Original Investigation|Articles in Press

Deep Learning-based Post Hoc CT Denoising for the Coronary Perivascular Fat Attenuation Index

Published:March 02, 2023DOI:

      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.

      Key Words


      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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        • Antonopoulos AS
        • Sanna F
        • Sabharwal N
        • et al.
        Detecting human coronary inflammation by imaging perivascular fat.
        Sci Transl Med. 2017; 9: eaal2658
        • Kwiecinski J
        • Dey D
        • Cadet S
        • et al.
        Peri-coronary adipose tissue density is associated with 18f-sodium fluoride coronary uptake in stable patients with high-risk plaques.
        JACC Cardiovasc Imaging. 2019; 12: 2000-2010
        • Lin A
        • Nerlekar N
        • Yuvaraj J
        • et al.
        Pericoronary adipose tissue computed tomography attenuation distinguishes different stages of coronary artery disease: a cross-sectional study.
        Eur Heart J Cardiovasc Imaging. 2021; 22: 298-306
        • Hedgire S
        • Baliyan V
        • Zucker EJ
        • et al.
        Perivascular epicardial fat stranding at coronary CT angiography: a marker of acute plaque rupture and spontaneous coronary artery dissection.
        Radiology. 2018; 287: 808-815
        • Goeller M
        • Achenbach S
        • Cadet S
        • et al.
        Pericoronary adipose tissue computed tomography attenuation and high-risk plaque characteristics in acute coronary syndrome compared with stable coronary artery disease.
        JAMA Cardiol. 2018; 3: 858-863
        • Oikonomou EK
        • Marwan M
        • Desai MY
        • et al.
        Non-invasive detection of coronary inflammation using computed tomography and prediction of residual cardiovascular risk (the CRISP CT study): a post-hoc analysis of prospective outcome data.
        Lancet. 2018; 392: 929-939
        • Oikonomou EK
        • Desai MY
        • Marwan M
        • et al.
        Perivascular fat attenuation index stratifies cardiac risk associated with high-risk plaques in the CRISP-CT study.
        J Am Coll Cardiol. 2020; 76: 755-757
        • Antoniades C
        • Kotanidis CP
        • Berman DS.
        State-of-the-art review article. Atherosclerosis affecting fat: What can we learn by imaging perivascular adipose tissue?.
        J Cardiovasc Comput Tomogr. 2019; 13: 288-296
        • Shaw LJ
        • Blankstein R
        • Bax JJ
        • et al.
        Society of cardiovascular computed tomography /North American Society of Cardiovascular Imaging – expert consensus document on coronary CT imaging of atherosclerotic plaque.
        J Cardiovasc Comput Tomogr. 2021; 15: 93-109
        • van Diemen PA
        • Bom MJ
        • Driessen RS
        • et al.
        Prognostic value of RCA pericoronary adipose tissue CT-attenuation beyond high-risk plaques, plaque volume, and ischemia.
        JACC Cardiovasc Imaging. 2021; 14: 1598-1610
        • Tzolos E
        • Williams MC
        • McElhinney P
        • et al.
        Pericoronary adipose tissue attenuation, low-attenuation plaque burden, and 5-year risk of myocardial infarction.
        JACC Cardiovasc Imaging. 2022; 15: 1078-1088
        • Lell MM
        • Wildberger JE
        • Alkadhi H
        • Damilakis J
        • Kachelriess M
        Evolution in computed tomography.
        Invest Radiol. 2015; 50: 629-644
      1. Deseive S, Chen MY, Korosoglou G, et al. Prospective randomized trial on radiation dose estimates of CT angiography applying iterative image reconstruction the PROTECTION V study.

      2. JACC Cardiovasc Imaging. 2015; 8: 888-896
        • Klüner LV
        • Oikonomou EK
        • Antoniades C.
        Assessing cardiovascular risk by using the fat attenuation index in coronary CT angiography.
        Radiol Cardiothorac Imaging. 2021; 3e200563
        • Renker M
        • Ramachandra A
        • Schoepf UJ
        • et al.
        Iterative image reconstruction techniques: applications for cardiac CT.
        J Cardiovasc Comput Tomogr. 2011; 5: 225-230
        • Tatsugami F
        • Higaki T
        • Nakamura Y
        • et al.
        Deep learning-based image restoration algorithm for coronary CT angiography.
        Eur Radiol. 2019; 29: 5322-5329
        • Benz DC
        • Benetos G
        • Rampidis G
        • et al.
        Validation of deep-learning image reconstruction for coronary computed tomography angiography: Impact on noise, image quality and diagnostic accuracy.
        J Cardiovasc Comput Tomogr. 2020; 14: 444-451
        • Nishii T
        • Kobayashi T
        • Tanaka H
        • et al.
        Deep learning-based post hoc CT denoising for myocardial delayed enhancement.
        Radiology. 2022; 305: 82-91
        • Noguchi T
        • Kawasaki T
        • Tanaka A
        • et al.
        High-intensity signals in coronary plaques on noncontrast T1-weighted magnetic resonance imaging as a novel determinant of coronary events.
        J Am Coll Cardiol. 2014; 63: 989-999
        • Uzu K
        • Kawakami R
        • Sawada T
        • et al.
        Histopathological characterization of high-intensity signals in coronary plaques on noncontrast T1-weighted magnetic resonance imaging.
        JACC Cardiovasc Imaging. 2021; 14: 518-519
        • Sato S
        • Matsumoto H
        • Li D
        • et al.
        Coronary high-intensity plaques at T1-weighted MRI in stable coronary artery disease: comparison with near-infrared spectroscopy intravascular US.
        Radiology. 2022; 302: 557-565
        • Ma R
        • Ties D
        • van Assen M
        • et al.
        Towards reference values of pericoronary adipose tissue attenuation: impact of coronary artery and tube voltage in coronary computed tomography angiography.
        Eur Radiol. European Radiology. 2020; 30: 6838-6846
        • Abbara S
        • Blanke P
        • Maroules CD
        • et al.
        SCCT guidelines for the performance and acquisition of coronary computed tomographic angiography: a report of the society of cardiovascular computed tomography guidelines committee: Endorsed by the North American society for cardiovascular imaging (NASCI).
        J Cardiovasc Comput Tomogr. 2016; 10: 435-449
        • Kobayashi T
        • Nishii T
        • Umehara K
        • et al.
        Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth.
        Acta radiol. 2022; 028418512211416
        • Zhang Y
        • Tian Y
        • Kong Y
        • Zhong B
        • Fu Y.
        Residual Dense Network for Image Restoration.
        IEEE Trans Pattern Anal Mach Intell. 2021; 43: 2480-2495
        • Nishii T
        • Kono AK
        • Tani W
        • et al.
        Four-dimensional noise reduction using the time series of medical computed tomography datasets with short interval times: a static-phantom study.
        PeerJ. 2016; 4: e1680
        • Cury RC
        • Blankstein R
        • Leipsic J
        • et al.
        CAD-RADSTM 2.0 - 2022 coronary artery disease - reporting and data system an expert consensus document of the society of cardiovascular computed tomography (SCCT), the American college of cardiology (ACC), the American college of radiology (ACR) and the North America Society of Cardiovascular Imaging (NASCI).
        J Cardiovasc Comput Tomogr. 2022; 16: 536-557
        • Kuroiwa Y
        • Uchida A
        • Yamashita A
        • et al.
        Coronary high-signal-intensity plaques on T 1 -weighted magnetic resonance imaging reflect intraplaque hemorrhage.
        Cardiovasc Pathol. 2019; 40: 24-31
        • Vickers AJ
        • van Calster B
        • Steyerberg EW.
        A simple, step-by-step guide to interpreting decision curve analysis.
        Diagn Progn Res. 2019; 3: 18
        • Yu M
        • Meng Y
        • Zhang H
        • et al.
        Associations between pericarotid fat density and image-based risk characteristics of carotid plaque.
        Eur J Radiol. 2022; 153110364
        • Zhang S
        • Gu H
        • Yu X
        • Kang B
        • Yuan X
        • Wang X.
        Association between carotid artery perivascular fat density and intraplaque hemorrhage.
        Front Cardiovasc Med. 2021; 8735794
        • Hyafil F
        • Schindler A
        • Sepp D
        • et al.
        High-risk plaque features can be detected in non-stenotic carotid plaques of patients with ischaemic stroke classified as cryptogenic using combined (18)F-FDG PET/MR imaging.
        Eur J Nucl Med Mol Imaging. 2016; 43: 270-279
        • Ma YJ
        • Ren Y
        • Feng P
        • He P
        • Guo XD
        • Wei B.
        Sinogram denoising via attention residual dense convolutional neural network for low-dose computed tomography.
        Nuclear Sci Techniques. 2021; 32: 1-14
        • Zhang X
        • Han Z
        • Shangguan H
        • Han X
        • Cui X
        • Wang A.
        Artifact and detail attention generative adversarial networks for low-dose CT denoising.
        IEEE Trans Med Imaging. 2021; 40: 3901-3918
      3. Japan Network for Research and Information on Medical Exposures: J-RIME.
        National Diagnostic Reference Levels in Japan, 2020 (Available at) (Accessed December 27, 2022)
        • Oikonomou EK
        • Williams MC
        • Kotanidis CP
        • et al.
        A novel machine learning-derived radiotranscriptomic signature of perivascular fat improves cardiac risk prediction using coronary CT angiography.
        Eur Heart J. 2019; 40: 3529-3543
        • Jonas RA
        • Weerakoon S
        • Fisher R
        • et al.
        Interobserver variability among expert readers quantifying plaque volume and plaque characteristics on coronary CT angiography: a CLARIFY trial sub-study.
        Clin Imaging. 2022; 91: 19-25
        • Nishii T
        • Okuyama S
        • Horinouchi H
        • et al.
        A real-world clinical implementation of automated processing using intelligent work aid for rapid reformation at the orbitomeatal line in head computed tomography.
        Invest Radiol. 2021; 56: 599-604