A Computer Vision Algorithm to Predict Superior Mesenteric Artery Margin Status for Patients with Pancreatic Ductal Adenocarcinoma.

OBJECTIVE: To evaluate the feasibility of developing a computer vision algorithm that uses preoperative computed tomography (CT) scans to predict superior mesenteric artery (SMA) margin status in patients undergoing Whipple for pancreatic ductal adenocarcinoma (PDAC), and to compare algorithm performance to that of expert abdominal radiologists and surgical oncologists. SUMMARY BACKGROUND DATA: Complete surgical resection is the only chance to achieve a cure for PDAC; however, current modalities to predict vascular invasion have limited accuracy. METHODS: Adult patients with PDAC who underwent Whipple and had preoperative contrast-enhanced CT scans were included (2010-2022). The SMA was manually annotated on the CT scans, and we trained a U-Net algorithm for SMA segmentation and a ResNet50 algorithm for predicting SMA margin status. Radiologists and surgeons reviewed the scans in a blinded fashion. SMA margin status per pathology reports was the reference. RESULTS: Two hundred patients were included. Forty patients (20%) had a positive SMA margin. For the segmentation task, the U-Net model achieved a Dice Similarity Coefficient of 0.90. For the classification task, all readers demonstrated limited sensitivity, although the algorithm had the highest sensitivity at 0.43 (versus 0.23 and 0.36 for the radiologists and surgeons, respectively). Specificity was universally excellent, with the radiologist and algorithm demonstrating the highest specificity at 0.94. Finally, the accuracy of the algorithm was 0.85 versus 0.80 and 0.76 for the radiologists and surgeons, respectively. CONCLUSIONS: We demonstrated the feasibility of developing a computer vision algorithm to predict SMA margin status using preoperative CT scans, highlighting its potential to augment the prediction of vascular involvement.

The relationships between patellofemoral bone remodeling, cartilage composition, and vertical loading rate: PET/MRI in isolated patellofemoral osteoarthritis.

OBJECTIVE: Loading is invariably an important factor of consideration for understanding the causality flow and parallel existence of articular cartilage and subchondral bone changes. The goal of this study was to investigate the patterns of subregional 18NaF-SUV vs. T1p-T2 associations and vertical ground reaction force loading rates; in isolated patellofemoral-joint-osteoarthritis (PFJ-OA) patients. METHOD: Thirty-five isolated PFJ-OA patients, with no tibiofemoral involvement, underwent simultaneous scans in a 3.0T whole-body hybrid positron emission tomography-magnetic resonance imaging scanner. MRI Whole-Organ Magnetic Resonance Imaging Scoring assessments were performed to identify/confirm isolated PFJ-OA knees from bilateral scans. T1p-T2 relaxation and SUV values were automatically computed for both trochlear and patellar cartilage and subchondral bone subregions (deep, superficial, lateral, and medial). Maximum vertical impact loading rates (Loading-RateNorm) were calculated from walking trials. Relationships were explored between SUV uptake, T1p-T2 values, and Loading-RateNorm via linear mixed-effects modeling. RESULTS: Significant and complex association patterns were noted between medial and lateral bone 18NaF-SUV uptakes vs. medial and lateral cartilage sub-regional T1p and T2. SUVMean and SUVMax were positively associated with deep cartilage subregional T1pand T2 values; and negatively associated with superficial cartilage subregional T1p-T2 values in both medial and lateral regions. Both medial and lateral bone 18NaF-SUVMean and SUVMax uptakes remained positively associated with the individual gait characteristics, i.e., peak vertical impact loading rates (Loading-RateNorm). CONCLUSION: Evidence of simultaneous, complementary, cross-sectional associations between T1p-T2 values and peak vertical loading rates with 18NaF-SUV, have been rare in the isolated PFJ-OA cohort. The clinical implications of such novel associations remain of utmost importance from a gait retraining perspective.

Unmet Needs in Spondyloarthritis: Imaging in Axial Spondyloarthritis.

Imaging biomarkers in axial spondyloarthritis (axSpA) are currently the most specific biomarkers for the diagnosis of this condition. Despite advances in imaging, from plain radiographs-which detect only damage-to magnetic resonance imaging (MRI)-which identifies disease activity and structural change-there are still many challenges that remain. Imaging in sacroiliitis is characterized by active and structural changes. Current classification criteria stress the importance of bone marrow edema (BME); however, BME can occur in various diseases, mechanical conditions, and healthy individuals. Thus, the identification of structural lesions such as erosion, subchondral fat, backfill, and ankylosis is important to distinguish from mimics on differential diagnosis. Various imaging modalities are available to examine structural lesions, but computed tomography (CT) is considered the current reference standard. Nonetheless, recent advances in MRI allow for direct bone imaging and the reconstruction of CT-like images that can provide similar information. Therefore, the ability of MRI to detect and measure structural lesions is strengthened. Here, we present an overview of the spectrum of current and cutting-edge techniques for SpA imaging in clinical practice; namely, we discuss the advantages, disadvantages, and usefulness of imaging in SpA through radiography, low-dose and dual-energy CT, and MRI. Cutting-edge MRI sequences including volumetric interpolated breath-hold examination, ultrashort echo time, zero echo time, and deep learning-based synthetic CT that creates CT-like images without ionizing radiation, are discussed. Imaging techniques allow for quantification of inflammatory and structural lesions, which is important in the assessment of treatment response and disease progression. Radiographic damage is poorly sensitive to change. Artificial intelligence has already revolutionized radiology practice, including protocolization, image quality, and image interpretation.

Ligamentum teres lesions are associated with compositional and structural hip cartilage degenerative change: region-specific cartilage degeneration.

OBJECTIVES: To investigate the association between magnetic resonance imaging (MRI)-based ligamentum teres lesions (LTL) and structural hip degeneration. METHODS: Bilateral 3-T hip MRIs of participants (n = 93 [36 men]; mean age ( ± SD) 51 years ± 15.4) recruited from the community and the orthopedic clinic of a single medical center were included. Clinical and imaging data acquired included hip disability and osteoarthritis outcome scores, semi-quantitative scoring of hip osteoarthritis on MRI (SHOMRI) scores on fluid-sensitive sequences, and cartilage T1ρ/T2 compositional sequences. An MRI-based LTL scoring system, incorporating continuity, thickening, and signal intensity, ranging from 0 (normal) to 4 (complete tear) was constructed. Hip morphological features associated with LTL, based on functional or anatomical relationships to LT, were defined. Relationships between MRI-LT scores and SHOMRI, global/regional cartilage T1ρ/T2, and proposed morphological abnormalities and LTL were explored by mixed effects linear and logistic regression models. RESULTS: In 82 (46.1%) hips, no pain was documented; 118 (63.4%) and 68 (36.6%) hips were graded as KL-grade ≤ 1 and ≥ 2, respectively. Compared to MRI-LT score = 0 (normal), score = 4 (complete tear) revealed significantly worse subchondral bony degenerative changes for bone marrow lesions (SHOMRI-BML) and subchondral cysts (SHOMRI-sc) (p < 0.001, p = 0.015, respectively). Global acetabular T1ρ, femoral T2 were significantly increased for abnormal MRI-LT scores (p-range = 0.005-0.032). Regional analyses revealed significantly increased T1ρ/T2 in central acetabular/increased T2 in off-central femoral regions (p-range = 0.005-0.046). Pulvinar effusion-synovitis, shallow fovea, and foveal osteophytes were significantly associated with abnormal LT MRI findings (p-range = < 0.001-0.044). CONCLUSION: MRI abnormalities of LT are associated with worse SHOMRI-sc/BML scores, indicative of hip osteoarthritis and higher T1ρ and T2 that differ by region. Pulvinar effusion-synovitis and changes in femoral head morphology are associated with LTL. CLINICAL RELEVANCE STATEMENT: Abnormal ligamentum teres findings identified via MRI are associated with structural degenerative changes of the hip joint and alterations in acetabular and femoral cartilage compositions show spatial differences in relation to LTL. KEY POINTS: The clinical significance of common ligamentum teres lesions (LTL) on MRI is not well understood. LTL identified by an MRI-based scoring system is associated with worse biomarkers, indicating more advanced degenerative hip changes. Effusion-synovitis signal at pulvinar, shallow fovea capitis, and foveal osteophytes are associated with LTL on imaging.

Is hip capsule morphology associated with hip pain in patients without another structural correlate?

OBJECTIVE: The goals of this study were (i) to assess the association between hip capsule morphology and pain in patients without any other MRI abnormalities that would correlate with pain and (ii) to investigate whether hip capsule morphology in hip pain patients is different from that of controls. METHODS: In this study, 76 adults with hip pain who did not show any structural abnormalities on MRI and 46 asymptomatic volunteers were included. Manual segmentation of the anterior and posterior hip capsules was performed. Total and mean anterior hip capsule area, posterior capsule area, anterior-to-posterior capsule area ratio, and medial-to-lateral area ratio in the anterior capsule were quantified. Differences between the pain and control groups were evaluated using logistic regression models. RESULTS: Patients with hip pain showed a significantly lower anterior-to-posterior area ratio as compared with the control group (p = 0.002). The pain group's posterior hip capsule area was significantly larger than that of controls (p = 0.001). Additionally, the ratio between the medial and lateral sections of the anterior capsule was significantly lower in the pain group (p = 0.004). CONCLUSIONS: Patients with hip pain are more likely to have thicker posterior capsules and a lower ratio of the anterior-to-posterior capsule area and thinner medial anterior capsules with a lower ratio of the medial-to-lateral anterior hip capsule compartment, compared with controls. CLINICAL RELEVANCE STATEMENT: During MRI evaluations of patients with hip pain, morphology of the hip capsule should be assessed. This study aims to be a foundation for future analyses to identify thresholds distinguishing normal from abnormal hip capsule measurements. KEY POINTS: • Even with modern image modalities such as MRI, one of the biggest challenges in handling hip pain patients is finding a structural link for their pain. • Hip capsule morphologies that correlated with hip pain showed a larger posterior hip capsule area and a lower anterior-to-posterior capsule area ratio, as well as a smaller medial anterior capsule area with a lower medial-to-lateral anterior hip capsule ratio. • The hip capsule morphology is correlated with hip pain in patients who do not show other morphology abnormalities in MRI and should get more attention in clinical practice.

Clinical and imaging findings associated with preservation of knee joint health over 8 years in individuals aged 65 and over: data from the OAI.

OBJECTIVE: While risk factors for osteoarthritis (OA) are well known, it is not well understood why certain individuals maintain high mobility and joint health throughout their life while others demonstrate OA at older ages. The purpose of this study was to assess which demographic, clinical and MRI quantitative and semi-quantitative factors are associated with preserving healthy knees in older individuals. METHODS: This study analyzed data from the OA Initiative (OAI) cohort of individuals at the age of 65 years or above. Participants without OA at baseline (BL) (Kellgren-Lawrence (KL) ≤ 1) were followed and classified as incident cases (KL ≥ 2 during follow-up; n = 115) and as non-incident (KL ≤ 1 over 96-month; n = 391). Associations between the predictor-variables sex, age, BMI, race, clinical scoring systems, T2 relaxation times and Whole-Organ Magnetic Resonance Imaging-Score (WORMS) readings at BL and the preservation of healthy knees (KL ≤ 1) during a 96-month follow-up period were assessed using logistic regression models. RESULTS: Obesity and presence of pain showed a significant inverse association with maintaining radiographically normal joints in patients aged 65 and above. T2 relaxation times of the lateral femur and tibia as well as the medial femur were also significantly associated with maintaining radiographically normal knee joints. Additionally, absence of lesions of the lateral meniscus and absence of cartilage lesions in the medial and patellofemoral compartments were significantly associated with maintaining healthy knee joints. CONCLUSION: Overall, this study provides protective clinical parameters as well as quantitative and semi-quantitative MR-imaging parameters associated with maintaining radiographically normal knee joints in an older population over 8 years.

Improved metal suppression using new generation low-field MRI: a biophantom feasibility study.

OBJECTIVE: Novel 0.55 MRI scanners have the potential to reduce metal artifacts around orthopedic implants. The purpose of this study was to compare metal artifact size and depiction of anatomy between 0.55 T and 3.0 T MRI in a biophantom. MATERIALS AND METHODS: Steel and titanium screws were implanted in 12 porcine knee specimens and imaging at 0.55 T and 3 T MRI was performed using the following sequences: turbo spin-echo (TSE), TSE with view angle tilting (VAT), and slice encoding for metal artifact correction (SEMAC) with proton-density (PD) and T2-weighted short-tau inversion-recovery (T2w-STIR) contrasts. Artifacts were measured, and visualization of anatomy (cartilage, bone, growth plates, cruciate ligaments) was assessed and compared between groups. RESULTS: Metal artifacts were significantly smaller at 0.55 T. The smallest artifact sizes were achieved with SEMAC at 0.55 T for both PD and T2w-STIR sequences; corresponding relative size reductions vs. 3.0 T were 78.7% and 79.4% (stainless steel) and 45.3% and 1.4% (titanium). Depiction of anatomical structures was superior at 0.55 T. CONCLUSION: Substantial reduction of artifact size resulting in superior depiction of anatomical structures is possible on novel 0.55 T MRI systems. Further clinical studies are required to elucidate patient-relevant advantages.

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.

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.

Local Patterns in 2-Year T1ρ and T2 Changes of Hip Cartilage Are Related to Sex and Functional Data: A Prospective Evaluation on Hip Osteoarthritis Participants.

BACKGROUND: Although T1ρ and T2 have emerged as early indicators for hip osteoarthritis (OA), there is little information regarding longitudinal changes across the cartilage in the early stages of this disease. PURPOSE: To characterize the variability in 2-year hip cartilage T1ρ and T2 changes and investigate associations between these patterns of change and common indicators of hip OA. STUDY TYPE: Prospective. POPULATION: A total of 25 women (age: 51.9 ± 16.3 years old; BMI: 22.6 ± 2.0 kg/m2 ) and 17 men (age: 55.8 ± 14.9 years old; body mass index (BMI): 24.4 ± 3.8 kg/m2 ) who were healthy or with early-to-moderate hip OA. FIELD STRENGTH/SEQUENCE: A 3 T MRI (GE), 3D combined T1ρ /T2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots. ASSESSMENT: Principal component (PC) analysis of Z-score difference maps of 2-year changes in hip cartilage T1ρ and T2 relaxation times, participant hip disability and osteoarthritis outcome scores (HOOS) and functional tests at 2-year follow-up. STATISTICAL TESTS: Shapiro-Wilk test, unpaired t-tests, Kruskal Wallis tests, Pearson or Spearman (ρ) correlations. Significance was set at P < 0.05. RESULTS: Women (-6.40 ± 14.48) had significantly lower T1ρ PC1 scores than men (10.05 ± 26.15). T1ρ PC4 was significantly correlated with HOOSsport , HOOSsymptoms , HOOSpain , HOOSadl , and HOOSqol at 2-year follow-up (ρ: [0.36, 0.50]). T1ρ PC2 and PC4 were significantly correlated with 30-second chair test (ρ = -0.39 and ρ = 0.24, respectively) and side plank (ρ = -0.32 and ρ = 0.21). T1ρ and T2 PC2 were significantly correlated with 40 m walk test (ρ = 0.34 and ρ = 0.31) and 30-second chair rise test (ρ = -0.39 and ρ = -0.32). DATA CONCLUSION: Men exhibited accelerated T1ρ increases across the femoral cartilage compared to women, suggesting sex should be considered when evaluating early hip OA. Participants with poorer HOOS and function exhibited greater T1ρ and T2 increases in superior and anterior femoral cartilage and greater T1ρ increases in the anterior femoral cartilage. These patterns of short-term relaxometry increases could indicate hip OA progression. EVIDENCE LEVEL: 2 TECHNICAL EFFICACY: Stage 3.

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

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

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.

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.

Deep Learning for Multi-Tissue Segmentation and Fully Automatic Personalized Biomechanical Models from BACPAC Clinical Lumbar Spine MRI.

STUDY DESIGN: In vivo retrospective study of fully automatic quantitative imaging feature extraction from clinically acquired lumbar spine magnetic resonance imaging (MRI). OBJECTIVE: To demonstrate the feasibility of substituting automatic for human-demarcated segmentation of major anatomic structures in clinical lumbar spine MRI to generate quantitative image-based features and biomechanical models. SETTING: Previous studies have demonstrated the viability of automatic segmentation applied to medical images; however, the feasibility of these networks to segment clinically acquired images has not yet been demonstrated, as they largely rely on specialized sequences or strict quality of imaging data to achieve good performance. METHODS: Convolutional neural networks were trained to demarcate vertebral bodies, intervertebral disc, and paraspinous muscles from sagittal and axial T1-weighted MRIs. Intervertebral disc height, muscle cross-sectional area, and subject-specific musculoskeletal models of tissue loading in the lumbar spine were then computed from these segmentations and compared against those computed from human-demarcated masks. RESULTS: Segmentation masks, as well as the morphological metrics and biomechanical models computed from those masks, were highly similar between human- and computer-generated methods. Segmentations were similar, with Dice similarity coefficients of 0.77 or greater across networks, and morphological metrics and biomechanical models were similar, with Pearson R correlation coefficients of 0.69 or greater when significant. CONCLUSIONS: This study demonstrates the feasibility of substituting computer-generated for human-generated segmentations of major anatomic structures in lumbar spine MRI to compute quantitative image-based morphological metrics and subject-specific musculoskeletal models of tissue loading quickly, efficiently, and at scale without interrupting routine clinical care.

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.

Magnetic Resonance Imaging of the Lumbar Spine: Recommendations for Acquisition and Image Evaluation from the BACPAC Spine Imaging Working Group.

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.

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.

Acetabular cartilage delamination: performance of MRI using arthroscopy as the standard of reference.

BACKGROUND: Femoroacetabular impingement (FAI) frequently leads to acetabular chondral delamination. Early identification and treatment of these cases is crucial to prevent further damage to the hip. PURPOSE: To evaluate the accuracy of morphological signs of cartilage acetabular delamination in non-arthrographic magnetic resonance imaging (MRI) using intra-articular arthroscopic findings in patients undergoing FAI surgery. MATERIAL AND METHODS: All hip MRI scans were assessed individually by three independent radiologists. Images were assessed for signs of delamination including the presence of a linear area of bright signal intensity along the acetabular subchondral bone and an area of darker tissue at the surface of the acetabular cartilage. All FAI patients underwent surgery; arthroscopy served as the standard of reference. RESULTS: The mean age of participants was 36.1±10.9 years with 36 (48.6%) women. In the FAI group, arthroscopic surgery showed acetabular chondral delamination in 37 hips. In all hips (including the controls), MRI signs of acetabular cartilage delamination showed an average sensitivity across the three raters of 73.0% with a specificity of 71.0%. In a separate analysis of only the FAI patients, a slightly higher sensitivity (77.7%) but lower specificity (66.7%) was demonstrated. The interrater reliability showed a moderate agreement (average [k]) across the raters (0.450). CONCLUSION: Performance of non-arthrographic MRI in diagnosing acetabular chondral delamination showed good results, yet inter-observer reproducibility among different radiologists was only moderate. Our results suggest that an increased level of awareness, for signs of delamination using MRI, will be helpful for detecting chondral delamination in patients with a history of FAI.

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.

Utilizing a Digital Swarm Intelligence Platform to Improve Consensus Among Radiologists and Exploring Its Applications.

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.