2022,
Physics in Medicine & Biology.
DOI:
10.1088/1361-6560/ac5f70
Training low dose CT denoising network without high quality reference data
Jie Jing, Wenjun Xia, Mingzheng Hou, Hu Chen, Yan Liu, Jiliu Zhou, Yi Zhang
Abstract:
Abstract
Objective. Currently, the field of low-dose CT (LDCT) denoising is dominated by supervised learning based methods, which need perfectly registered pairs of LDCT and its corresponding clean reference image (normal-dose CT). However, training without clean labels is more practically feasible and significant, since it is clinically impossible to acquire a large amount of these paired samples. In this paper, a self-supervised denoising method is proposed for LDCT imaging. Approach. The proposed method does not require any clean images. In addition, the perceptual loss is used to achieve data consistency in feature domain during the denoising process. Attention blocks used in decoding phase can help further improve the image quality. Main results. In the experiments, we validate the effectiveness of our proposed self-supervised framework and compare our method with several state-of-the-art supervised and unsupervised methods. The results show that our proposed model achieves competitive performance in both qualitative and quantitative aspects to other methods. Significance. Our framework can be directly applied to most denoising scenarios without collecting pairs of training data, which is more flexible for real clinical scenario.
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2023-10-30 13:57:00
前进:
#paper https://doi.org/10.1088/1361-6560/ac5f70
Training low dose CT denoising network without high quality
reference data
低剂量CT（LDCT）去噪领域主要是基于监督学习的方法，需要完全配准的LDCT对及其相应的干净参考图像（normal-dose CT）。然而，无干净标签的训练更具有实际意义，因为在临床上不可能获得大量的这些配对样本。本文提出了一种用于LDCT成像的自监督去噪方法。方法该方法不需要任何干净的图像。此外，在去噪过程中，利用感知损失来实现特征域的数据一致性。在解码阶段使用的注意块可以帮助进一步提高图像质量。在实验中横向对比了3种方法，并进行了6个消融实验，验证了提出的自监督框架的有效性，以及自注意模块和感知损失的有效性。