In this study quantitative MRI and gait analysis were used to investigate the relationships between proximal femur 3D bone shape, cartilage morphology, cartilage biochemical composition, and joint biomechanics in subject with hip Osteoarthritis (OA). Eighty subjects underwent unilateral hip MR-imaging: T1ρ and T2 relaxation times were extracted through voxel based relaxometry and bone shape was assessed with 3D MRI-based statistical shape modeling. In addition, 3D gait analysis was performed in seventy-six of the studied subjects. Associations between shape, cartilage lesion presence, severity, and cartilage T1ρ and T2 were analyzed with linear regression and statistical parametric mapping. An ad hoc analysis was performed to investigate biomechanics and shape associations. Our results showed that subjects with a higher neck shaft angle in the coronal plane (higher mode 1, coxa valga), thicker femoral neck and a less spherical femoral head (higher mode 5, pistol grip) exhibited more severe acetabular and femoral cartilage abnormalities, showing different interactions with demographics factors. Subjects with coxa valga also demonstrated a prolongation of T1ρ and T2. Subjects with pistol grip deformity exhibited reduced hip internal rotation angles and subjects with coxa valga exhibited higher peak hip adduction moment and moment impulse. The results of this study establish a clear relationship between 3D proximal femur shape variations and markers of hip joint degeneration-morphological, compositional, well as insight on the possible interactions with demographics and biomechanics, suggesting that 3D MRI-based bone shape maybe a promising biomarker of early hip joint degeneration. © 2017 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:330-341, 2018.

The goal of this study was to use quantitative MRI analysis to longitudinally observe the relationship between 3D proximal femur shape and hip joint degenerative changes. Forty-six subjects underwent unilateral hip MR imaging at three time points (baseline, 18 and 36 months). 3D shape analysis, hip cartilage T1ρ/T2 relaxation time quantification, and SHOMRI MRI grading were performed at each time point. Subjects were grouped based on KL, SHOMRI, and HOOS pain scores. Associations between these score groupings, time, and longitudinal variation in shape, were analyzed using a generalized estimating equation. One-way ANCOVA was conducted to evaluate change in shape as a predictor of the worsening of degenerative changes at 36 months. Our results demonstrated that subjects displayed an increase in the volume of the femoral head and neck (Mode 3) over time. This shape mode was significantly more prevalent in patients that reported pain. Longitudinal changes in this shape mode also served as borderline predictors of elevated T1ρ values (p = 0.055) and of cartilage lesions (p = 0.068). Subjects showed a change in the Femoral Neck Anteversion angle (FNA) over time (Mode 6). This shape mode showed a significant interaction with the presence of cartilage lesions. The results of this study suggest that specific variations in bone shape quantified through 3D-MRI based Statistical Shape modeling show an observable relationship with hip joint compositional and morphological changes. The shapes observed lead to early degenerative changes, which may lead into OA, thus confirming the important role of bone shape changes in the pathogenesis of OA.

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.