Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth
Deep learning-based noise reduction for coronary CT angiography: using four-dimensional noise-reduction images as the ground truth
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DOI：
10.1177/02841851221141656
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发表时间：
2022-12-07
期刊：
影响因子：
1.3
通讯作者：
Ishida, Takayuki
中科院分区：
文献类型：
--
作者：
Kobayashi, Takuma;Nishii, Tatsuya;Ishida, Takayuki
为了评估低对比度区域，如斑块和冠状动脉狭窄，冠状动脉计算机断层扫描血管造影（CCTA）需要提供低噪声图像，而不增加辐射剂量。目的开发一种基于深度学习的CCTA降噪方法，使用四维降噪（4DNR）作为监督学习的基础事实。材料与方法我们回顾性收集了100例回顾性ECG门控CTA。我们使用非刚性配准和加权平均三个时间轴CCTA体积数据（在舒张中期间隔为50 ms）创建4DNR图像。该方法以原始重建图像为输入，4DNR图像为目标图像，通过残差学习得到降噪后的图像。我们根据主动脉的图像噪声和冠状动脉的对比噪声比（CNR）评估了原始图像和基于深度学习的降噪（DLNR）图像的客观图像质量。此外，委员会认证的放射科医生使用5点Likert量表主观评价了几个心脏结构的模糊，并独立分配了冠状动脉疾病报告和数据系统（CAD-RADS）类别。结果DLNR CTA图像噪声降低64.5%（P < 0.001），冠状动脉CNR提高2.9倍，主观比较无明显模糊（P > 0.1）。DLNR图像中CAD-RADS与原始CCT的观察者内一致性极佳（0.87，95%置信区间= 0.77-0.99）。结论4DNR指导下的DLNR方法在不影响冠状动脉狭窄评估的前提下，显著降低了CTA图像的噪声。
Background To assess low-contrast areas such as plaque and coronary artery stenosis, coronary computed tomography angiography (CCTA) needs to provide images with lower noise without increasing radiation doses. Purpose To develop a deep learning-based noise-reduction method for CCTA using four-dimensional noise reduction (4DNR) as the ground truth for supervised learning. Material and Methods \We retrospectively collected 100 retrospective ECG-gated CCTAs. We created 4DNR images using non-rigid registration and weighted averaging three timeline CCTA volumetric data with intervals of 50 ms in the mid-diastolic phase. Our method set the original reconstructed image as the input and the 4DNR as the target image and obtained the noise-reduced image via residual learning. We evaluated the objective image quality of the original and deep learning-based noise-reduction (DLNR) images based on the image noise of the aorta and the contrast-to-noise ratio (CNR) of the coronary arteries. Further, a board-certified radiologist evaluated the blurring of several heart structures using a 5-point Likert scale subjectively and assigned a coronary artery disease reporting and data system (CAD-RADS) category independently. Results DLNR CCTAs showed 64.5% lower image noise (P < 0.001) and achieved a 2.9 times higher CNR of coronary arteries than that in original images, without significant blurring in subjective comparison (P > 0.1). The intra-observer agreement of CAD-RADS in the DLNR image was excellent (0.87, 95% confidence interval = 0.77-0.99) with original CCTAs. Conclusion Our DLNR method supervised by 4DNR significantly reduced the image noise of CCTAs without affecting the assessment of coronary stenosis.