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.

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.

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.

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.

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.

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis.

PURPOSE: To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning. METHODS: The sagittal tibial tubercle-trochlear groove (sTTTG) distance, tibial tubercle-trochlear groove distance, trochlear sulcus angle, trochlear depth, Caton-Deschamps Index (CDI), and flexion angle were measured by use of deep learning-generated segmentations on a subset of the Osteoarthritis Initiative study with radiologist-graded PFJ cartilage grades (n = 2,461). Kruskal-Wallis H tests were performed to compare differences in PFJ morphology between subjects without PFJ osteoarthritis (OA) and those with PFJ OA. PFJ morphology was correlated with secondary outcomes of mean patellar cartilage thickness and mean patellar cartilage T2 relaxation time using linear regression models controlling for age, sex, and body mass index. RESULTS: A total of 1,626 knees did not have PFJ OA, whereas 835 knees had PFJ OA. Knees without PFJ OA had an increased (anterior) sTTTG distance (mean ± standard deviation, 11.1 ± 12.8 mm) compared with knees with PFJ OA (8.4 ± 12.7 mm) (P < .001), indicating a more posterior tibial tubercle in subjects with PFJ OA. Knees without PFJ OA had a decreased sulcus angle (127.4° ± 7.1° vs 128.0° ± 8.4°, P = .01) and increased trochlear depth (9.1 ± 1.7 mm vs 9.0 ± 2.0 mm, P = .03) compared with knees with PFJ OA. Decreased patellar cartilage thickness was associated with decreased trochlear depth (ß = 0.12, P = .002) and increased CDI (ß = -0.07, P < .001). Increased patellar cartilage T2 relaxation time was correlated with decreased sTTTG distance (ß = -0.08, P = .01), decreased sulcus angle (ß = -0.12, P = .04), and decreased CDI (ß = -0.12, P < .001). CONCLUSIONS: PFJ OA, patellar cartilage thickness, and patellar cartilage T2 relaxation time were shown to be associated with the underlying geometries within the PFJ. This large longitudinal study highlights that a decreased sTTTG distance (i.e., a more posterior tibial tubercle) is significantly associated with PFJ degenerative cartilage change. LEVEL OF EVIDENCE: Level III, retrospective comparative prognostic trial.

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.

Magnetization-prepared spoiled gradient-echo snapshot imaging for efficient measurement of R2 -R1ρ in knee cartilage.

PURPOSE: To validate the potential of quantifying R2 -R1ρ using one pair of signals with T1ρ preparation and T2 preparation incorporated to magnetization-prepared angle-modulated partitioned k-space spoiled gradient-echo snapshots (MAPSS) acquisition and to find an optimal preparation time (Tprep ) for in vivo knee MRI. METHODS: Bloch equation simulations were first performed to assess the accuracy of quantifying R2 -R1ρ using T1ρ - and T2 -prepared signals with an equivalent Tprep . For validation of this technique in comparison to the conventional approach that calculates R2 -R1ρ after estimating both T2 and T1ρ , phantom experiments and in vivo validation with five healthy subjects and five osteoarthritis patients were performed at a clinical 3T scanner. RESULTS: Bloch equation simulations demonstrated that the accuracy of this efficient R2 -R1ρ quantification method and the optimal Tprep can be affected by image signal-to-noise ratio (SNR) and tissue relaxation times, but quantification can be closest to the reference with an around 25 ms Tprep for knee cartilage. Phantom experiments demonstrated that the proposed method can depict R2 -R1ρ changes with agarose gel concentration. With in vivo data, significant correlation was observed between cartilage R2 -R1ρ measured from the conventional and the proposed methods, and a Tprep of 25.6 ms provided the most agreement by Bland-Altman analysis. R2 -R1ρ was significantly lower in patients than in healthy subjects for most cartilage compartments. CONCLUSION: As a potential biomarker to indicate cartilage degeneration, R2 -R1ρ can be efficiently measured using one pair of T1ρ -prepared and T2 -prepared signals with an optimal Tprep considering cartilage relaxation times and image SNR.

Clinical language search algorithm from free-text: facilitating appropriate imaging.

BACKGROUND: The comprehensiveness and maintenance of the American College of Radiology (ACR) Appropriateness Criteria (AC) makes it a unique resource for evidence-based clinical imaging decision support, but it is underutilized by clinicians. To facilitate the use of imaging recommendations, we develop a natural language processing (NLP) search algorithm that automatically matches clinical indications that physicians write into imaging orders to appropriate AC imaging recommendations. METHODS: We apply a hybrid model of semantic similarity from a sent2vec model trained on 223 million scientific sentences, combined with term frequency inverse document frequency features. AC documents are ranked based on their embeddings' cosine distance to query. For model testing, we compiled a dataset of simulated simple and complex indications for each AC document (n = 410) and another with clinical indications from randomly sampled radiology reports (n = 100). We compare our algorithm to a custom google search engine. RESULTS: On the simulated indications, our algorithm ranked ground truth documents as top 3 for 98% of simple queries and 85% of complex queries. Similarly, on the randomly sampled radiology report dataset, the algorithm ranked 86% of indications with a single match as top 3. Vague and distracting phrases present in the free-text indications were main sources of errors. Our algorithm provides more relevant results than a custom Google search engine, especially for complex queries. CONCLUSIONS: We have developed and evaluated an NLP algorithm that matches clinical indications to appropriate AC guidelines. This approach can be integrated into imaging ordering systems for automated access to guidelines.

AI MSK clinical applications: cartilage and osteoarthritis.

The advancements of artificial intelligence (AI) for osteoarthritis (OA) applications have been rapid in recent years, particularly innovations of deep learning for image classification, lesion detection, cartilage segmentation, and prediction modeling of future knee OA development. This review article focuses on AI applications in OA research, first describing machine learning (ML) techniques and workflow, followed by how these algorithms are used for OA classification tasks through imaging and non-imaging-based ML models. Deep learning applications for OA research, including analysis of both radiographs for automatic detection of OA severity, and MR images for detection of cartilage/meniscus lesions and cartilage segmentation for automatic T2 quantification will be described. In addition, information on ML models that identify individuals at high risk of OA development will be provided. The future vision of machine learning applications in imaging of OA and cartilage hinges on implementation of AI for optimizing imaging protocols, quantitative assessment of cartilage, and automated analysis of disease burden yielding a faster and more efficient workflow for a radiologist with a higher level of reproducibility and precision. It may also provide risk assessment tools for individual patients, which is an integral part of precision medicine.

Posterior Tibial Slope, Notch Width, Condylar Morphology, Trochlear Inclination, and Tibiofemoral Mismatch Predict Outcomes Following Anterior Cruciate Ligament Reconstruction.

PURPOSE: To provide a comprehensive summary of the available literature on the influence of bone morphology on outcomes after anterior cruciate ligament reconstruction (ACLR). METHODS: Our protocol was prospectively registered with PROSPERO (International Prospective Register of Systematic Reviews) and followed PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses) guidelines. The PubMed, Embase, and MEDLINE databases were searched for studies investigating knee morphologic features and outcomes after ACLR. Articles were screened and references lists were reviewed to identify relevant studies, after which methodologic quality was assessed for each study included in this review. Because of significant variability in terminology and methodology between studies, no meta-analyses were conducted. RESULTS: Systematically screening a total of 19,647 studies identified from the search revealed 24 studies that met the inclusion and exclusion criteria. Among tibial shape features identified as predictors of poor outcomes after ACLR, increased posterior tibial slope was most common (16 studies). Other features such as increased tibial plateau area (1 study), decreased medial plateau width (1 study), and increased medial plateau height (1 study) were also associated with poor outcomes. For the femur, features related to notch width and condylar morphology were most common (4 studies and 7 studies, respectively). An increased condylar offset ratio, increased lateral femoral condylar ratio, and larger notch width were each found to be associated with negative ACLR outcomes, including increased cartilage degeneration, worse patient-reported outcomes, and graft failure. CONCLUSIONS: Posterior tibial slope, notch width, condylar morphology, trochlear inclination, and tibiofemoral mismatch are associated with and predictive of outcomes after ACLR. LEVEL OF EVIDENCE: Level IV, systematic review of Level II-IV studies.

Hip-Knee Joint Coordination Patterns are Associated With Patellofemoral Joint Cartilage Composition in Patients With Anterior Cruciate Ligament Reconstruction.

Joint coordination variability during walking that is associated with patellofemoral joint cartilage degeneration after anterior cruciate ligament reconstruction are not well understood. The purpose of this study was to assess between-limb differences in joint coordination variability and to determine the relationship of coordination variability with postoperative patellofemoral joint cartilage composition. Thirty-five patients underwent bilateral gait analysis and a magnetic resonance exam of the reconstructed knee joint at 6 months post anterior cruciate ligament reconstruction. Vector coding was used to assess coordination variability during the early (1%-33%), mid (34%-66%), and late (67%-100%) stance phase. The T1ρ/T2 mapping was used to evaluate the glycosaminoglycan-collagen matrix of the patellar and femoral trochlear cartilage. Compared with the uninjured limb, the reconstructed limb exhibited higher hip sagittal/knee sagittal plane coordination variability during midstance as well as higher knee sagittal/ankle sagittal plane coordination variability during both mid and late stance. The hip sagittal/knee sagittal plane coordination variability during midstance predicted 14.6% of the variance in patellar cartilage T1ρ values within the reconstructed limb. In addition, sex of participants was able to predict 32.4% and 13.7% of the variance in femoral trochlea T1ρ and T2 values, respectively. The study results demonstrate that a multijoint mechanism may be associated with early patellofemoral joint cartilage degeneration at 6 months after anterior cruciate ligament reconstruction.

Augmenting Osteoporosis Imaging with Machine Learning.

PURPOSE OF REVIEW: In this paper, we discuss how recent advancements in image processing and machine learning (ML) are shaping a new and exciting era for the osteoporosis imaging field. With this paper, we want to give the reader a basic exposure to the ML concepts that are necessary to build effective solutions for image processing and interpretation, while presenting an overview of the state of the art in the application of machine learning techniques for the assessment of bone structure, osteoporosis diagnosis, fracture detection, and risk prediction. RECENT FINDINGS: ML effort in the osteoporosis imaging field is largely characterized by "low-cost" bone quality estimation and osteoporosis diagnosis, fracture detection, and risk prediction, but also automatized and standardized large-scale data analysis and data-driven imaging biomarker discovery. Our effort is not intended to be a systematic review, but an opportunity to review key studies in the recent osteoporosis imaging research landscape with the ultimate goal of discussing specific design choices, giving the reader pointers to possible solutions of regression, segmentation, and classification tasks as well as discussing common mistakes.

Development and Validation of a Multitask Deep Learning Model for Severity Grading of Hip Osteoarthritis Features on Radiographs.

Background A multitask deep learning model might be useful in large epidemiologic studies wherein detailed structural assessment of osteoarthritis still relies on expert radiologists' readings. The potential of such a model in clinical routine should be investigated. Purpose To develop a multitask deep learning model for grading radiographic hip osteoarthritis features on radiographs and compare its performance to that of attending-level radiologists. Materials and Methods This retrospective study analyzed hip joints seen on weight-bearing anterior-posterior pelvic radiographs from participants in the Osteoarthritis Initiative (OAI). Participants were recruited from February 2004 to May 2006 for baseline measurements, and follow-up was performed 48 months later. Femoral osteophytes (FOs), acetabular osteophytes (AOs), and joint-space narrowing (JSN) were graded as absent, mild, moderate, or severe according to the Osteoarthritis Research Society International atlas. Subchondral sclerosis and subchondral cysts were graded as present or absent. The participants were split at 80% (n = 3494), 10% (n = 437), and 10% (n = 437) by using split-sample validation into training, validation, and testing sets, respectively. The multitask neural network was based on DenseNet-161, a shared convolutional features extractor trained with multitask loss function. Model performance was evaluated in the internal test set from the OAI and in an external test set by using temporal and geographic validation consisting of routine clinical radiographs. Results A total of 4368 participants (mean age, 61.0 years ± 9.2 [standard deviation]; 2538 women) were evaluated (15 364 hip joints on 7738 weight-bearing anterior-posterior pelvic radiographs). The accuracy of the model for assessing these five features was 86.7% (1333 of 1538) for FOs, 69.9% (1075 of 1538) for AOs, 81.7% (1257 of 1538) for JSN, 95.8% (1473 of 1538) for subchondral sclerosis, and 97.6% (1501 of 1538) for subchondral cysts in the internal test set, and 82.7% (86 of 104) for FOS, 65.4% (68 of 104) for AOs, 80.8% (84 of 104) for JSN, 88.5% (92 of 104) for subchondral sclerosis, and 91.3% (95 of 104) for subchondral cysts in the external test set. Conclusion A multitask deep learning model is a feasible approach to reliably assess radiographic features of hip osteoarthritis. © RSNA, 2020 Online supplemental material is available for this article.

Lumbar intervertebral disc characterization through quantitative MRI analysis: An automatic voxel-based relaxometry approach.

PURPOSE: To develop an automated pipeline based on convolutional neural networks to segment lumbar intervertebral discs and characterize their biochemical composition using voxel-based relaxometry, and establish local associations with clinical measures of disability, muscle changes, and other symptoms of lower back pain. METHODS: This work proposes a new methodology using MRI (n = 31, across the spectrum of disc degeneration) that combines deep learning-based segmentation, atlas-based registration, and statistical parametric mapping for voxel-based analysis of T1ρ and T2 relaxation time maps to characterize disc degeneration and its associated disability. RESULTS: Across degenerative grades, the segmentation algorithm produced accurate, high-confidence segmentations of the lumbar discs in two independent data sets. Manually and automatically extracted mean disc T1ρ and T2 relaxation times were in high agreement for all discs with minimal bias. On a voxel-by-voxel basis, imaging-based degenerative grades were strongly negatively correlated with T1ρ and T2 , particularly in the nucleus. Stratifying patients by disability grades revealed significant differences in the relaxation maps between minimal/moderate versus severe disability: The average T1ρ relaxation maps from the minimal/moderate disability group showed clear annulus nucleus distinction with a visible midline, whereas the severe disability group had lower average T1ρ values with a homogeneous distribution. CONCLUSION: This work presented a scalable pipeline for fast, automated assessment of disc relaxation times, and voxel-based relaxometry that overcomes limitations of current region of interest-based analysis methods and may enable greater insights and associations between disc degeneration, disability, and lower back pain.