ArticleRadiology. Cardiothoracic imaging2026
Deep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT.
Article in Radiology. Cardiothoracic imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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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,
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