人工智能
卷积神经网络
计算机科学
迭代重建
图像质量
水准点(测量)
深度学习
图形
计算机视觉
图像(数学)
GSM演进的增强数据速率
医学影像学
模式识别(心理学)
大地测量学
理论计算机科学
地理
作者
Shalini Ramanathan,Mohan Ramasundaram
标识
DOI:10.1109/apsit58554.2023.10201801
摘要
Image reconstruction performs a protruding role in medical image analysis. Low-Dose CT (LDCT) scan images are a common diagnostic procedure to identify diseases in the human body. Recent scanners follow deep learning-based post-processing methods for low-dose imaging. Low-dose CT image reconstruction techniques deteriorate image quality, which has an impact on a physician's diagnosis. Therefore, this paper introduces a novel LDCT image reconstruction method based on the edge attention technique utilized in graph convolutional neural networks. The quality of the outcomes is measured through the perceptual loss function. Experimental assessments are shown on the LoDoPaB-CT benchmark dataset. It is demonstrated that the proposed method produced an improved high-quality image compared to both traditional and deep learning-based reconstruction methods qualitatively and quantitatively.
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