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Imaging of musculoskeletal disorders, including arthritis, infection, osteoporosis, sarcopenia, and malignancies, is often limited when using conventional modalities such as radiography, computed tomography (CT), and MR imaging. As a result of recent advances in Positron Emission Tomography (PET) instrumentation, total-body PET/CT offers a longer axial field-of-view, higher geometric sensitivity, and higher spatial resolution compared with standard PET systems. This article discusses the potential applications of total-body PET/CT imaging in the assessment of musculoskeletal disorders.

Purpose: Ultrashort echo time (UTE) MRI can be a radiation‐free alternative to CT for craniofacial imaging of pediatric patients. However, unlike CT, bone‐specific MR imaging is limited by long scan times, relatively low spatial resolution, and a time‐consuming bone segmentation workflow. Methods: A rapid, high‐resolution UTE technique for brain and skull imaging in conjunction with an automatic segmentation pipeline was developed. A dual‐RF, dual‐echo UTE sequence was optimized for rapid scan time (3 min) and smaller voxel size (0.65 mm3). A weighted least‐squares conjugate gradient method for computing the bone‐selective image improves bone specificity while retaining bone sensitivity. Additionally, a deep‐learning U‐Net model was trained to automatically segment the skull from the bone‐selective images. Ten healthy adult volunteers (six male, age 31.5 ± 10 years) and three pediatric patients (two male, ages 12 to 15 years) were scanned at 3 T. Clinical CT for the three patients were obtained for validation. Similarities in 3D skull reconstructions relative to clinical standard CT were evaluated based on the Dice similarity coefficient and Hausdorff distance. Craniometric measurements were used to assess geometric accuracy of the 3D skull renderings. Results: The weighted least‐squares method produces images with enhanced bone specificity, suppression of soft tissue, and separation from air at the sinuses when validated against CT in pediatric patients. Dice similarity coefficient overlap was 0.86 ± 0.05, and the 95th percentile Hausdorff distance was 1.77 ± 0.49 mm between the full‐skull binary masks of the optimized UTE and CT in the testing dataset. Conclusion: An optimized MRI acquisition, reconstruction, and segmentation workflow for craniofacial imaging was developed. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]