DIAMOND demonstrated feasibility of generating high-kiloelectron volt virtual monoenergetic images from single-energy energy-integrating detector CT, providing artifact-reduced coronary imaging and improved stenosis quantification for heavily calcified plaques comparable to photon-counting detector CT without hardware upgrades.
Key Findings
Results
DIAMOND significantly reduced average percent diameter stenosis compared to standard EID CT, approaching values obtained with UHR PCD CT.
In 23 participants (mean age 69 years ± 8 [SD]; 18 male), average percent diameter stenosis (PDS) decreased from 35.65% with EID CT to 25.19% with DIAMOND (P < .05)
DIAMOND values approached 24.27% with UHR PCD CT (P < .05 compared to EID CT)
The difference between DIAMOND and PCD CT values was small (25.19% vs 24.27%)
Participants had heavily calcified plaques and underwent EID CT at 120 kV followed by same-day PCD CT
Results
DIAMOND led to CAD-RADS reclassification in 42% of lesions relative to EID CT.
Reclassification occurred in 11 of 26 (42%) lesions based on Coronary Artery Disease Reporting and Data System (CAD-RADS) criteria
Reclassifications were mainly in the mild to moderate stenosis ranges
Changes in stenosis severity categorization were evaluated based on PDS
Results
DIAMOND reduced blooming artifacts and improved lumen visualization with image quality resembling PCD CT.
DIAMOND was trained using a simplified U-Net architecture
70-keV PCD VMIs served as inputs and 100-keV PCD VMIs as targets during training
The trained model was applied to prospective EID CT data acquired at 120 kV
Image quality was described as resembling PCD CT
Results
DIAMOND processing time was approximately 0.21 second per axial section on a standard graphics processing unit.
Processing time of approximately 0.21 second per axial section was achieved on a standard GPU
This indicates practical clinical feasibility for rapid image generation
Methods
DIAMOND was trained on a small retrospective dataset of PCD CT examinations and validated prospectively.
Training used a retrospective dataset of only 10 CCTA examinations performed with ultrahigh-resolution PCD CT
The study period was August 2022 to September 2023
A combination of retrospective and prospective imaging data was used
Percent diameter stenosis was quantified for a phantom and participants using commercial software and compared across EID CT, DIAMOND, and PCD CT using Bland-Altman analysis
Chang S, Koons E, Gong H, Thorne J, Williamson E, Foley T, et al.. (2026). Deep Learning-based Monoenergetic Imaging for Calcified Coronary Stenosis Assessment at Energy-integrating Detector CT.. Radiology. Cardiothoracic imaging. https://doi.org/10.1148/ryct.250230