No abstract

This deep learning pipeline may lend towards diagnostic worklist prioritization, standardization, and generalizability in assessing anterior cruciate ligament lesions, in addition to point-of-care communication with patients by non-experts.

This study characterized the distribution of [ 18 F]-sodium fluoride (NaF) uptake and blood flow in the femur and acetabulum in hip osteoarthritis (OA) patients to find associations between bone remodeling and cartilage composition in the presence of morphological abnormalities using simultaneous positron emission tomography and magnetic resonance imaging (PET/MR), quantitative magnetic resonance imaging (MRI) and femur shape modeling. Ten patients underwent a [ 18 F]-NaF PET/MR dynamic scan of the hip simultaneously with: (i) fast spin-echo CUBE for morphology grading and (ii) T 1ρ /T 2 magnetization-prepared angle-modulated partitioned k-space spoiled gradient echo snapshots for cartilage, bone segmentation, bone shape modeling, and T 1ρ /T 2 quantification. The standardized uptake values (SUVs) and Patlak kinetic parameter (K pat ) were calculated for each patient as PET outcomes, using an automated post-processing pipeline. Shape modeling was performed to extract the variations in bone shapes in the patients. Pearson's correlation coefficients were used to study the associations between bone shapes, PET outcomes, and patient reported pain. Direct associations between quantitative MR and PET evidence of bone remodeling were established in the acetabulum and femur. Associations of shaft thickness with SUV in the femur (p = 0.07) and K pat in the acetabulum (p = 0.02), cam deformity with acetabular score (p = 0.09), osteophytic growth on the femur head with K pat (p = 0.01) were observed. Pain had increased correlations with SUV in the acetabulum (p = 0.14) and femur (p = 0.09) when shaft thickness was accounted for. This study demonstrated the ability of [ 18 F]-NaF PET-MRI, 3D shape modeling, and quantitative MRI to investigate cartilage-bone interactions and bone shape features in hip OA, providing potential investigative tools to diagnose OA.

Modes of variation in the femur, tibia and patella bones (modeled separately). Top to bottom (modes) with percentage of variation of that mode listed under its label. Left and right columns: mode of variation (T1ρ‐SUV) visualized at −5 and +5 standard deviations. by Tibrewala et al. 1462–1474

Background Accurate interpretation of hip MRI is time‐intensive and difficult, prone to inter‐ and intrareviewer variability, and lacks a universally accepted grading scale to evaluate morphological abnormalities. Purpose To 1) develop and evaluate a deep‐learning‐based model for binary classification of hip osteoarthritis (OA) morphological abnormalities on MR images, and 2) develop an artificial intelligence (AI)‐based assist tool to find if using the model predictions improves interreader agreement in hip grading. Study Type Retrospective study aimed to evaluate a technical development. Population A total of 764 MRI volumes (364 patients) obtained from two studies (242 patients from LASEM [FORCe] and 122 patients from UCSF), split into a 65–25–10% train, validation, test set for network training. Field Strength/Sequence 3T MRI, 2D T2 FSE, PD SPAIR. Assessment Automatic binary classification of cartilage lesions, bone marrow edema‐like lesions, and subchondral cyst‐like lesions using the MRNet, interreader agreement before and after using network predictions. Statistical Tests Receiver operating characteristic (ROC) curve, area under curve (AUC), specificity and sensitivity, and balanced accuracy. Results For cartilage lesions, bone marrow edema‐like lesions and subchondral cyst‐like lesions the AUCs were: 0.80 (95% confidence interval [CI] 0.65, 0.95), 0.84 (95% CI 0.67, 1.00), and 0.77 (95% CI 0.66, 0.85), respectively. The sensitivity and specificity of the radiologist for binary classification were: 0.79 (95% CI 0.65, 0.93) and 0.80 (95% CI 0.59, 1.02), 0.40 (95% CI –0.02, 0.83) and 0.72 (95% CI 0.59, 0.86), 0.75 (95% CI 0.45, 1.05) and 0.88 (95% CI 0.77, 0.98). The interreader balanced accuracy increased from 53%, 71% and 56% to 60%, 73% and 68% after using the network predictions and saliency maps. Data Conclusion We have shown that a deep‐learning approach achieved high performance in clinical classification tasks on hip MR images, and that using the predictions from the deep‐learning model improved the interreader agreement in all pathologies. Level of Evidence 3 Technical Efficacy Stage 1 J. Magn. Reson. Imaging 2020;52:1163–1172.

To explore bone shape features that are associated with patellofemoral joint (PFJ) osteoarthritic features. Thirty subjects with PFJ degeneration (six males, 53.2 ± 9.8 years) and 23 controls (12 males, 48.1 ± 10.6 years) were included. Magnetic resonance (MR) assessment was performed to provide bone segmentation, morpholgocial grading, and cartilage relaxation times. In addition, subject self‐reported symptoms were reported. Logistic regressions were used to identify the shape features that were associated with the presence and worsening of PFJ morphological lesions over 3 years, and worsening of self‐reported symptoms. Statistical parametric mapping was used to evaluate the associations between shape features and cartilage relaxation times at 3 years. Results indicated that subjects with PFJ degeneration exhibited a trochlea with longer lateral condyle and shallower trochlear groove (adjusted odds ratio [OR] = 0.30; 95% confidence interval [CI]: 0.10, 0.86; P = .025). Subjects with worsening of PFJ degeneration exhibited a patella with equally distributed facets (adjusted OR = 3.14; 95% CI: 1.05, 9.37; P = .040) and lateral bump (adjusted OR = 0.14; 95% CI: 0.02, 0.83; P = .030). No shape features were associated with worsening of self‐reported symptoms. Elevated T1ρ and T2 times at 3 years were associated with a patella with a lateral hook, equally distributed facets, round and thick as well as a trochlea larger in size (R = 0.38~0.46, P = .015~.025). The study demonstrated the ability of 3D statistical shape modeling to quantify patella and trochlear bone shape features that are associated with the presence and progression of PFJ osteoarthritic features.

Background: Modic 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.Methods: Seventy-five lumbar spine MRI exams that presented with acute-tochronic 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 T 1 -weighted sagittal lumbar spine images. Next, two radiologists segmented and labeled MCs from a combined T 1 -and T 2 -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. Results:The 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 < 0.05).Conclusions: This deep learning-based approach demonstrates substantial agreement with radiologists and may serve as a tool to improve inter-rater reliability in the assessment of MCs.Kenneth T. Gao and Radhika Tibrewala contributed equally to this study.

Structured Abstract 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 anatomical 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 0.77 or greater across networks, morphological metrics and biomechanical models were similar with Pearson R correlation coefficients 0.69 or greater when significant. Conclusions This study demonstrates the feasibility of substituting computer-generated for human-generated segmentations of major anatomical 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.