Faculty Bibliography

BACKGROUND:Three-dimensional MR fingerprinting (3D-MRF) techniques have been recently described for simultaneous multiparametric mapping of knee cartilage. However, investigation of repeatability remains limited. PURPOSE/OBJECTIVE:maps using a 3D-MRF sequence for simultaneous measurement. STUDY TYPE/METHODS:Prospective. SUBJECTS/METHODS:Fourteen healthy subjects (35.4 ± 9.3 years, eight males), scanned on Day 1 and Day 7. FIELD STRENGTH/SEQUENCE/UNASSIGNED:maps. ASSESSMENT/RESULTS:maps were acquired using variable flip angles and a modified 3D-Turbo-Flash sequence with different echo and spin-lock times, respectively, and were fitted using mono-exponential models. Each sequence was acquired on Day 1 and Day 7 with two scans on each day. STATISTICAL TESTS/METHODS:were calculated in five manually segmented regions of interest (ROIs), including lateral femur, lateral tibia, medial femur, medial tibia, and patella cartilages. Intra-subject and inter-subject repeatabilities were assessed using coefficient of variation (CV) and intra-class correlation coefficient (ICC), respectively, on the same day and among different days. Regression and Bland-Altman analysis were performed to compare maps between the conventional and 3D-MRF sequences. RESULTS: > 0.59. CONCLUSION/CONCLUSIONS:with good agreement with conventional sequences. EVIDENCE LEVEL/METHODS:1 TECHNICAL EFFICACY: Stage 1.

BACKGROUND:parameters for an explanation as it relates to proteoglycan, collagen, and water content in these tissues. PURPOSE/OBJECTIVE:-PETRA) sequence. STUDY TYPE/METHODS:Prospective. POPULATION/METHODS:The study group consisted of 22 healthy subjects (11 females, ages: 41 ± 18 years, and 11 males, ages: 41 ± 14 years) with no known inflammation, trauma, or pain in the knee joint. FIELD STRENGTH/SEQUENCE/UNASSIGNED:-prepared 3D-PETRA sequence was used to acquire fat-suppressed images with varying spin-lock lengths (TSLs) of the knee joint at 3T. ASSESSMENT/RESULTS:parameters were measured in the anterior cruciate ligament (ACL), posterior cruciate ligament (PCL), and patellar tendon (PT) by manually drawing ROIs over the entirety of the tissues. STATISTICAL TESTS/METHODS:parameters. Statistical significance was defined as P < 0.05. RESULTS: = 0.28-0.74) with the exception of the short fraction in the PCL (P = 0.18), and the short relaxation time in the ACL (P = 0.58) and in the PCL (P = 0.14). DATA CONCLUSION/CONCLUSIONS:parameters in three ligaments of healthy volunteers, which are thought to be related to changes in tissue composition and structure during the aging process. LEVEL OF EVIDENCE/METHODS:2 TECHNICAL EFFICACY: Stage 1.

Different pathologies of the hip are characterized by the abnormal shape of the bony structures of the joint, namely the femur and the acetabulum. Three-dimensional (3D) models of the hip can be used for diagnosis, biomechanical simulation, and planning of surgical treatments. These models can be generated by building 3D surfaces of the joint's structures segmented on magnetic resonance (MR) images. Deep learning can avoid time-consuming manual segmentations, but its performance depends on the amount and quality of the available training data. Data augmentation and transfer learning are two approaches used when there is only a limited number of datasets. In particular, data augmentation can be used to artificially increase the size and diversity of the training datasets, whereas transfer learning can be used to build the desired model on top of a model previously trained with similar data. This study investigates the effect of data augmentation and transfer learning on the performance of deep learning for the automatic segmentation of the femur and acetabulum on 3D MR images of patients diagnosed with femoroacetabular impingement. Transfer learning was applied starting from a model trained for the segmentation of the bony structures of the shoulder joint, which bears some resemblance to the hip joint. Our results suggest that data augmentation is more effective than transfer learning, yielding a Dice similarity coefficient compared to ground-truth manual segmentations of 0.84 and 0.89 for the acetabulum and femur, respectively, whereas the Dice coefficient was 0.78 and 0.88 for the model based on transfer learning. The Accuracy for the two anatomical regions was 0.95 and 0.97 when using data augmentation, and 0.87 and 0.96 when using transfer learning. Data augmentation can improve the performance of deep learning models by increasing the diversity of the training dataset and making the models more robust to noise and variations in image quality. The proposed segmentation model could be combined with radiomic analysis for the automatic evaluation of hip pathologies.

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by high rates of reinjury. Evidence of remaining injury observed on magnetic resonance imaging (MRI) at the time of return to sport (RTS) may be associated with strength deficits and prognostic for reinjury, however, conventional imaging has failed to establish a relationship. Quantitative measure of muscle microstructure using diffusion tensor imaging (DTI) may hold potential for assessing a possible association between injury-related structural changes and clinical outcomes. The purpose of this study was to determine the association of RTS MRI-based quantitative measures, such as edema volume, muscle volume, and DTI metrics, with clinical outcomes (i.e., strength and reinjury) following HSI. Spearman's correlations and Firth logistic regressions were used to determine relationships in between-limb imaging measures and between-limb eccentric strength and reinjury status, respectively. Twenty injuries were observed, with four reinjuries. At the time of RTS, between-limb differences in eccentric hamstring strength were significantly associated with principal effective diffusivity eigenvalue λ₁ (r = -0.64, p = 0.003) and marginally associated with mean diffusivity (r = -0.46, p = 0.056). Significant relationships between other MRI-based measures of morphology and eccentric strength were not detected, as well as between any MRI-based measure and reinjury status. In conclusion, this preliminary evidence indicates DTI may track differences in hamstring muscle microstructure, not captured by conventional imaging at the whole muscle level, that relate to eccentric strength.

This article will provide a perspective review of the most extensively investigated deep learning (DL) applications for musculoskeletal disease detection that have the best potential to translate into routine clinical practice over the next decade. Deep learning methods for detecting fractures, estimating pediatric bone age, calculating bone measurements such as lower extremity alignment and Cobb angle, and grading osteoarthritis on radiographs have been shown to have high diagnostic performance with many of these applications now commercially available for use in clinical practice. Many studies have also documented the feasibility of using DL methods for detecting joint pathology and characterizing bone tumors on magnetic resonance imaging (MRI). However, musculoskeletal disease detection on MRI is difficult as it requires multi-task, multi-class detection of complex abnormalities on multiple image slices with different tissue contrasts. The generalizability of DL methods for musculoskeletal disease detection on MRI is also challenging due to fluctuations in image quality caused by the wide variety of scanners and pulse sequences used in routine MRI protocols. The diagnostic performance of current DL methods for musculoskeletal disease detection must be further evaluated in well-designed prospective studies using large image datasets acquired at different institutions with different imaging parameters and imaging hardware before they can be fully implemented in clinical practice. Future studies must also investigate the true clinical benefits of current DL methods and determine whether they could enhance quality, reduce error rates, improve workflow, and decrease radiologist fatigue and burnout with all of this weighed against the costs.

OBJECTIVE:To determine if MRI-based radiomics from hamstring muscles are related to injury and if the features could be used to perform a time to return to sport (RTS) classification. We hypothesize that radiomics from hamstring muscles, especially T2-weighted and diffusion tensor imaging-based features, are related to injury and can be used for RTS classification. SUBJECTS AND METHODS/METHODS:MRI data from 32 athletes at the University of Wisconsin-Madison that sustained a hamstring strain injury were collected. Diffusion tensor imaging and T1- and T2-weighted images were processed, and diffusion maps were calculated. Radiomics features were extracted from the four hamstring muscles in each limb and for each MRI modality, individually. Feature selection was performed and multiple support vector classifiers were cross-validated to differentiate between involved and uninvolved limbs and perform binary (≤ or > 25 days) and multiclass (< 14 vs. 14-42 vs. > 42 days) classification of RTS. RESULT/RESULTS:The combination of radiomics features from all diffusion tensor imaging and T2-weighted images provided the most accurate differentiation between involved and uninvolved limbs (AUC ≈ 0.84 ± 0.16). For the binary RTS classification, the combination of all extracted radiomics offered the most accurate classification (AUC ≈ 0.95 ± 0.15). While for the multiclass RTS classification, the combination of features from all the diffusion tensor imaging maps provided the most accurate classification (weighted one vs. rest AUC ≈ 0.81 ± 0.16). CONCLUSION/CONCLUSIONS:This pilot study demonstrated that radiomics features from hamstring muscles are related to injury and have the potential to predict RTS.

Hamstring strain injuries (HSI) are a common occurrence in athletics and complicated by limited prognostic indicators and high rates of reinjury. Assessment of injury characteristics at the time of injury (TOI) may be used to manage athlete expectations for time to return to sport (RTS) and mitigate reinjury risk. Magnetic resonance imaging (MRI) is routinely used in soft tissue injury management, but its prognostic value for HSI is widely debated. Recent advancements in musculoskeletal MRI, such as diffusion tensor imaging (DTI), have allowed for quantitative measures of muscle microstructure assessment. The purpose of this study was to determine the association of TOI MRI-based measures, including the British Athletic Muscle Injury Classification (BAMIC) system, edema volume, and DTI metrics, with time to RTS and reinjury incidence. Negative binomial regressions and generalized estimating equations were used to determine relationships between imaging measures and time to RTS and reinjury, respectively. Twenty-six index injuries were observed, with five recorded reinjuries. A significant association was not detected between BAMIC score and edema volume at TOI with days to RTS (p-values ≥ 0.15) or reinjury (p-values ≥ 0.13). Similarly, a significant association between DTI metrics and days to RTS was not detected (p-values ≥ 0.11). Although diffusivity metrics are expected to increase following injury, decreased values were observed in those who reinjured (mean diffusivity, p = 0.016; radial diffusivity, p = 0.02; principal effective diffusivity eigenvalues, p-values = 0.007-0.057). Additional work to further understand the directional relationship observed between DTI metrics and reinjury status and the influence of external factors is warranted.

While musculoskeletal imaging volumes are increasing, there is a relative shortage of subspecialized musculoskeletal radiologists to interpret the studies. Will artificial intelligence (AI) be the solution? For AI to be the solution, the wide implementation of AI-supported data acquisition methods in clinical practice requires establishing trusted and reliable results. This implementation will demand close collaboration between core AI researchers and clinical radiologists. Upon successful clinical implementation, a wide variety of AI-based tools can improve the musculoskeletal radiologist's workflow by triaging imaging examinations, helping with image interpretation, and decreasing the reporting time. Additional AI applications may also be helpful for business, education, and research purposes if successfully integrated into the daily practice of musculoskeletal radiology. The question is not whether AI will replace radiologists, but rather how musculoskeletal radiologists can take advantage of AI to enhance their expert capabilities.

OBJECTIVE:To identify the region of interest (ROI) to represent injury and observe between-limb diffusion tensor imaging (DTI) microstructural differences in muscle following hamstring strain injury. MATERIALS AND METHODS/METHODS:Participants who sustained a hamstring strain injury prospectively underwent 3T-MRI of bilateral thighs using T1, T2, and diffusion-weighted imaging at time of injury (TOI), return to sport (RTS), and 12 weeks after RTS (12wks). ROIs were using the hyperintense region on a T2-weighted sequence: edema, focused edema, and primary muscle injured excluding edema (no edema). Linear mixed-effects models were used to compare diffusion parameters between ROIs and timepoints and limbs and timepoints. RESULTS:(p-values = 0.058-0.12), and compared to 12wks (p-values < 0.02). In the no edema ROI, differences in diffusivity measures were not observed (p-values > 0.82). At TOI, no edema ROI diffusivity measures were lower than the edema ROI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.69). A significant limb-by-timepoint interaction was detected for all diffusivity measures with increased diffusion in the involved limb at TOI (p-values < 0.001) but not at RTS or 12wks (p-values > 0.42). Significant differences in fractional anisotropy over time or between limbs were not detected. CONCLUSION/CONCLUSIONS:Hyperintensity on T2-weighted imaging used to define the injured region holds promise in describing muscle microstructure following hamstring strain injury by demonstrating between-limb differences at TOI but not at follow-up timepoints.