Redefining the Future of CT Imaging

Computed tomography (CT) continues to evolve at a rapid pace, with advances in artificial intelligence, detector technology, and system design reshaping both clinical practice and operational strategy. In a recent conversation with Applied Radiology, Kieran Anderson spoke with Prashant Nagpal, MD, Professor and Section Chief of Cardiovascular Imaging at UW Health in Madison, and Peter Noël, PhD, Associate Professor of Radiology and Director of CT Research at the University of Pennsylvania’s Center for Advanced Computer Tomography and Imaging Services, about where CT stands today and where it is heading.

AI Across the CT Workflow

Both experts described artificial intelligence as no longer an adjunct but an embedded component of modern CT imaging. Dr. Noël noted that AI tools are increasingly incorporated into CT workflows and may support efficiency and consistency. AI now touches nearly every step of the CT process, from patient positioning and protocol selection to reconstruction and interpretation. Deep learning reconstruction, motion correction, and automated landmark detection are already enhancing image quality and improving efficiency.¹

Dr. Noël also pointed to the efficiency gains AI can deliver at the reading workstation. AI tools intended to highlight areas of interest automated algorithms that screen for findings such as pulmonary embolism, rib fractures, or aortic dissection help radiologists triage case more quickly. Rather than replacing the radiologist, these tools function as a concurrent reviewer, highlighting areas of concern and helping prioritize attention. “Those incremental improvements add up,” he said, particularly in busy practices facing increasing imaging volumes.

Beyond interpretation, AI is contributing to standardization. Dr. Noël noted that protocol selection, scan planning, and reconstruction choices are increasingly automated, may support more uniform workflows. For both experts, this drive toward consistency is central to the next phase of CT.

Photon Counting: Promise and Practical Questions

Photon counting CT in general has generated significant attention in recent years, and both physicians acknowledged its potential while also recognizing remaining challenges. Dr Noël described photon counting to be particularly valuable in areas such as chest, musculoskeletal, cardiac, and vascular imaging. High-resolution imaging is one of the most visible benefits, but he stressed that this is only part of the story;  hardware and software advances must evolve together.

At the same time, Dr Nagpal highlighted the clinical implications of the possibility of improved resolution in cardiovascular imaging. Coronary arteries are small, often 3 millimeters or less in diameter, and any gain in resolution may aid in detecting subtle atherosclerotic changes. However, he cautioned that improved image detail introduces new considerations for quantification. Universal standardized metrics such as fractional flow reserve and plaque burden are tied to well established thresholds. If photon counting systems alters how those measurements are derived or visualized, clinicians may need to revisit how guidelines are applied.

Both experts agreed that integration into existing health systems presents operational questions. Many institutions operate mixed fleets of CT scanners. Incorporating photon counting systems effectively, without diminishing their unique capabilities, requires thoughtful planning. As Dr. Noël observed, the goal is not to make photon counting images resemble older energy-integrating detector images, but to fully leverage their strengths while maintaining workflow efficiency.

Envisioning the CT of the Future

Both experts expressed excitement regarding consistency and access. Dr. Nagpal envisioned a future in which scan planning, reconstruction, and even portions of reporting are increasingly automated, creating standardized workflows across institutions. He expects that improved efficiency will translate into shorter wait times and expanded access to imaging, particularly for urgent indications.

Dr. Noël agreed, emphasizing that quantitative precision and workflow harmonization will likely define the next phase of CT. “We are moving toward greater consistency,” he said, both in acquisition and interpretation. While fully automated reporting remains unlikely, structured outputs and pre-populated findings may become routine.

For institutions preparing for this transition, both experts advised aligning technology choices with clinical priorities. Dr. Nagpal recommended focusing on the predominant needs of a given practice and deploying advanced systems strategically rather than uniformly. Dr. Noël encouraged radiology leaders to remain forward-looking, noting that adopting newer technologies thoughtfully can position institutions to shape, rather than simply react to, the evolution of CT.

Together, their perspectives suggest that the future of CT will not be defined by a single innovation. Instead, it will emerge from the integration of advanced detectors, AI-driven reconstruction and workflow tools, and new system designs, all aimed at improving precision, efficiency, and patient access to high-quality imaging.

Reference

1. Refers to features optionally available with Canon’s Aquilion ONE Insight edition