The Power of Ultra-High-Resolution CT & Deep Learning in Modern Medical Imaging

Nearly 100 million medical computed tomography (CT) exams are performed every year in the United States, underscoring the crucial role CT plays in both diagnostic imaging and treatment planning. The primary driver behind this substantial clinical demand for CT stems from its ability to generate highly detailed images of patient anatomy in a matter of seconds or less. Recent advancements in technology have introduced commercially available CT systems that can resolve objects as small as 100-200 microns – providing much needed boosts in spatial resolution that may further improve imaging of the heart, lungs, bones, and vascularized tissue in general. Ongoing research and development efforts aim to further refine CT systems, pushing the boundaries of spatial resolution and reducing necessary radiation exposure to patients by implementation of deep-learning reconstruct techniques.



This talk will discuss 1) the fundamental physics of ultra-high-resolution CT “UHRCT”, 2) clinical and emerging applications of UHRCT, and 3) applications of deep learning reconstruction techniques for modern CT systems.