Deep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT.

Abstract

Purpose To develop and evaluate a Deep learnIng-bAsed MONoenergetic imaging at Different energies (DIAMOND) framework for generating virtual monoenergetic images (VMIs) from conventional energy-integrating detector (EID) CT, aiming to reduce blooming artifacts and improve stenosis assessment in coronary CT angiography (CCTA) with heavily calcified plaques. Materials and Methods This study (August 2022-September 2023) used a combination of retrospective and prospective imaging data. DIAMOND was trained using a simplified U-Net architecture on a retrospective dataset of 10 CCTA examinations performed with ultrahigh-resolution (UHR) photon-counting detector (PCD) CT; 70-keV PCD VMIs (energy equivalent to 120-kV single-energy EID CT) served as inputs and 100-keV PCD VMIs as targets. The trained model was then applied to a prospective dataset of participants with heavily calcified plaques who underwent EID CT at 120 kV followed by same-day PCD CT. Percent diameter stenosis (PDS) was quantified for a phantom and participants by using commercial software and compared across EID CT, DIAMOND, and PCD CT using Bland-Altman analysis. Changes in stenosis severity categorization based on PDS were evaluated. Results DIAMOND reduced blooming artifacts and improved lumen visualization, with image quality resembling PCD CT. In 23 participants (mean age, 69 years ± 8 [SD]; 18 male), average PDS decreased from 35.65% (EID CT) to 25.19% (DIAMOND, P < .05), approaching 24.27% with UHR PCD CT (P < .05). Relative to EID CT, DIAMOND led to Coronary Artery Disease Reporting and Data System reclassification in 11 of 26 (42%) lesions, mainly in mild to moderate stenosis ranges. Processing time was approximately 0.21 second per axial section on a standard graphics processing unit. Conclusion This study demonstrated the feasibility of using DIAMOND to generate high-kiloelectron volt VMIs from single-energy EID CT, providing artifact-reduced coronary imaging and improved stenosis quantification for heavily calcified plaques comparable to PCD CT without hardware upgrades. Keywords: Coronary CT Angiography, Coronary Artery Stenosis, Energy-Integrating Detector CT, Photon-Counting Detector CT, Deep Learning, CT-Photon Counting, Angiography, Coronary Arteries Supplemental material is available for this article. © RSNA, 2026.