This technical report presents a novel CBCT-Based Grading System for Oropharyngeal Airway Narrowing, designed to provide clinicians with a standardised, objective method to assess oropharyngeal airway narrowing using Cone-Beam Computed Tomography (CBCT). The grading system is developed based on the least surface area on axial section measurements/minimal cross-sectional area (MCA) on CBCT. It classifies oropharyngeal narrowing into five distinct grades (Grade 0 to 4). Each grade also has subcategories that correspond to specific anatomical regions-distal to the soft palate (P), distal to the base of the tongue (T), or distal to both the soft palate and the tongue (B)-and includes precise surface area ranges, contributing to better understanding. Traditional methods have commonly relied upon lateral cephalometry or supine CT; however, CBCT offers 3D mapping in a natural upright position, ensuring functional relevance of the airway assessment. Owing to its high spatial resolution, adequate contrast between the soft tissue and empty space, relatively low radiation dose compared to multidetector row CT and visibility of the upper airway by utilising a large field of view (FOV) protocol, CBCT a useful diagnostic tool for evaluation of the airway. The fact that CBCT is taken in a sitting or standing position, where the head is in equilibrium and orofacial and neck musculature is in voluntary control, vis-à-vis the supine position, where this control is taken over by the autonomic nervous system, and the distal part of the soft palate compresses the already narrowed airway further adds to its usefulness. CBCT imaging, with its three-dimensional mapping capabilities, allows for precise visualisation of the airway from the level of the posterior nasal spine, where the hard palate ends, extending to the epiglottis-thus measuring the oropharyngeal airway. The system is particularly useful for early detection and evaluation of conditions such as obstructive sleep apnoea, hypertrophy of the nasopharyngeal tonsils (adenoids), predicting difficult airways for ease of intubation, guiding orthognathic surgical interventions, craniofacial anomalies, and complex orthognathic surgical planning. It holds promise for integration into AI-enabled diagnostic platforms and digital imaging software, offering consistency in research and practice. This report details the rationale, grading criteria, anatomical references, and potential applications of this classification. The system offers a streamlined approach for identifying airway compromise, ultimately aiding multidisciplinary use in optimising patient outcomes.
Internal mammary lymph node (IMLN) metastases play an important role in breast cancer staging and treatment planning but is often difficult to detect because of their small size and anatomical location. Recent advances in digital time-of-flight (TOF) positron emission tomography (PET)/CT and advanced image reconstruction techniques may improve the visualization of such small lesions. This study aimed to evaluate the performance of advanced reconstruction methods (HYPER Iterative and uAI HYPER DPR) for visualizing IMLN metastases in breast cancer using phantom and clinical data. A modified NEMA image quality phantom and a retrospective cohort of breast cancer patients with IMLN metastases were evaluated using a high-resolution digital TOF PET/CT system (uMI 550). Images were reconstructed using ordered subset expectation maximization (OSEM), HYPER Iterative, and uAI HYPER DPR with different reconstruction parameters, and quantitative metrics and visual scores were assessed. In both phantom and clinical images, smaller RS-values for HYPER Iterative and larger Str-values for uAI HYPER DPR were associated with higher lesion conspicuity and contrast-related metrics, at the expense of increased noise. Images reconstructed with a 256 × 256 matrix showed lower background variability than those reconstructed with a 512 × 512 matrix. In the clinical study, these reconstruction settings resulted in higher SUVmax and tumor-to-background ratios for IMLN metastases, and visual scores for diagnostic confidence were higher for HYPER Iterative (RS = 0.7–0.91) and uAI HYPER DPR (Str = 2–4) than for OSEM.
