Deep Learning-Based Versus Threshold-Based Bone Removal for CT Angiography of Head and Neck Arteriovenous Fistulas: A Retrospective Comparison of Image Quality, Key-Structure Display, and Post-Processing Time

Background/Objectives: Bone overlap at the skull base limits CT angiography (CTA) in head and neck arteriovenous fistulas (hAVFs), and threshold-based bone removal fails where bone and contrast attenuation overlap. Whether deep learning-based removal improves the display of the structures that determine endovascular planning is unknown in a DSA-confirmed cohort. We therefore compared the two methods within the same CTA datasets.