Author profile
University of Kentucky
Deep learning-based image quality assessment (DL-IQA) models are commonly trained using radiologist ratings and may provide a more objective approach to CT image quality evaluation. However, clinical deployment may be limited by institutional variation in sca...
Population-level CT dose monitoring typically relies on summary statistics and benchmark comparisons, which can obscure distributional features such as multimodality and disproportionate upper tails. This preliminary feasibility study evaluated the utility of...
ACR CT Dose Index Registry (DIR) benchmarking is commonly reviewed at discrete reporting intervals, but single-interval interpretation can be limited by benchmark shifts, transient variation, or incomplete reference data. This study evaluates how longitudinal...
To evaluate how patient size, injected FDG dose, and acquisition time relate to routine whole-body PET/CT image quality, and to assess whether a simple dose–time–weight index can guide individualized protocols for more consistent noise performance.