Rationale and Objectives
Coronary inflammation related to high-risk hemorrhagic plaques can be captured by the perivascular fat attenuation index (FAI) using coronary computed tomography angiography (CCTA). Since the FAI is susceptible to image noise, we believe deep learning (DL)-based post hoc noise reduction can improve diagnostic capability. We aimed to assess the diagnostic performance of the FAI in DL-based denoised high-fidelity CCTA images compared with coronary plaque magnetic resonance imaging (MRI) delivered high-intensity hemorrhagic plaques (HIPs).
Materials and methods
We retrospectively reviewed 43 patients who underwent CCTA and coronary plaque MRI. We generated high-fidelity CCTA images by denoising the standard CCTA images using a residual dense network that supervised the denoising task by averaging three cardiac phases with nonrigid registration. We measured the FAIs as the mean CT value of all voxels (range of –190 to –30 HU) located within a radial distance from the outer proximal right coronary artery wall. The diagnostic reference standard was defined as HIPs (high-risk hemorrhagic plaques) using MRI. The diagnostic performance of the FAI in the original and denoised images was assessed using receiver operating characteristic curves.
Of 43 patients, 13 had HIPs. The denoised CCTA improved the area under the curve (0.89 [95% confidence interval (CI) 0.78–0.99]) of the FAI compared with that in the original image (0.77 [95% CI, 0.62–0.91], p = 0.008). The optimal cutoff value for predicting HIPs in denoised CCTA was –69 HU with 0.85 (11/13) sensitivity, 0.79 (25/30) specificity, and 0.80 (36/43) accuracy.
DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs.
Abbreviations:AUC (area under the receiver operating characteristic curve), CCTA (coronary CT angiography), FAI (coronary perivascular fat attenuation index), HIP (high-intensity plaque), HRP (high-risk plaque), HU (Hounsfield units), MRI (magnetic resonance imaging), RCA (right coronary artery)
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Published online: March 02, 2023
Accepted: January 17, 2023
Received in revised form: January 6, 2023
Received: December 8, 2022
Publication stageIn Press Corrected Proof
© 2023 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.