Cranial bone imaging using ultrashort echo-time bone-selective MRI as an alternative to gradient-echo based "black-bone" techniques.

OBJECTIVES: CT is the clinical standard for surgical planning of craniofacial abnormalities in pediatric patients. This study evaluated three MRI cranial bone imaging techniques for their strengths and limitations as a radiation-free alternative to CT. METHODS: Ten healthy adults were scanned at 3 T with three MRI sequences: dual-radiofrequency and dual-echo ultrashort echo time sequence (DURANDE), zero echo time (ZTE), and gradient-echo (GRE). DURANDE bright-bone images were generated by exploiting bone signal intensity dependence on RF pulse duration and echo time, while ZTE bright-bone images were obtained via logarithmic inversion. Three skull segmentations were derived, and the overlap of the binary masks was quantified using dice similarity coefficient. Craniometric distances were measured, and their agreement was quantified. RESULTS: There was good overlap of the three masks and excellent agreement among craniometric distances. DURANDE and ZTE showed superior air-bone contrast (i.e., sinuses) and soft-tissue suppression compared to GRE. DISCUSSIONS: ZTE has low levels of acoustic noise, however, ZTE images had lower contrast near facial bones (e.g., zygomatic) and require effective bias-field correction to separate bone from air and soft-tissue. DURANDE utilizes a dual-echo subtraction post-processing approach to yield bone-specific images, but the sequence is not currently manufacturer-supported and requires scanner-specific gradient-delay corrections.

Machine learning-based mortality prediction in hip fracture patients using biomarkers.

The purpose of this retrospective study was to assess whether mortality following a hip fracture can be predicted by a machine learning model trained on basic blood and lab test data as well as basic demographic data. Additionally, the purpose was to identify the key variables most associated with 1-, 5-, and 10-year mortality and investigate their clinical significance. Input data included 3751 hip fracture patient records sourced from the Medical Information Mart for Intensive Care IV database, which provided records from in-hospital database systems at the Beth Israel Deaconess Medical Center. The 1-year mortality rate for all patients studied was 21% and for those aged 80+ was 29%. We assessed 10 different machine learning classification models, finding LightGBM to have the strongest 1-year mortality prediction performance, with accuracy of 81%, AUC of 0.79, sensitivity of 0.34, and specificity of 0.98 on the test set. The strongest-weighted features of the 1-year model included age, glucose, red blood cell distribution width, mean corpuscular hemoglobin concentration, white blood cells, urea nitrogen, prothrombin time, platelet count, calcium levels, and partial thromboplastin time. Most of these were also in the top 10 features of the LightGBM 5- and 10-year prediction models trained. Testing for these high-ranking biomarkers in new hip fracture patients can aid clinicians in assessing the likelihood of poor outcomes for hip fracture patients, and additional research can use these biomarkers to develop a mortality risk score.

Predicting Fractures Using Vertebral 18F-NaF Uptake in Prostate Cancer Patients.

BACKGROUND: Patients with prostate cancer tend to be at heightened risk for fracture due to bone metastases and treatment with androgen-deprivation therapy. Bone mineral density (BMD) derived from dual energy X-ray absorptiometry (DXA) is the standard for determining fracture risk in this population. However, BMD often fails to predict many osteoporotic fractures. Patients with prostate cancer also undergo 18F-sodium fluoride (18F-NaF)-positron emission tomography/computed tomography (PET/CT) to monitor metastases. The purpose of this study was to assess whether bone deposition, assessed by 18F-NaF uptake in 18F-NaF PET/CT, could predict incident fractures better than DXA- or CT-derived BMD in patients with prostate cancer. METHODS: This study included 105 males with prostate cancer who had undergone full body 18F-NaF PET/CT. Standardized uptake value (SUVmean and SUVmax) and CT-derived Hounsfield units (HU), a correlate of BMD, were recorded for each vertebral body. The average SUVmean, SUVmax, and HU were calculated for cervical, thoracic, lumbar, and sacral areas. The t-test was used to assess significant differences between fracture and no-fracture groups. RESULTS: The SUVmean and SUVmax values for the thoracic area were lower in the fracture group than in the no-fracture group. There was no significant difference in cervical, thoracic, lumbar or sacral HU between the 2 groups. CONCLUSIONS: Our study reports that lower PET-derived non-metastatic bone deposition in the thoracic spine is correlated with incidence of fractures in patients with prostate cancer. CT-derived HU, a correlate of DXA-derived BMD, was not predictive of fracture risk. 18F-NaF PET/CT may provide important insight into bone quality and fracture risk.

A Super-Resolution Diffusion Model for Recovering Bone Microstructure from CT Images.

Purpose: To use a diffusion-based deep learning model to recover bone microstructure from low-resolution images of the proximal femur, a common site of traumatic osteoporotic fractures. Materials and Methods: Training and testing data in this retrospective study consisted of high-resolution cadaveric micro-CT scans (n = 26), which served as ground truth. The images were downsampled prior to use for model training. The model was used to increase spatial resolution in these low-resolution images threefold, from 0.72 mm to 0.24 mm, sufficient to visualize bone microstructure. Model performance was validated using microstructural metrics and finite element simulation-derived stiffness of trabecular regions. Performance was also evaluated across a handful of image quality assessment metrics. Correlations between model performance and ground truth were assessed using intraclass correlation coefficients (ICCs) and Pearson correlation coefficients. Results: Compared with popular deep learning baselines, the proposed model exhibited greater accuracy (mean ICC of proposed model, 0.92 vs ICC of next best method, 0.83) and lower bias (mean difference in means, 3.80% vs 10.00%, respectively) across the physiologic metrics. Two gradient-based image quality metrics strongly correlated with accuracy across structural and mechanical criteria (r > 0.89). Conclusion: The proposed method may enable accurate measurements of bone structure and strength with a radiation dose on par with current clinical imaging protocols, improving the viability of clinical CT for assessing bone health.Keywords: CT, Image Postprocessing, Skeletal-Appendicular, Long Bones, Radiation Effects, Quantification, Prognosis, Semisupervised Learning Online supplemental material is available for this article. © RSNA, 2023.

The Application of Advanced Bone Imaging Technologies in Sports Medicine.

Until recently, the evaluation of bone health and fracture risk through imaging has been limited to dual-energy X-ray absorptiometry (DXA) and plain radiographs, with a limited application in the athletic population. Several novel imaging technologies are now available for the clinical assessment of bone health, including bone injury risk and healing progression, with a potential for use in sports medicine. Among these imaging modalities is high-resolution peripheral quantitative computed tomography (HR-pQCT) which is a promising technology that has been developed to examine the bone microarchitecture in both cortical and trabecular bone at peripheral anatomical sites. Technologies that do not expose patients to ionizing radiation are optimal, particularly for athletes who may require frequent imaging. One such alternative is diagnostic ultrasound, which is preferable due to its low cost and lack of radiation exposure. Furthermore, ultrasound, which has not been a common imaging modality for monitoring fracture healing, has been shown to potentially demonstrate earlier signs of union compared to conventional radiographs, including callus mineralization and density at the healing site. Through the use of conventional magnetic resonance imaging (MRI), finite element analysis (FEA) can be used to simulate the structural and mechanical properties of bone. On the other hand, the ultrashort echo time (UTE) MRI can evaluate cortical bone quality by detecting water bound to the organic bone matrix and free water, providing important information about bone porosity. Several novel bone imaging techniques originally developed for osteoporosis assessment have great potential to be utilized to improve the standard of care in bone fracture risk assessment and healing in sports medicine with much greater precision and less adverse radiation exposure.

Validation of Biomechanical Computed Tomography for Fracture Risk Classification in Metastatic Hormone-sensitive Prostate Cancer.

BACKGROUND: Guidelines recommend dual-energy x-ray absorptiometry (DXA) screening to assess fracture risk and benefit from antiresorptive therapy in men with metastatic hormone-sensitive prostate cancer (mHSPC) on androgen deprivation therapy (ADT). However, <30% of eligible patients undergo DXA screening. Biomechanical computed tomography (BCT) is a radiomic technique that measures bone mineral density (BMD) and bone strength from computed tomography (CT) scans. OBJECTIVE: To evaluate the (1) correlations between BCT- and DXA-assessed BMD, and (2) associations between BCT-assessed metrics and subsequent fracture. DESIGN, SETTING, AND PARTICIPANTS: A multicenter retrospective cohort study was conducted among patients with mHSPC between 2013 and 2020 who received CT abdomen/pelvis or positron emission tomography/CT within 48 wk before ADT initiation and during follow-up (48-96 wk after ADT initiation). OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: We used univariate logistic regression to assess the associations between BCT measurements and the primary outcomes of subsequent pathologic and nonpathologic fractures. RESULTS AND LIMITATIONS: Among 91 eligible patients, the median ([interquartile range) age was 67 yr (62-75), 44 (48.4%) were White, and 41 (45.1%) were Black. During the median follow-up of 82 wk, 17 men (18.6%) developed a pathologic and 15 (16.5%) a nonpathologic fracture. BCT- and DXA-assessed femoral-neck BMD T scores were strongly correlated (R2 = 0.93). On baseline CT, lower BCT-assessed BMD (odds ratio [OR] 1.80, 95% confidence interval or CI [1.10, 3.25], p = 0.03) was associated with an increased risk of a pathologic fracture. Lower femoral strength (OR 1.63, 95% CI [0.99, 2.71], p = 0.06) was marginally associated with an increased risk of a pathologic fracture. Neither BMD (OR 1.52, 95% CI [0.95, 2.63], p = 0.11) nor strength (OR 1.14, 95% CI [0.75, 1.80], p = 0.57) was associated with a nonpathologic fracture. BCT identified nine (9.9%) men eligible for antiresorptive therapy, of whom four (44%) were not treated. Limitations include low fracture numbers resulting in lower power to detect fracture associations. CONCLUSIONS: Among men diagnosed with mHSPC, BCT assessments were strongly correlated with DXA, predicted subsequent pathologic fracture, and identified additional men indicated for antiresorptive therapy. PATIENT SUMMARY: We assess whether biomechanical computer tomography (BCT) from routine computer tomography (CT) scans can identify fracture risk among patients recently diagnosed with metastatic prostate cancer. We find that BCT and dual-energy x-ray absorptiometry-derived bone mineral density are strongly correlated and that BCT accurately identifies the risk for future fracture. BCT may enable broader fracture risk assessment and facilitate timely interventions to reduce fracture risk in metastatic prostate cancer patients.

Six-minute, in vivo MRI quantification of proximal femur trabecular bone 3D microstructure.

BACKGROUND: Assessment of proximal femur trabecular bone microstructure in vivo by magnetic resonance imaging has recently been validated for acquiring information independent of bone mineral density in osteoporotic patients. However, the requisite signal-to-noise ratio (SNR) and resolution for interrogation of the trabecular microstructure at this anatomical location prolongs the scan duration and renders the imaging protocol clinically infeasible. Parallel imaging and compressed sensing (PICS) techniques can reduce the scan duration of the imaging protocol without substantially compromising image quality. The present work investigates the limits of acceleration for a commonly used PICS technique, ℓ1-ESPIRiT, for the purpose of quantifying measures of trabecular bone microarchitecture. Based on a desired error tolerance, a six-minute, prospectively accelerated variant of the imaging protocol was developed and assessed for intersession reproducibility and agreement with the longer reference scan. PURPOSE: To investigate the limits of acceleration for MRI-based trabecular bone quantification by parallel imaging and compressed sensing reconstruction, and to develop a prototypical imaging protocol for assessing the proximal femur microstructure in a clinically practical scan time. METHODS: Healthy participants (n = 11) were scanned by a 3D balanced steady-state free precession (bSSFP) sequence satisfying the Nyquist criterion with a scan duration of about 18 min. The raw data were retrospectively undersampled and reconstructed to mimic various acceleration factors ranging from 2 to 6. Trabecular volumes-of-interest in four major femoral regions (greater trochanter, intertrochanteric region, femoral neck, and femoral head) were analyzed and six relevant measures of trabecular bone microarchitecture (bone volume fraction, surface-to-curve ratio, erosion index, elastic modulus, trabecular thickness, plates-to-rods ratio) were obtained for images of all accelerations. To assess agreement, median percent error and intraclass correlation coefficients (ICCs) were computed using the fully-sampled data as reference. Based on this analysis, a prospectively 3-fold accelerated sequence with a duration of about 6 min was developed and the analysis was repeated. RESULTS: A prospective acceleration factor of 3 demonstrated comparable performance in reproducibility and absolute agreement to the fully-sampled scan. The median CoV over all image-derived metrics was generally <6 % and ICCs >0.70. Also, measurements from prospectively 3-fold accelerated scans demonstrated in general median percent errors of <7 % and ICCs >0.70. CONCLUSION: The present work proposes a method to make in vivo quantitative assessment of proximal femur trabecular microstructure with a clinically practical scan duration of about 6 min.

Conquering the Cobb Angle: A Deep Learning Algorithm for Automated, Hardware-Invariant Measurement of Cobb Angle on Radiographs in Patients with Scoliosis.

Scoliosis is a disease estimated to affect more than 8% of adults in the United States. It is diagnosed with use of radiography by means of manual measurement of the angle between maximally tilted vertebrae on a radiograph (ie, the Cobb angle). However, these measurements are time-consuming, limiting their use in scoliosis surgical planning and postoperative monitoring. In this retrospective study, a pipeline (using the SpineTK architecture) was developed that was trained, validated, and tested on 1310 anterior-posterior images obtained with a low-dose stereoradiographic scanning system and radiographs obtained in patients with suspected scoliosis to automatically measure Cobb angles. The images were obtained at six centers (2005-2020). The algorithm measured Cobb angles on hold-out internal (n = 460) and external (n = 161) test sets with less than 2° error (intraclass correlation coefficient, 0.96) compared with ground truth measurements by two experienced radiologists. Measurements, produced in less than 0.5 second, did not differ significantly (P = .05 cutoff) from ground truth measurements, regardless of the presence or absence of surgical hardware (P = .80), age (P = .58), sex (P = .83), body mass index (P = .63), scoliosis severity (P = .44), or image type (low-dose stereoradiographic image vs radiograph; P = .51) in the patient. These findings suggest that the algorithm is highly robust across different clinical characteristics. Given its automated, rapid, and accurate measurements, this network may be used for monitoring scoliosis progression in patients. Keywords: Cobb Angle, Convolutional Neural Network, Deep Learning Algorithms, Pediatrics, Machine Learning Algorithms, Scoliosis, Spine Supplemental material is available for this article. © RSNA, 2023.

Machine Learning and Artificial Intelligence: A Web-Based Implant Failure and Peri-implantitis Prediction Model for Clinicians.

PURPOSE: To develop a machine learning model that can predict dental implant failure and peri-implantitis as a tool for maximizing implant success. MATERIALS AND METHODS: This study used a supervised learning model to retrospectively analyze 398 unique patients receiving a total of 942 dental implants presenting at the Philadelphia Veterans Affairs Medical Center from 2006 to 2013. Logistic regression, random forest classifiers, support vector machines, and ensemble techniques were employed to analyze this dataset. RESULTS: The random forest model possessed the highest predictive performance on test sets, with receiver operating characteristic area under curves (ROC AUC) of 0.872 and 0.840 for dental implant failures and peri-implantitis, respectively. The five most important features correlating with implant failure were amount of local anesthetic, implant length, implant diameter, use of preoperative antibiotics, and frequency of hygiene visits. The five most important features correlating with peri-implantitis were implant length, implant diameter, use of preoperative antibiotics, frequency of hygiene visits, and presence of diabetes mellitus. CONCLUSION: This study demonstrated the ability of machine learning models to assess demographics, medical history, and surgical plans, as well as the influence of these factors on dental implant failure and peri-implantitis. This model may serve as a resource for clinicians in the treatment of dental implants. Int J Oral Maxillofac Implants 2023;38:576-582. doi: 10.11607/jomi.9852.

Automated, calibration-free quantification of cortical bone porosity and geometry in postmenopausal osteoporosis from ultrashort echo time MRI and deep learning.

BACKGROUND: Assessment of cortical bone porosity and geometry by imaging in vivo can provide useful information about bone quality that is independent of bone mineral density (BMD). Ultrashort echo time (UTE) MRI techniques of measuring cortical bone porosity and geometry have been extensively validated in preclinical studies and have recently been shown to detect impaired bone quality in vivo in patients with osteoporosis. However, these techniques rely on laborious image segmentation, which is clinically impractical. Additionally, UTE MRI porosity techniques typically require long scan times or external calibration samples and elaborate physics processing, which limit their translatability. To this end, the UTE MRI-derived Suppression Ratio has been proposed as a simple-to-calculate, reference-free biomarker of porosity which can be acquired in clinically feasible acquisition times. PURPOSE: To explore whether a deep learning method can automate cortical bone segmentation and the corresponding analysis of cortical bone imaging biomarkers, and to investigate the Suppression Ratio as a fast, simple, and reference-free biomarker of cortical bone porosity. METHODS: In this retrospective study, a deep learning 2D U-Net was trained to segment the tibial cortex from 48 individual image sets comprised of 46 slices each, corresponding to 2208 training slices. Network performance was validated through an external test dataset comprised of 28 scans from 3 groups: (1) 10 healthy, young participants, (2) 9 postmenopausal, non-osteoporotic women, and (3) 9 postmenopausal, osteoporotic women. The accuracy of automated porosity and geometry quantifications were assessed with the coefficient of determination and the intraclass correlation coefficient (ICC). Furthermore, automated MRI biomarkers were compared between groups and to dual energy X-ray absorptiometry (DXA)- and peripheral quantitative CT (pQCT)-derived BMD. Additionally, the Suppression Ratio was compared to UTE porosity techniques based on calibration samples. RESULTS: The deep learning model provided accurate labeling (Dice score 0.93, intersection-over-union 0.88) and similar results to manual segmentation in quantifying cortical porosity (R2 ≥ 0.97, ICC ≥ 0.98) and geometry (R2 ≥ 0.82, ICC ≥ 0.75) parameters in vivo. Furthermore, the Suppression Ratio was validated compared to established porosity protocols (R2 ≥ 0.78). Automated parameters detected age- and osteoporosis-related impairments in cortical bone porosity (P ≤ .002) and geometry (P values ranging from <0.001 to 0.08). Finally, automated porosity markers showed strong, inverse Pearson's correlations with BMD measured by pQCT (|R| ≥ 0.88) and DXA (|R| ≥ 0.76) in postmenopausal women, confirming that lower mineral density corresponds to greater porosity. CONCLUSION: This study demonstrated feasibility of a simple, automated, and ionizing-radiation-free protocol for quantifying cortical bone porosity and geometry in vivo from UTE MRI and deep learning.

MRI Quantification of Cortical Bone Porosity, Mineralization, and Morphologic Structure in Postmenopausal Osteoporosis.

Background Preclinical studies have suggested that solid-state MRI markers of cortical bone porosity, morphologic structure, mineralization, and osteoid density are useful measures of bone health. Purpose To explore whether MRI markers of cortical bone porosity, morphologic structure, mineralization, and osteoid density are affected in postmenopausal osteoporosis (OP) and to examine associations between MRI markers and bone mineral density (BMD) in postmenopausal women. Materials and Methods In this single-center study, postmenopausal women were prospectively recruited from January 2019 to October 2020 into two groups: participants with OP who had not undergone treatment, defined as having any dual-energy x-ray absorptiometry (DXA) T-score of -2.5 or less, and age-matched control participants without OP (hereafter, non-OP). Participants underwent MRI in the midtibia, along with DXA in the hip and spine, and peripheral quantitative CT in the midtibia. Specifically, MRI measures of cortical bone porosity (pore water and total water), osteoid density (bound water [BW]), morphologic structure (cortical bone thickness), and mineralization (phosphorous [P] density [31P] and 31P-to-BW concentration ratio) were quantified at 3.0 T. MRI measures were compared between OP and non-OP groups and correlations with BMD were assessed. Results Fifteen participants with OP (mean age, 63 years ± 5 [SD]) and 19 participants without OP (mean age, 65 years ± 6) were evaluated. The OP group had elevated pore water (11.6 mol/L vs 9.5 mol/L; P = .007) and total water densities (21.2 mol/L vs 19.7 mol/L; P = .03), and had lower cortical bone thickness (4.8 mm vs 5.6 mm; P < .001) and 31P density (6.4 mol/L vs 7.5 mol/L; P = .01) than the non-OP group, respectively, although there was no evidence of a difference in BW or 31P-to-BW concentration ratio. Pore and total water densities were inversely associated with DXA and peripheral quantitative CT BMD (P < .001), whereas cortical bone thickness and 31P density were positively associated with DXA and peripheral quantitative CT BMD (P = .01). BW, 31P density, and 31P-to-BW concentration ratio were positively associated with DXA (P < .05), but not with peripheral quantitative CT. Conclusion Solid-state MRI of cortical bone was able to help detect potential impairments in parameters reflecting porosity, morphologic structure, and mineralization in postmenopausal osteoporosis. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Bae in this issue.

Atherogenic Indices as a Predictor of Aortic Calcification in Prostate Cancer Patients Assessed Using 18F-Sodium Fluoride PET/CT.

A major pathophysiological cause of cardiovascular disease is vascular plaque calcification. Fluorine 18−Sodium Fluoride (18F-NaF) PET/CT can be used as a sensitive imaging modality for detection of vascular calcification. The aim of this study was to find a non-invasive, cost-efficient, and readily available metric for predicting vascular calcification severity. This retrospective study was performed on 36 participants who underwent 18F-NaF fused PET/CT scans. The mean standard uptake values (SUVs) were calculated from manually sectioned axial sections over the aortic arch and thoracic aorta. Correlation analyses were performed between SUVs and calculated atherogenic indices (AIs). Castelli's Risk Index I (r = 0.63, p < 0.0001), Castelli's Risk Index II (r = 0.64, p < 0.0001), Atherogenic Coefficient (r = 0.63, p < 0.0001), Atherogenic Index of Plasma (r = 0.51, p = 0.00152), and standalone high-density lipoprotein (HDL) cholesterol (r = −0.53, p = 0.000786) were associated with aortic calcification. AIs show strong association with aortic arch and thoracic aorta calcifications. AIs are better predictors of vascular calcification compared to standalone lipid metrics, with the exception of HDL cholesterol. Clinical application of AIs provides a holistic metric beneficial for enhancing screening and treatment protocols.

Computed Tomography-Based Stiffness Measures of Trabecular Bone Microstructure: Cadaveric Validation and In Vivo Application.

Osteoporosis causes bone fragility and elevates fracture risk. Applications of finite element (FE) analysis (FEA) for assessment of trabecular bone (Tb) microstructural strength at whole-body computed tomography (CT) imaging are limited due to challenges with Tb microstructural segmentation. We present a nonlinear FEA method for distal tibia CT scans evading binary segmentation of Tb microstructure, while accounting for bone microstructural distribution. First, the tibial axis in a CT scan was aligned with the FE loading axis. FE cubic mesh elements were modeled using image voxels, and CT intensity values were calibrated to ash density defining mechanical properties at individual elements. For FEA of an upright volume of interest (VOI), the bottom surface was fixed, and a constant displacement was applied at each vertex on the top surface simulating different loading conditions. The method was implemented and optimized using the ANSYS software. CT-derived computational modulus values were repeat scan reproducible (intraclass correlation coefficient [ICC] ≥ 0.97) and highly correlated (r ≥ 0.86) with the micro-CT (µCT)-derived values. FEA-derived von Mises stresses over the segmented Tb microregion were significantly higher (p < 1 × 10-11) than that over the marrow space. In vivo results showed that both shear and compressive modulus for males were higher (p < 0.01) than for females. Effect sizes for different modulus measures between males and females were moderate-to-high (≥0.55) and reduced to small-to-negligible (<0.40) when adjusted for pure lean mass. Among body size and composition attributes, pure lean mass and height showed highest (r ∈ [0.45 0.56]) and lowest (r ∈ [0.25 0.39]) linear correlation, respectively, with FE-derived modulus measures. In summary, CT-based nonlinear FEA provides an effective surrogate measure of Tb microstructural stiffness, and the relaxation of binary segmentation will extend the scope for FEA in human studies using in vivo imaging at relatively low-resolution. © 2022 The Authors. JBMR Plus published by Wiley Periodicals LLC on behalf of American Society for Bone and Mineral Research.

Pediatric Osteosarcoma: Correlation of Imaging Findings with Histopathologic Features, Treatment, and Outcome.

Osteosarcoma is the most common primary bone sarcoma in children. Imaging plays a pivotal role in diagnostic workup, surgical planning, and follow-up monitoring for possible disease relapse. Survival depends on multiple factors, including presence or absence of metastatic disease, chemotherapy response, and surgical margins. At diagnosis, radiography and anatomic MRI are used to characterize the primary site of disease, whereas chest CT and whole-body bone scintigraphy and/or PET are used to identify additional sites of disease. Treatment starts with neoadjuvant chemotherapy, followed by en bloc tumor resection and limb reconstruction, and finally, adjuvant chemotherapy. Preoperative planning requires precise tumor delineation, which traditionally has been based on high-spatial-resolution anatomic MRI to identify tumor margins (medullary and extraosseous), skip lesions, neurovascular involvement, and joint invasion. These findings direct the surgical approach and affect the options for reconstruction. For skeletally immature children, the risk of cumulative limb-length discrepancy and need for superior longevity of the reconstruction have led to the advent and preferential use of several pediatric-specific surgical techniques, including rotationplasty, joint preservation surgery, autograft or allograft reconstruction, and extendible endoprostheses. A better understanding of the clinically impactful imaging features can directly and positively influence patient care. Online supplemental material is available for this article. ©RSNA, 2022.

Finite element analysis of trabecular bone microstructure using CT imaging and continuum mechanical modeling.

PURPOSE: Osteoporosis is a bone disease associated with enhanced bone loss, microstructural degeneration, and fracture-risk. Finite element (FE) modeling is used to estimate trabecular bone (Tb) modulus from high-resolution three-dimensional (3-D) imaging modalities including micro-computed tomography (CT), magnetic resonance imaging (MRI), and high-resolution peripheral quantitative CT (HR-pQCT). This paper validates an application of voxel-based continuum finite element analysis (FEA) to predict Tb modulus from clinical CT imaging under a condition similar to in vivo imaging by comparing with measures derived by micro-CT and experimental approaches. METHOD: Voxel-based continuum FEA methods for CT imaging were implemented using linear and nonlinear models and applied on distal tibial scans under a condition similar to in vivo imaging. First, tibial axis in a CT scan was aligned with the coordinate z-axis at 150 µm isotropic voxels. FEA was applied on an upright cylindrical volume of interests (VOI) with its axis coinciding with the tibial bone axis. Voxel volume, edge, and vertex elements and their connectivity were defined as per the isotropic image grid. A calibration phantom was used to calibrate CT numbers in Hounsfield unit to bone mineral density (BMD) values, which was then converted into calcium hydroxyapatite (CHA) density. Mechanical properties at each voxel volume element was defined using its ash-density defined on CT-derived CHA density. For FEA, the bottom surface of the cylindrical VOI was fixed and a constant displacement was applied along the z-direction at each vertex element on the top surface to simulate a physical axial compressive loading condition. Finally, a Poisson's ratio of 0.3 was applied, and Tb modulus (MPa) was computed as the ratio of average von Mises stress (MPa) of volume elements on the top surface and the applied displacement. FEA parameters including mesh element size, substep number, and different tolerance values were optimized. RESULTS: CT-derived Tb modulus values using continuum FEA showed high linear correlation with the micro-CT-derived reference values (r ∈ [0.87 0.90]) as well as experimentally measured values (r ∈ [0.80 0.87]). Linear correlation of computed modulus with their reference values using continuum FEA with linear modeling was comparable with that obtained by nonlinear modeling. Nonlinear continuum FEA-based modulus values (mean of 1087.2 MPa) showed greater difference from their reference values (mean of 1498.9 MPa using micro-CT-based FEA) as compared with linear continuum methods. High repeat CT scan reproducibility (intra-class correlation [ICC] = 0.98) was observed for computed modulus values using both linear and nonlinear continuum FEA. It was observed that high stress regions coincide with Tb microstructure as fuzzily characterized by BMD values. Distributions of von Mises stress over Tb microstructure and marrow regions were significantly different (p < 10-8 ). CONCLUSION: Voxel-based continuum FEA offers surrogate measures of Tb modulus from CT imaging under a condition similar to in vivo imaging that alleviates the need for segmentation of Tb and marrow regions, while accounting for bone distribution at the microstructural level. This relaxation of binary segmentation will extend the scope of FEA application to assess mechanical properties of bone microstructure at relatively low-resolution imaging.

Vertebral Deformity Measurements at MRI, CT, and Radiography Using Deep Learning.

PURPOSE: To construct and evaluate the efficacy of a deep learning system to rapidly and automatically locate six vertebral landmarks, which are used to measure vertebral body heights, and to output spine angle measurements (lumbar lordosis angles [LLAs]) across multiple modalities. MATERIALS AND METHODS: In this retrospective study, MR (n = 1123), CT (n = 137), and radiographic (n = 484) images were used from a wide variety of patient populations, ages, disease stages, bone densities, and interventions (n = 1744 total patients, 64 years ± 8, 76.8% women; images acquired 2005-2020). Trained annotators assessed images and generated data necessary for deformity analysis and for model development. A neural network model was then trained to output vertebral body landmarks for vertebral height measurement. The network was trained and validated on 898 MR, 110 CT, and 387 radiographic images and was then evaluated or tested on the remaining images for measuring deformities and LLAs. The Pearson correlation coefficient was used in reporting LLA measurements. RESULTS: On the holdout testing dataset (225 MR, 27 CT, and 97 radiographic images), the network was able to measure vertebral heights (mean height percentage of error ± 1 standard deviation: MR images, 1.5% ± 0.3; CT scans, 1.9% ± 0.2; radiographs, 1.7% ± 0.4) and produce other measures such as the LLA (mean absolute error: MR images, 2.90°; CT scans, 2.26°; radiographs, 3.60°) in less than 1.7 seconds across MR, CT, and radiographic imaging studies. CONCLUSION: The developed network was able to rapidly measure morphometric quantities in vertebral bodies and output LLAs across multiple modalities.Keywords: Computer Aided Diagnosis (CAD), MRI, CT, Spine, Demineralization-Bone, Feature Detection Supplemental material is available for this article. © RSNA, 2021.

Pulmonary vascular inflammation with fatal coronavirus disease 2019 (COVID-19): possible role for the NLRP3 inflammasome.

BACKGROUND: Pulmonary hyperinflammation is a key event with SARS-CoV-2 infection. Acute respiratory distress syndrome (ARDS) that often accompanies COVID-19 appears to have worse outcomes than ARDS from other causes. To date, numerous lung histological studies in cases of COVID-19 have shown extensive inflammation and injury, but the extent to which these are a COVID-19 specific, or are an ARDS and/or mechanical ventilation (MV) related phenomenon is not clear. Furthermore, while lung hyperinflammation with ARDS (COVID-19 or from other causes) has been well studied, there is scarce documentation of vascular inflammation in COVID-19 lungs. METHODS: Lung sections from 8 COVID-19 affected and 11 non-COVID-19 subjects, of which 8 were acute respiratory disease syndrome (ARDS) affected (non-COVID-19 ARDS) and 3 were from subjects with non-respiratory diseases (non-COVID-19 non-ARDS) were H&E stained to ascertain histopathological features. Inflammation along the vessel wall was also monitored by expression of NLRP3 and caspase 1. RESULTS: In lungs from COVID-19 affected subjects, vascular changes in the form of microthrombi in small vessels, arterial thrombosis, and organization were extensive as compared to lungs from non-COVID-19 (i.e., non-COVID-19 ARDS and non-COVID-19 non-ARDS) affected subjects. The expression of NLRP3 pathway components was higher in lungs from COVID-19 ARDS subjects as compared to non-COVID-19 non-ARDS cases. No differences were observed between COVID-19 ARDS and non-COVID-19 ARDS lungs. CONCLUSION: Vascular changes as well as NLRP3 inflammasome pathway activation were not different between COVID-19 and non-COVID-19 ARDS suggesting that these responses are not a COVID-19 specific phenomenon and are possibly more related to respiratory distress and associated strategies (such as MV) for treatment.

Chronic Workload, Subjective Arm Health, and Throwing Injury in High School Baseball Players: 3-Year Retrospective Pilot Study.

BACKGROUND: Baseball workloads are monitored by pitch counts, appearances, innings per appearance, ball velocity, and distance, whereas current workload standards neglect throws made during nongame situations. The association between total workloads, subjective measures, and injury in baseball is poorly understood. The question remains whether baseball athletes are at higher risk of injury by throwing more often or if they generate injury resilience when appropriately transitioned into the higher demands of throwing. HYPOTHESIS: Increased chronic load, along with subjective arm health measures, are related to decreased injury risk. STUDY DESIGN: Clinical research. LEVEL OF EVIDENCE: Level 3. METHODS: A total of 49 male baseball players (age 17.9 ± 0.4 years, height 181.8 ± 6.8 cm, body mass 80.6 ± 9.1 kg) competing at the varsity high school level were included in this 3-year retrospective data analysis from 2016 to 2019. Players wore the motusTHROW sleeve and sensor during all throwing activities. RESULTS: A total of 898,492 throws and 9455 athletic exposures were captured with the motusTHROW sensors. There were 24 injuries recorded throughout the 3-year analysis, with 11 throwing-related and 13 non-throwing related injuries. Results of the 1-way analysis of variance found chronic load was significantly related to throwing-injury occurrence (P < 0.01). Six of the throwing-related injuries occurred when athletes had a chronic load greater than 11.3, marking 75th percentile across all observations. There was a relationship between arm health and throwing arm-related injury occurrence (P < 0.01). Higher chronic load was associated with increased throwing-related injuries even when adjusted for arm health (P = 0.01). Specifically, injuries were more likely to occur in pitchers (either as a combination player or pitcher only) with a chronic load greater than 9.2. CONCLUSION: This study revealed a significant relationship between chronic load, subjective arm health, and throwing-related injury in varsity high school baseball players. Contrary to our hypothesis, increased chronic load was associated with increased injury risk. However, subjective arm health measures remain a relevant factor in assessing injury risk. Normative data for this population also provide key information around positional demands along with overall demands of the sport during the competitive season and off-season.

Vascular Inflammation in Lungs of Patients with Fatal Coronavirus Disease 2019 (COVID-19): Possible Role for the NLRP3 Inflammasome.

Background: Hyperinflammation is a key event that occurs with SARS-CoV-2 infection. In the lung, hyperinflammation leads to structural damage to tissue. To date, numerous lung histological studies have shown extensive alveolar damage, but there is scarce documentation of vascular inflammation in postmortem lung tissue. Methods: Lung sections from 8 COVID-19 affected and 11 non-COVID-19 subjects [of which 8 were acute respiratory disease syndrome (ARDS) affected and 3 were from subjects with non-respiratory diseases] were stained for H & E to ascertain histopathological features including presence of thrombi/microthrombi. Inflammation along the vessel wall was also monitored by quantification of the expression of moieties of the NLRP3 inflammasome pathway (NLRP3 and caspase-1). Results: In lungs from "fatal COVID-19", vascular changes in the form of microthrombi in small vessels, arterial thrombosis, and organization were extensive as compared to lungs from "non-COVID-19 non respiratory disease" affected subjects. The NLRP3 pathway components were significantly higher in lungs from COVID-19 subjects as compared to non-COVID-19 fatal cases without respiratory disease. No significant differences were observed between COVID-19 lungs and non-COVID-19 ARDS lungs. Conclusion: We posit that inflammasome formation along the vessel wall is a characteristic of lung inflammation that accompanies COVID-19. Thus, the NLRP3 inflammasome pathway seems to be probable candidate that drives amplification of inflammation post SARS-CoV-2 infection.

Changes in bone microarchitecture following parathyroidectomy in patients with secondary hyperparathyroidism.

BACKGROUND: Secondary hyperparathyroidism (SHPT) in patients with chronic kidney disease (CKD) has a significant effect on bone, affecting both trabecular and cortical compartments. Although parathyroidectomy results in biochemical improvement in mineral metabolism, changes in bone microarchitecture as evaluated by high-resolution imaging modalities are not known. Magnetic resonance imaging (MRI) provides in-depth three-dimensional assessment of bone microarchitecture, as well as determination of mechanical bone strength determined by finite element analysis (FEA). METHODS: We conducted a single-centre longitudinal study to evaluate changes in bone microarchitecture with MRI in patients with SHPT undergoing parathyroidectomy. MRI was performed at the distal tibia at baseline (time of parathyroidectomy) and at least 12 months following surgery. Trabecular and cortical topological parameters as well as bone mechanical competence using FEA were assessed. RESULTS: Fifteen patients with CKD (12 male, 3 female) underwent both MRI scans at the time of surgery and at least 12 months post-surgery. At baseline, 13 patients were on dialysis, one had a functioning kidney transplant, and one was pre-dialysis with stage 5 CKD. Seven patients received a kidney transplant following parathyroidectomy prior to follow-up MRI. MRI parameters in patients at follow up were consistent with loss in trabecular and cortical bone thickness (p = 0.006 and 0.03 respectively). Patients who underwent a kidney transplant in the follow-up period had reduction in trabecular thickness (p = 0.05), whereas those who continued on dialysis had reduction in cortical thickness (p = 0.04) and mechanical bone strength on FEA (p = 0.03). CONCLUSION: Patients with severe SHPT requiring parathyroidectomy have persistent changes in bone microarchitecture at least 12 months following surgery with evidence of ongoing decline in trabecular and cortical thickness.