Rotational Augmented Noise2Inverse for Low-Dose Computed Tomography Reconstruction

IF 4.6 Q1 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING IEEE Transactions on Radiation and Plasma Medical Sciences Pub Date : 2023-12-08 DOI:10.1109/TRPMS.2023.3340955
Hang Xu;Alessandro Perelli
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Abstract

In this work, we present a novel self-supervised method for low-dose computed tomography (LDCT) reconstruction. Reducing the radiation dose to patients during a computed tomography (CT) scan is a crucial challenge since the quality of the reconstruction highly degrades because of low photons or limited measurements. Supervised deep learning DL methods have shown the ability to remove noise in images but require accurate ground truth which can be obtained only by performing additional high-radiation CT scans. Therefore, we propose a novel self-supervised framework for LDCT, in which ground truth is not required for training the convolutional neural network (CNN). Based on the noise2inverse (N2I) method, we enforce in the training loss the equivariant property of rotation transformation, which is induced by the CT imaging system, to improve the quality of the CT image in a lower dose. Numerical and experimental results show that the reconstruction accuracy of N2I with sparse views is degrading while the proposed rotational augmented noise2inverse (RAN2I) method keeps better-image quality over a different range of sampling angles. Finally, the quantitative results demonstrate that RAN2I achieves higher-image quality compared to N2I, and experimental results of RAN2I on real projection data show comparable performance to supervised learning.
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用于低剂量计算机断层扫描重建的旋转增强噪声 2 逆
在这项研究中,我们提出了一种用于低剂量计算机断层扫描(LDCT)重建的新型自监督方法。在 CT 扫描过程中,降低患者的辐射剂量是一项重大挑战,因为低光子或有限的测量会导致重建质量严重下降。有监督的深度学习方法已显示出去除图像中噪声的能力,但需要精确的地面实况,而这只能通过执行额外的高辐射 CT 扫描来获得。因此,我们提出了一种用于 LDCT 的新型自监督框架,在该框架中,训练卷积神经网络(CNN)时不需要地面实况。基于 Noise2Inverse(N2I)方法,我们在训练损耗中强制执行由 CT 成像系统引起的旋转变换的等变性质,从而在较低剂量下提高 CT 图像的质量。数值和实验结果表明,稀疏视图下的 N2I 重建精度正在下降,而所提出的旋转增强噪声反转(RAN2I)方法在不同的采样角度范围内都能保持较好的图像质量。最后,定量结果表明,与 N2I 相比,RAN2I 可获得更高的图像质量,而且 RAN2I 在真实投影数据上的实验结果表明其性能与监督学习相当。
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来源期刊
IEEE Transactions on Radiation and Plasma Medical Sciences
IEEE Transactions on Radiation and Plasma Medical Sciences RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING-
CiteScore
8.00
自引率
18.20%
发文量
109
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Affiliate Plan of the IEEE Nuclear and Plasma Sciences Society Table of Contents Introducing IEEE Collabratec IEEE Transactions on Radiation and Plasma Medical Sciences Publication Information Member Get-a-Member (MGM) Program
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