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

医学 放射科 体素 接收机工作特性 磁共振成像 计算机断层血管造影 核医学 霍恩斯菲尔德秤 切断 计算机断层摄影术 内科学 量子力学 物理
作者
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
出处
期刊:Academic Radiology [Elsevier BV]
卷期号:30 (11): 2505-2513 被引量:3
标识
DOI:10.1016/j.acra.2023.01.023
摘要

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).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.
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