Faculty Bibliography

BACKGROUND:in the knee menisci can potentially be used as noninvasive biomarkers in detecting early-stage osteoarthritis (OA). PURPOSE/OBJECTIVE:relaxation mapping of human knee menisci. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:Eight healthy volunteers with no known inflammation, trauma, or pain in the knee and three symptomatic subjects with early knee OA. FIELD STRENGTH/SEQUENCE/UNASSIGNED:-weighted images on a 3 T MRI scanner. ASSESSMENT/RESULTS:relaxation values were assessed in 11 meniscal regions of interest (ROIs) using monoexponential and biexponential models. STATISTICAL TESTS/UNASSIGNED:Nonparametric rank-sum tests, Kruskal-Wallis test, and coefficient of variation. RESULTS:-long, respectively. DATA CONCLUSION/UNASSIGNED:was increased in medial, lateral, and body menisci of early OA while the biexponential numbers were decreased in early OA patients. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2019.

BACKGROUND:Osteoporosis (OP) results in weak bone and can ultimately lead to fracture. Drugs such as glucocorticoids can also induce OP (glucocorticoid-induced osteoporosis [GIO]). Bone marrow adipose tissue composition and quantity may play a role in OP pathophysiology, but has not been thoroughly studied in GIO compared to primary OP. PURPOSE/HYPOTHESIS/UNASSIGNED:Chemical shift-encoded (CSE) MRI allows detection of subregional differences in bone marrow adipose tissue composition and quantity in the proximal femur of GIO compared to OP subjects and has high agreement with the reference standard of magnetic resonance spectroscopy (MRS). STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:In all, 18 OP and 13 GIO subjects. FIELDS STRENGTH/UNASSIGNED:3T. SEQUENCE/UNASSIGNED:Multiple gradient-echo, stimulated echo acquisition mode (STEAM). ASSESSMENT/RESULTS:Subjects underwent CSE-MRI in the proximal femurs, and for each parametric map regions of interest (ROIs) were assessed in the femoral head (fHEAD), femoral neck (fNECK), Ward's triangle (fTRIANGLE), and the greater trochanter (GTROCH). In addition, we compared CSE-MRI against the reference standard of MRS performed in the femoral neck and Ward's triangle. STATISTICAL TESTS/UNASSIGNED:Differences between OP/GIO were investigated using the Mann-Whitney nonparametric test. Bland-Altman methodology was used to assess measurement agreement between CSE-MRI and MRS. RESULTS: DATA CONCLUSION/UNASSIGNED:3T CSE-MRI may allow reliable assessment of subregional bone marrow adipose tissue (bMAT) quantity and composition in the proximal femur in a clinically reasonable scan time. Glucocorticoids may alter the lipid profile of bMAT and potentially result in reduced bone quality. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

PURPOSE OF REVIEW/OBJECTIVE:Artificial intelligence tools have found new applications in medical diagnosis. These tools have the potential to capture underlying trends and patterns, otherwise impossible with previous modeling capabilities. Machine learning and deep learning models have found a role in osteoporosis, both to model the risk of fragility fracture, and to help with the identification and segmentation of images. RECENT FINDINGS/RESULTS:Here we survey the latest research in the artificial intelligence application to the prediction of osteoporosis that is published between January 2017 and March 2019. Around half of the articles that are covered here predict (by classification or regression) an indicator of osteoporosis, such as bone mass or fragility fractures; the other half of studies use tools for automatic segmentation of the images of patients with or at risk of osteoporosis. The data for these studies include diverse signal sources: acoustics, MRI, CT, and of course, X-rays. SUMMARY/CONCLUSIONS:New methods for automatic image segmentation, and prediction of fracture risk show promising clinical value. Though these recent developments have had a successful initial application to osteoporosis research, their development is still under improvement, such as accounting for positive/negative class bias. We urge care when reporting accuracy metrics, and when comparing such metrics between different studies.

BACKGROUND:A current challenge in osteoporosis is identifying patients at risk of bone fracture. PURPOSE/OBJECTIVE:To identify the machine learning classifiers that predict best osteoporotic bone fractures and, from the data, to highlight the imaging features and the anatomical regions that contribute most to prediction performance. STUDY TYPE/METHODS:Prospective (cross-sectional) case-control study. POPULATION/METHODS:. Field Strength/ Sequence: 3D FLASH at 3T. ASSESSMENT/RESULTS:Quantitative MRI outcomes by software algorithms. Mechanical and topological microstructural parameters of the trabecular bone were calculated for five femoral regions, and added to the vector of features together with bone mineral density measurement, fracture risk assessment tool (FRAX) score, and personal characteristics such as age, weight, and height. We fitted 15 classifiers using 200 randomized cross-validation datasets. Statistical Tests: Data: Kolmogorov-Smirnov test for normality. Model Performance: sensitivity, specificity, precision, accuracy, F1-test, receiver operating characteristic curve (ROC). Two-sided t-test, with P < 0.05 for statistical significance. RESULTS:The top three performing classifiers are RUS-boosted trees (in particular, performing best with head data, F1 = 0.64 ± 0.03), the logistic regression and the linear discriminant (both best with trochanteric datasets, F1 = 0.65 ± 0.03 and F1 = 0.67 ± 0.03, respectively). A permutation of these classifiers comprised the best three performers for four out of five anatomical datasets. After averaging across all the anatomical datasets, the score for the best performer, the boosted trees, was F1 = 0.63 ± 0.03 for All-features dataset, F1 = 0.52 ± 0.05 for the no-MRI dataset, and F1 = 0.48 ± 0.06 for the no-FRAX dataset. Data Conclusion: Of many classifiers, the RUS-boosted trees, the logistic regression, and the linear discriminant are best for predicting osteoporotic fracture. Both MRI and FRAX independently add value in identifying osteoporotic fractures. The femoral head, greater trochanter, and inter-trochanter anatomical regions within the proximal femur yielded better F1-scores for the best three classifiers. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2018.

Currently, clinical determination of pathologic fracture risk in the hip is conducted using measures of defect size and shape in the stance loading condition. However, these measures often do not consider how changing lesion locations or how various loading conditions impact bone strength. The goal of this study was to determine the impact of defect location on bone strength parameters in both the sideways fall and stance-loading conditions. We recruited 20 female subjects aged 48-77 years for this study and performed MRI of the proximal femur. Using these images, we simulated 10-mm pathologic defects in greater trochanter, superior, middle, and inferior femoral head, superior, middle, and inferior femoral neck, and lateral, middle, and medial proximal diaphysis to determine the effect of defect location on change in bone strength by performing finite element analysis. We compared the effect of each osteolytic lesion on bone stiffness, strength, resilience, and toughness. For the sideways fall loading, defects in the inferior femoral head (12.21%) and in the greater trochanter (6.43%) resulted in the greatest overall reduction in bone strength. For the stance loading, defects in the mid femoral head (-7.91%) and superior femoral head (-7.82%) resulted in the greatest overall reduction in bone strength. Changes in stiffness, yield force, ultimate force, resilience, and toughness were not found to be significantly correlated between the sideways fall and stance-loading for the majority of defect locations, suggesting that calculations based on the stance-loading condition are not predictive of the change in bone strength experienced in the sideways fall condition. While stiffness was significantly related to yield force (R²â€¯> 0.82), overall force (R²â€¯> 0.59), and resilience (R²â€¯> 0.55), in both, the stance-loading and sideways fall conditions for most defect locations, stiffness was not significantly related to toughness. Therefore, structure-dependent measure such as stiffness may not fully explain the post-yield measures, which depend on material failure properties. The data showed that MRI-based models have the sensitivity to determine the effect of pathologic lesions on bone strength.

BACKGROUND:Osteoporotic hip fractures heavily cost the health care system. Clinicians and patients can benefit from improved tools to assess bone health. Herein, we aim to develop a three-dimensional magnetic resonance imaging (MRI) method to assess cortical bone thickness and assess the ability of the method to detect regional changes in the proximal femur. METHODS:Eighty-nine patients underwent hip magnetic resonance imaging. FireVoxel and 3DSlicer were used to generate three-dimensional proximal femur models. ParaView was used to define five regions: head, neck, greater trochanter, intertrochanteric region, and subtrochanteric region. Custom software was used to calculate the cortical bone thickness and generate a color map of the proximal femur. Mean cortical thickness values for each region were calculated. Statistical t-tests were performed to evaluate differences in cortical thickness based on proximal femur region. Measurement reliability was evaluated using coefficient of variation, intraclass correlation coefficients, and overlap metrics. RESULTS:Three-dimensional regional cortical thickness maps for all subjects were generated. The subtrochanteric region was found to have the thickest cortical bone and the femoral head had the thinnest cortical bone. There were statistically significant differences between regions (p < 0.01) for all possible comparisons. CONCLUSIONS:Cortical bone is an important contributor to bone strength, and its thinning results in increased hip fracture risk. We describe the development and measurement reproducibility of an MRI tool permitting assessment of proximal femur cortical thickness. This study represents an important step toward longitudinal clinical trials interested in monitoring the effectiveness of drug therapy on proximal femur cortical thickness.

BACKGROUND:ρ along with the morphological assessment can potentially be used as noninvasive biomarkers in detecting early-stage OA. To correlate the biochemical and morphological data, submillimeter isotropic resolution for both studies is required. PURPOSE/OBJECTIVE:ρ relaxometry of human knee cartilage in vivo. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:Ten healthy volunteers with no known inflammation, trauma, or pain in the knee. FIELD STRENGTH/SEQUENCE/UNASSIGNED:ρ-weighted images on a 3T MRI scanner. ASSESSMENT/RESULTS:ρ relaxations were assessed in the articular cartilage of 10 healthy volunteers. STATISTICAL TESTS/UNASSIGNED:Nonparametric rank-sum tests. Bland-Altman analysis and coefficient of variation. RESULTS:for volume, and -0.78 mm and +0.46 mm for thickness, respectively. DATA CONCLUSION/UNASSIGNED:ρ relaxation of knee joint with 0.7 × 0.7 × 0.7 mm isotropic spatial resolution is demonstrated in vivo. Comparison with a standard method showed that the proposed technique is suitable for assessing the volume and thickness of articular cartilage. LEVEL OF EVIDENCE/METHODS:2 Technical Efficacy: Stage 1 J. Magn. Reson. Imaging 2018;00:000-000.

PURPOSE OF REVIEW:Hip fractures have catastrophic consequences. The purpose of this article is to review recent developments in high-resolution magnetic resonance imaging (MRI)-guided finite element analysis (FEA) of the hip as a means to determine subject-specific bone strength. RECENT FINDINGS:Despite the ability of DXA to predict hip fracture, the majority of fractures occur in patients who do not have BMD T scores less than - 2.5. Therefore, without other detection methods, these individuals go undetected and untreated. Of methods available to image the hip, MRI is currently the only one capable of depicting bone microstructure in vivo. Availability of microstructural MRI allows generation of patient-specific micro-finite element models that can be used to simulate real-life loading conditions and determine bone strength. MRI-based FEA enables radiation-free approach to assess hip fracture strength. With further validation, this technique could become a potential clinical tool in managing hip fracture risk.

Magnetic resonance imaging (MRI) has been proposed as a complimentary method to measure bone quality and assess fracture risk. However, manual segmentation of MR images of bone is time-consuming, limiting the use of MRI measurements in the clinical practice. The purpose of this paper is to present an automatic proximal femur segmentation method that is based on deep convolutional neural networks (CNNs). This study had institutional review board approval and written informed consent was obtained from all subjects. A dataset of volumetric structural MR images of the proximal femur from 86 subjects were manually-segmented by an expert. We performed experiments by training two different CNN architectures with multiple number of initial feature maps, layers and dilation rates, and tested their segmentation performance against the gold standard of manual segmentations using four-fold cross-validation. Automatic segmentation of the proximal femur using CNNs achieved a high dice similarity score of 0.95 ± 0.02 with precision = 0.95 ± 0.02, and recall = 0.95 ± 0.03. The high segmentation accuracy provided by CNNs has the potential to help bring the use of structural MRI measurements of bone quality into clinical practice for management of osteoporosis.