± 22.8 vs. 76.4 ± 22.2 ms, p = 0.009). Group differences were largest at L5-S1 (ΔT1ρcLBP-control = -11.3 ms, p

Radiologists today play a central role in making diagnostic decisions and labeling images for training and benchmarking artificial intelligence (AI) algorithms. A key concern is low inter-reader reliability (IRR) seen between experts when interpreting challenging cases. While team-based decisions are known to outperform individual decisions, inter-personal biases often creep up in group interactions which limit nondominant participants from expressing true opinions. To overcome the dual problems of low consensus and interpersonal bias, we explored a solution modeled on bee swarms. Two separate cohorts, three board-certified radiologists, (cohort 1), and five radiology residents (cohort 2) collaborated on a digital swarm platform in real time and in a blinded fashion, grading meniscal lesions on knee MR exams. These consensus votes were benchmarked against clinical (arthroscopy) and radiological (senior-most radiologist) standards of reference using Cohen's kappa. The IRR of the consensus votes was then compared to the IRR of the majority and most confident votes of the two cohorts. IRR was also calculated for predictions from a meniscal lesion detecting AI algorithm. The attending cohort saw an improvement of 23% in IRR of swarm votes (k = 0.34) over majority vote (k = 0.11). Similar improvement of 23% in IRR (k = 0.25) in 3-resident swarm votes over majority vote (k = 0.02) was observed. The 5-resident swarm had an even higher improvement of 30% in IRR (k = 0.37) over majority vote (k = 0.07). The swarm consensus votes outperformed individual and majority vote decision in both the radiologists and resident cohorts. The attending and resident swarms also outperformed predictions from a state-of-the-art AI algorithm.

ObjectiveTo assess the effects of weight loss and weight gain on hip and knee radiographic changes, pain, and joint replacement over 4 years.MethodsParticipants (n = 2,752) from the Osteoarthritis Initiative were classified as those with weight gain (more than 5% gain), weight loss (more than -5% loss), or as controls (-3% to 3% change) over 4 years. Generalized estimating equations (adjusted for age, sex, and body mass index) were used to assess the relationship between the weight-change group and 4-year changes in knee radiographic osteoarthritis (OA) (Kellgren/Lawrence [K/L] grade), hip OA (Croft summary grade), joint space narrowing (JSN), and joint pain.ResultsFor radiographic knee OA, weight loss was associated with significantly lower odds of K/L grade worsening over 4 years (odds ratio [OR] 0.69 [95% confidence interval (95% CI) 0.53-0.91], P = 0.009), and weight gain was significantly associated with higher odds of medial knee JSN (OR 1.29 [95% CI 1.01-1.64], P = 0.038) compared to controls. For knee pain, weight loss was significantly associated with knee pain resolution over 4 years (OR 1.40 [95% CI 1.06-1.86], P = 0.019) while weight gain was associated with knee pain development (OR 1.34 [95% CI 1.08-1.67], P = 0.009) compared to controls. For all hip outcomes, no significant associations (P > 0.05) were found with weight-change groups. The associations between the weight-change group and total hip or total knee replacement were not significant (P > 0.05).ConclusionThis large, longitudinal study (n = 2,752 with 4-year follow-up) suggests that weight loss may protect against, and weight gain may exacerbate, radiographic and symptomatic knee OA, while weight change (at a 5% threshold) does not have significant effects on hip OA.

To determine the cross-sectional and longitudinal associations of patella alignment with cartilage relaxation and patients self-reported symptoms. Thirty participants with isolated patellofemoral joint (PFJ) degeneration (six males, 53.7 ± 9.3 years) and 24 controls (12 males, 47.6 ± 10.7 years) were included. Magnetic resonance assessment was performed to provide grading of structural abnormalities, cartilage relaxation times, and patella alignment. Self-reported symptoms were assessed using the self-administrated knee injury and osteoarthritis outcome score (KOOS). All participants were examined at baseline and 3 years. Statistical parametric mapping and Pearson partial correlation were used to evaluate the associations between patella alignment with cartilage relaxation times and self-reported symptoms, respectively. The analyses were performed between baseline (cross-sectional) as well as the baseline against 3 years (longitudinal). Results indicated that patella height and patella flexion were associated with T1ρ and T2 relaxation times at baseline (percentages of voxels showing significant correlation [PSV] = 10.1%-24.8%; mean correlations [R] = 0.34-0.36; mean p = 0.015-0.026). Furthermore, greater patella lateral alignment, lateral tilt, and lateral spin were associated with longer T2 times at 3 years (PSV = 11.0%-14.4%, R = 0.39-0.44, p = 0.017-0.028). Last, a higher patella was associated with a lower KOOS at baseline and at 3 years (R = -0.33 to -0.35). The study suggests that patella malalignment is a risk factor for worsening cartilage health, informing clinicians of a better rehabilitation program that targets PFJ degeneration.

BackgroundSynovial inflammation is a risk factor for osteoarthritis (OA). But to date, there is limited information on how inflammation impacts progression of knee OA.PurposeTo investigate how sustained synovitis, assessed with semi-quantitative magnetic resonance imaging (MRI) scores, impacts progression of knee degenerative changes over 4 years.Study typeRetrospective cohort study.SubjectsIn 249 participants (N = 132 women [53%]), from the Osteoarthritis Initiative (OAI) two definitions for synovitis were used resulting in two groups of participants with sustained synovitis at baseline, 2-year, and 4-year follow-up (N = 80 and N = 132), and two groups without synovitis at all three time points (N = 81 and N = 47).Field strength/sequence3 T intermediate-weighted (IW) turbo spin-echo (TSE) sequence and three-dimensional (3D) dual-echo steady-state (DESS) sequence.AssessmentSynovitis was scored semi-quantitatively using the Anterior Cruciate Ligament Osteoarthritis Score (ACLOAS), MRI Osteoarthritis Knee Score (MOAKS), and synovial proliferation score (SPS). Two MRI-based definitions of synovitis were used: (i) score ≥2 based on cumulative score of MOAKS and ACLOAS, and (ii) score ≥3 based on the cumulative score of ACLOAS, MOAKS, and SPS. Changes in structural abnormalities from baseline to year 4 measured using the whole-organ MRI score (WORMS) were defined as outcomes.Statistical testsLinear regression models were used to compare the differences in longitudinal changes in WORMS scores between participants with and without sustained synovitis for each definition of sustained synovitis. A P-value of

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.

BackgroundTo 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.MethodsOne 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.ResultsThe BMI-depressive symptoms interaction was significantly associated with knee pain (p

BackgroundIt is unknown if football players with femoroacetabular impingement (FAI) syndrome report worse burden than those with other causes of hip/groin pain, and to what extent this is mediated by cartilage defects and labral tears.HypothesisFootball players with FAI syndrome would report worse burden than other symptomatic players, with the effect partially mediated by cartilage defects and/or labral tears.Study designCross-sectional study.Level of evidenceLevel 4.MethodsFootball (soccer and Australian football) players (n = 165; 35 women) with hip/groin pain (≥6 months and positive flexion-adduction-internal rotation test) were recruited. Participants completed 2 patient-reported outcome measures (PROMs; the International Hip Outcome Tool-33 [iHOT-33] and Copenhagen Hip and Groin Outcome Score [HAGOS]) and underwent hip radiographs and magnetic resonance imaging (MRI). FAI syndrome was determined to be present when cam and/or pincer morphology were present. Cartilage defects and labral tears were graded as present or absent using MRI. Linear regression models investigated relationships between FAI syndrome (dichotomous independent variable) and PROM scores (dependent variables). Mediation analyses investigated the effect of cartilage defects and labral tears on these relationships.ResultsFAI syndrome was not related to PROM scores (unadjusted b values ranged from -4.693 (P = 0.23) to 0.337 (P = 0.93)) and cartilage defects and/or labral tears did not mediate its effect (P = 0.22-0.97).ConclusionFootball players with FAI syndrome did not report worse burden than those with other causes of hip/groin pain. Cartilage defects and/or labral tears did not explain the effect of FAI syndrome on reported burden.Clinical relevanceFAI syndrome, cartilage defects, and labral tears were prevalent but unrelated to reported burden in symptomatic football players.

BackgroundParaspinal musculature (PSM) is increasingly recognized as a contributor to low back pain (LBP), but with conventional MRI sequences, assessment is limited. Chemical shift encoding-based water-fat MRI (CSE-MRI) enables the measurement of PSM fat fraction (FF), which may assist investigations of chronic LBP.PurposeTo investigate associations between PSM parameters from conventional MRI and CSE-MRI and between PSM parameters and pain.Study typeProspective, cross-sectional.PopulationEighty-four adults with chronic LBP (44.6 ± 13.4 years; 48 males).Field strength/sequence3-T, T1-weighted fast spin-echo and iterative decomposition of water and fat with echo asymmetry and least squares estimation sequences.AssessmentT1-weighted images for Goutallier classification (GC), muscle volume, lumbar indentation value, and muscle-fat index, CSE-MRI for FF extraction (L1/2-L5/S1). Pain was self-reported using a visual analogue scale (VAS). Intra- and/or interreader agreement was assessed for MRI-derived parameters.Statistical testsMixed-effects and linear regression models to 1) assess relationships between PSM parameters (entire cohort and subgroup with GC grades 0 and 1; statistical significance α = 0.0025) and 2) evaluate associations of PSM parameters with pain (α = 0.05). Intraclass correlation coefficients (ICCs) for intra- and/or interreader agreement.ResultsThe FF showed excellent intra- and interreader agreement (ICC range: 0.97-0.99) and was significantly associated with GC at all spinal levels. Subgroup analysis suggested that early/subtle changes in PSM are detectable with FF but not with GC, given the absence of significant associations between FF and GC (P-value range: 0.036 at L5/S1 to 0.784 at L2/L3). Averaged over all spinal levels, FF and GC were significantly associated with VAS scores.Data conclusionIn the absence of FF, GC may be the best surrogate for PSM quality. Given the ability of CSE-MRI to detect muscle alterations at early stages of PSM degeneration, this technique may have potential for further investigations of the role of PSM in chronic LBP.Level of evidence2 TECHNICAL EFFICACY STAGE: 2.

The aims of this case-control study were to: (1) Identify cartilage locations and volumes at risk of osteoarthritis (OA) using subject-specific finite element (FE) models; (2) Quantify the relationships between the simulated biomechanical parameters and T2 and T1ρ relaxation times of magnetic resonance imaging (MRI). We created subject-specific FE models for seven patients with anterior cruciate ligament (ACL) reconstruction and six controls based on a previous proof-of-concept study. We identified locations and cartilage volumes susceptible to OA, based on maximum principal stresses and absolute maximum shear strains in cartilage exceeding thresholds of 7 MPa and 32%, respectively. The locations and volumes susceptible to OA were compared qualitatively and quantitatively against 2-year longitudinal changes in T2 and T1ρ relaxation times. The degeneration volumes predicted by the FE models, based on excessive maximum principal stresses, were significantly correlated (r = 0.711, p

OBJECTIVE: To qualitatively evaluate the utility of zero echo-time (ZTE) MRI sequences in identifying osseous findings and to compare ZTE with optimized spoiled gradient echo (SPGR) sequences in detecting knee osseous abnormalities. MATERIALS AND METHODS: ZTE and standard knee MRI sequences were acquired at 3T in 100 consecutive patients. Three radiologists rated confidence in evaluating osseous abnormalities and image quality on a 5-grade Likert scale in ZTE compared to standard sequences. In a subset of knees (n = 57) SPGR sequences were also obtained, and diagnostic confidence in identifying osseous structures was assessed, comparing ZTE and SPGR sequences. Statistical significance of using ZTE over SPGR was characterized with a paired t-test. RESULTS: Image quality of the ZTE sequences was rated high by all reviewers with 278 out of 299 (100 studies, 3 radiologists) scores ≥ 4 on the Likert scale. Diagnostic confidence in using ZTE sequences was rated very high confidence in 97%, 85%, 71%, and 73% of the cases for osteophytosis, subchondral cysts, fractures, and soft tissue calcifications/ossifications, respectively. In 74% of cases with osseous findings, reviewer scores indicated confidence levels (score ≥ 3) that ZTE sequences improved diagnostic certainty over standard sequences. The diagnostic confidence in using ZTE over SPGR sequences for osseous structures as well as abnormalities was favorable and statistically significant (p

Excessive tissue deformation near cartilage lesions and acute inflammation within the knee joint after anterior cruciate ligament (ACL) rupture and reconstruction surgery accelerate the loss of fixed charge density (FCD) and subsequent cartilage tissue degeneration. Here, we show how biomechanical and biochemical degradation pathways can predict FCD loss using a patient-specific finite element model of an ACL reconstructed knee joint exhibiting a chondral lesion. Biomechanical degradation was based on the excessive maximum shear strains that may result in cell apoptosis, while biochemical degradation was driven by the diffusion of pro-inflammatory cytokines. We found that the biomechanical model was able to predict substantial localized FCD loss near the lesion and on the medial areas of the lateral tibial cartilage. In turn, the biochemical model predicted FCD loss all around the lesion and at intact areas; the highest FCD loss was at the cartilage-synovial fluid-interface and decreased toward the deeper zones. Interestingly, simulating a downturn of an acute inflammatory response by reducing the cytokine concentration exponentially over time in synovial fluid led to a partial recovery of FCD content in the cartilage. Our novel numerical approach suggests that in vivo FCD loss can be estimated in injured cartilage following ACL injury and reconstruction. Our novel modeling platform can benefit the prediction of PTOA progression and the development of treatment interventions such as disease-modifying drug testing and rehabilitation strategies.

PurposeThe composition of the subchondral bone marrow and cartilage endplate (CEP) could affect intervertebral disc health by influencing vertebral perfusion and nutrient diffusion. However, the relative contributions of these factors to disc degeneration in patients with chronic low back pain (cLBP) have not been quantified. The goal of this study was to use compositional biomarkers derived from quantitative MRI to establish how CEP composition (surrogate for permeability) and vertebral bone marrow fat fraction (BMFF, surrogate for perfusion) relate to disc degeneration.MethodsMRI data from 60 patients with cLBP were included in this prospective observational study (28 female, 32 male; age = 40.0 ± 11.9 years, 19-65 [mean ± SD, min-max]). Ultra-short echo-time MRI was used to calculate CEP T2* relaxation times (reflecting biochemical composition), water-fat MRI was used to calculate vertebral BMFF, and T1ρ MRI was used to calculate T1ρ relaxation times in the nucleus pulposus (NP T1ρ, reflecting proteoglycan content and degenerative grade). Univariate linear regression was used to assess the independent effects of CEP T2* and vertebral BMFF on NP T1ρ. Mixed effects multivariable linear regression accounting for age, sex, and BMI was used to assess the combined relationship between variables.ResultsCEP T2* and vertebral BMFF were independently associated with NP T1ρ (p = 0.003 and 0.0001, respectively). After adjusting for age, sex, and BMI, NP T1ρ remained significantly associated with CEP T2* (p = 0.0001) but not vertebral BMFF (p = 0.43).ConclusionPoor CEP composition plays a significant role in disc degeneration severity and can affect disc health both with and without deficits in vertebral perfusion.

BackgroundModic changes (MCs) are the most prevalent classification system for describing magnetic resonance imaging (MRI) signal intensity changes in the vertebrae. However, there is a growing need for novel quantitative and standardized methods of characterizing these anomalies, particularly for lesions of transitional or mixed nature, due to the lack of conclusive evidence of their associations with low back pain. This retrospective imaging study aims to develop an interpretable deep learning-based detection tool for voxel-wise mapping of MCs.MethodsSeventy-five lumbar spine MRI exams that presented with acute-to-chronic low back pain, radiculopathy, and other symptoms of the lumbar spine were enrolled. The pipeline consists of two deep convolutional neural networks to generate an interpretable voxel-wise Modic map. First, an autoencoder was trained to segment vertebral bodies from T1-weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T1- and T2-weighted assessment to serve as ground truth for training a second autoencoder that performs segmentation of MCs. The voxels in the detected regions were then categorized to the appropriate Modic type using a rule-based signal intensity algorithm. Post hoc, three radiologists independently graded a second dataset with the aid of the model predictions in an artificial (AI)-assisted experiment.ResultsThe model successfully identified the presence of changes in 85.7% of samples in the unseen test set with a sensitivity of 0.71 (±0.072), specificity of 0.95 (±0.022), and Cohen's kappa score of 0.63. In the AI-assisted experiment, the agreement between the junior radiologist and the senior neuroradiologist significantly improved from Cohen's kappa score of 0.52 to 0.58 (p

PurposeTo assess the correlation between changes in hip capsule morphology with improvements in patient-reported outcome (PRO) scores after arthroscopic surgery for femoroacetabular impingement syndrome (FAIS) using the periportal capsulotomy technique.MethodsTwenty-eight patients with cam morphology FAIS (without arthritis, dysplasia, or hypermobility) were prospectively enrolled before arthroscopic labral repair and femoroplasty through periportal capsulotomy (anterolateral/midanterior portals) without closure. Patients completed the Hip Disability and Osteoarthritis Outcomes Score (HOOS) and had nonarthrographic 3T magnetic resonance imaging (MRI) scans of the affected hip before and 1 year after surgery. Anterior capsule thickness, posterior capsule thickness, anterior-posterior capsule thickness ratio, and proximal-distal anterior capsule thickness ratio were measured on axial-oblique MRI sequences. Pearson correlation coefficients were calculated to determine the association between hip capsule morphology and PRO scores.ResultsPostoperative imaging showed that for all 28 patients (12 female), labral repairs and capsulotomies had healed within 1 year of surgery. Analysis revealed postoperative decreases in anterior hip capsule thickness (1395.4 ± 508.4 mm3 vs 1758.4 ± 487.9 mm3; P = .003) and anterior-posterior capsule thickness ratio (0.92 ± 0.33 vs 1.12 ± 0.38; P = .02). Higher preoperative anterior-posterior capsule thickness ratio correlated with lower preoperative scores for HOOS pain (R = -0.43; P = .02), activities of daily living (ADL) (R = -0.43; P = .02), and sport (R = -0.38; P = .04). Greater decrease from preoperative to postoperative anterior-posterior capsule thickness ratio correlated with greater improvement for HOOS pain (R = -0.40; P = .04), ADL (R = -0.45; P = .02), and sport (R = -0.46; P = .02).ConclusionsPeriportal capsulotomy without closure demonstrates capsule healing by 1 year after arthroscopic FAIS treatment. Changes in hip capsule morphology including decreased anterior-posterior capsule thickness ratio after surgery may be correlated with improvements in patient pain, function, and ability to return to sports.Level of evidenceLevel II, prospective cohort study.