Photon-Counting CT Plaque Analysis Consistent With Energy-Integrating Detector CT

Energy-integrating detector (EID) computed tomography (CT) and photon-counting detector CT (PCD-CT) are highly consistent in AI-enabled plaque quantification, stenosis grade, and FFRCT values, according to recent research.¹

What is Photon-Counting Detector CT?

CT has long been clinical routine based on rapid technical progress – the introduction of PCD-CT is the current pinnacle of this innovation. Existing cardiac CT is plagued by limited spatial resolution and a lack of quality spectral data. PCD-CT has the capacity to solve these limitations; however, the actual process of transitioning a clinic from EID-CT to PCD-CT may potentially impact the analysis of these scans.^1,2

This study was presented at the 21st Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography (SCCT) in San Diego, California, by Omar Khalique, MD, director of the division of cardiovascular imaging at Saint Francis Hospital and Catholic Health, director of the cardiovascular imaging research and education at DeMatteis Cardiovascular Institute, and a clinical professor of cardiology at the New York Institute of Technology. Khalique and colleagues attempted to determine whether the transitionary period between EID-CT and PCD-CT impacts artificial intelligence (AI) enabled analyses.¹

“Photon-counting CT allows us to have much higher accuracy, which has been shown by other studies mostly for stenosis, and it has to do with the fact that the resolution is much better, so we can see finer detail.,” Ron Blankstein, MD, the associate director of the cardiovascular imaging program, director of cardiac computed tomography, and co-director of the cardiovascular imaging training program at Brigham and Women’s Hospital, as well as a professor of medicine at Harvard Medical School, told HCPLive in an exclusive interview. “There’s less calcium blooming, we have the ability to look at finer structures, we have the ability to look at lesions that are calcified, and to look at stents, which we often tried to avoid in CT.”

How did the study compare the scanners?

Khalique and colleagues conducted a retrospective, multi-center, longitudinal study across 6 clinical sites which were transitioning from EID to PCD-CT between 2024 and 2025. Another site was ultimately included, but only with data for post PCD-CT. The investigators analyzed scans from a 180-day window before and after the date of the scanner change. HeartFlow’s AI-enabled software was implemented to quantify total plaque volume (TPV), maximum stenosis grade, FFRCT values, non-calcified plaque (NCP), and calcified plaque.¹

A total of 10,412 patients were ultimately included in the analysis. Among these patients, the team saw no statistically significant differences between EID and PCD-CT for TPV, NCP, calcified plaque, max stenosis grade, lowest FFRCT value, and mismatch rates across FFRCT >0.8 and >30% and >50% stenosis. Additionally, per-vessel analysis of lowest FFRCT and max stenosis grade by vessel territory was consistent with these findings, indicating no statistically significant differences in median values across scanner types.¹

Ultimately, the team concluded that all major values in plaque analysis remained consistent across both EID and PCD-CT. The AI algorithm also indicated robust performance in both detectors – based on this, the team posits few issues in clinical continuity during hardware upgrades at a given clinic.¹

“This ushers in a new tool in drug development that, before we do a large, expensive outcomes trial, can let us see if there’s a signal,” Blankstein said. “What happens when a therapy is used in a smaller trial where imaging is the measure of outcome? And so far, there’s been a very good concordance that the changes we see in the plaque are highly associated with what we see in outcomes. This is a test that can revolutionize how we develop drugs.”

Editors’ Note: Blankstein reports disclosures with Amgen, Heartflow, Nanox AI, Novartis, Caristo Diagnostics, Siemens, and others.

References

1. Khalique O, Parikh R, Carr J, et al. Impact of Photon-Counting CT On AI-Enabled Plaque Quantification, Stenosis Grading, and FFRct Values: A Multi-Center Real-World Analysis. Presented at the 21st Annual Scientific Meeting of the Society of Cardiovascular Computed Tomography (SCCT), San Diego, CA. July 9-12, 2026.

2. Flohr T, Schmidt B, Ulzheimer S, Alkadhi H. Cardiac imaging with photon counting CT. Br J Radiol. 2023 Dec;96(1152):20230407. doi: 10.1259/bjr.20230407. Epub 2023 Oct 24. PMID: 37750856; PMCID: PMC10646663. https://pubmed.ncbi.nlm.nih.gov/37750856/