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

Tatsuya Nishii; Takuma Kobayashi; Tatsuya Saito; Akiyuki Kotoku; Yasutoshi Ohta; Satoshi Kitahara; Kensuke Umehara; Junko Ota; Hiroki Horinouchi; Yoshiaki Morita; Teruo Noguchi; Takayuki Ishida; Tetsuya Fukuda

doi:10.1016/j.acra.2023.01.023

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

Acad Radiol. 2023 Nov;30(11):2505-2513. doi: 10.1016/j.acra.2023.01.023. Epub 2023 Mar 2.

Authors

Affiliations

¹ Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan. Electronic address: ttsynishii@gmail.com.
² Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan; Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan.
³ Department of Radiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan.
⁴ Department of Cardiology, National Cerebral and Cardiovascular Center, Suita, Osaka, Japan.
⁵ Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan; Medical Informatics Section, QST Hospital, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan; Applied MRI Research, Department of Molecular Imaging and Theranostics, Institute for Quantum Medical Science, National Institutes for Quantum Science and Technology, Inage-ku, Chiba, Japan.
⁶ Department of Medical Physics and Engineering, Graduate School of Medicine, Osaka University, Suita, Japan.

PMID: 36868878
DOI: 10.1016/j.acra.2023.01.023

Abstract

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.

Results: 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.

Conclusion: DL-based denoised high-fidelity CCTA improved the AUC and specificity of the FAI for predicting HIPs.

Keywords: Atherosclerosis; Computed Tomography Angiography; Computer-Assisted; Coronary Artery Disease; Deep Learning; Image Processing; Inflammation.