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: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.

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.

More than a decade after the introduction of compressed sensing (CS) in MRI, researchers are still working on ways to translate it into different research and clinical applications. The greatest advantage of CS in MRI is the reduced amount of k-space data needed to reconstruct images, which can be exploited to reduce scan time or to improve spatial resolution and volumetric coverage. Efficient data acquisition using CS is extremely important for compositional mapping of the musculoskeletal system in general and knee cartilage mapping techniques in particular. High-resolution quantitative information about tissue biochemical composition could be obtained in just a few minutes using CS MRI. However, in order to make this goal a reality, some issues still need to be addressed. In this article we review the current state of the art of CS methods for rapid compositional mapping of knee cartilage. Specifically, data acquisition strategies, image reconstruction algorithms, and data fitting models are discussed. Different CS studies for T₂ and T_1ρ mapping of knee cartilage are reviewed, with illustrative results. Future directions, opportunities, and challenges of rapid compositional mapping techniques are also discussed.

MR imaging is an indispensable instrument for the diagnosis of musculoskeletal diseases. In vivo MR imaging at 7T offers many advantages, including increased signal-to-noise ratio, higher spatial resolution, improved spectral resolution for spectroscopy, improved sensitivity for X-nucleus imaging, and decreased image acquisition times. There are also however technical challenges of imaging at a higher field strength compared with 1.5 and 3T MR imaging systems. We discuss the many potential opportunities as well as the challenges presented by 7T MR imaging systems and highlight recent developments in in vivo research imaging of musculoskeletal applications in general and cartilage, skeletal muscle, and bone in particular.

PURPOSE/OBJECTIVE:To determine the proportion of women working in academic musculoskeletal (MSK) radiology divisions and how this compares to national benchmarks for women in academic medicine. MATERIALS AND METHODS/METHODS:A list of academic radiology departments in the United States was compiled using the U.S. News and World Report listing of Best Hospitals for Orthopedics and Rheumatology to aid in department selection. Faculty information for MSK radiology divisions was obtained using websites pertinent to each department. Figures were obtained from the Association of American Medical Colleges and used as a benchmark for comparison. RESULTS:The 25 top-ranked hospitals with MSK radiology divisions were identified, with a total of 215 MSK trained radiologists. Female radiologists made up 33% of the MSK trained faculty (n = 71). This compares to 38% of fulltime female faculty in academic medicine. 79 assistant professor roles were identified, of which 35% were women (n = 28). 34% of associate professors were women (n = 11). Only 23% of professors were women (n = 10); this compares with 21% of female professors in academic medicine. Six MSK radiologists held either chair or vice-chair roles and three of these were women. Of the 24 chief or director roles identified, 30% of these were held by women. CONCLUSION/CONCLUSIONS:The proportions of women within various roles in academic MSK radiology are similar to and in some instances higher than corresponding national benchmarks. However, there remains a discrepancy between the sexes with an overall male majority. Awareness of this fact is the first step required to help correct this imbalance.

OBJECT/OBJECTIVE:To quantify and compare subregional proximal femur bone marrow fat composition in premenopausal and postmenopausal women using chemical shift-encoded-MRI (CSE-MRI). MATERIALS AND METHODS/METHODS:A multi gradient-echo sequence at 3 T was used to scan both hips of premenopausal (n = 9) and postmenopausal (n = 18) women. Subregional fat composition (saturation, poly-unsaturation, mono-unsaturation) was quantitatively assessed in the femoral head, femoral neck, Ward's triangle, greater trochanter, and proximal shaft in bone marrow adipose tissue and separately within red and yellow marrow adipose tissue. RESULTS:Significant differences in fat composition in postmenopausal compared to premenopausal women, which varied depending on the subregion analyzed, were found. Within both whole and yellow marrow adipose tissue, postmenopausal women demonstrated higher saturation (+14.7% to +43.3%), lower mono- (-11.4% to -33%) and polyunsaturation (-52 to -83%) (p < 0.05). Within red marrow adipose tissue, postmenopausal women demonstrated lower fat quantity (-16% to -24%) and decreased polyunsaturation (-80 to -120%) in the femoral neck, greater trochanter, and Ward's triangle (p < 0.05). CONCLUSION/CONCLUSIONS:CSE-MRI can be used to detect subregional differences in proximal femur marrow adipose tissue composition between pre- and post-menopausal women in clinically feasible scan times.

Introduction: Diagnostic laparoscopy (DL) is an increasingly used modality when approaching penetrating anterior abdominal injury (PAAI). Historically, exploratory laparotomy (EL) for PAAI can result in a 20% negative laparotomy, 5% mortality and 20% morbidity rate. Laparoscopically trained trauma surgeons can utilize a minimally invasive technique to quickly assess for intra-abdominal organ injury in hemodynamically stable patients. In the hands of a skilled surgeon, PAAI with a confirmed injury can be repaired through therapeutic laparoscopy (TL) or EL without delay in treatment. This study analyzes the safety and efficacy of using DL as a first line therapy for hemodynamically stable patients with PAAI. Methods: Between December 2006 and September 2016, 56 patients underwent DL after presenting to NYU Langone Hospital-Brooklyn Emergency Room with PAAI. A retrospective analysis was conducted to analyze protocol and treatment outcomes. Variables reviewed included Glasgow Coma Scale (GCS), Injury Severity Scale (ISS), FAST exam and/or CT scan results, length of stay (LOS), and postoperative complications. Based on outcomes, patients were cate-gorized into three groups: DL, DL with progression to TL, and DL with conversion to EL. Results: In the study period, a total of 94 patients presented with PAAI that went to the OR, 56 of which were initially treated laparoscopically. Causes of injury included stab wounds, gunshot wounds, traffic accidents, and self-inflicted injuries. The mean age was 40 +/- 12 years. The mean GCS was 14 +/- 2, and the mean ISS was 4 +/- 4. Of the 56 patients who underwent DL, 25 patients (44.6%) required no further intervention (group 1), 21 patients (37.5%) underwent TL (group 2), and 10 patients (17.8%) required EL (group 3). Mean LOS for groups 1, 2 and 3 were 4 +/- 3.3 days, 3 +/- 1.9 days and 6 +/- 4.5 days, respectively. There were no missed injuries or postoperative complications requiring the OR in all groups. TL included diaphragm laceration repairs, control of hepatic laceration and primary bowel repair. Conclusion: Diagnostic Laparoscopy should be considered first line for hemodynamically stable patients with PAAI with equivocal FAST and/or CT scan findings. Eighty-two percent of our patients did not require conversion to EL. This allowed for decreased postoperative pain, quicker recovery time, and shorter hospital stays. When in the hands of laparoscopically trained trauma surgeons, these patients can be quickly and safely treated while avoiding any delays in diagnosis

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.