A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network
A Dual-Encoder-Single-Decoder Based Low-Dose CT Denoising Network
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DOI：
10.1109/jbhi.2022.3155788
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发表时间：
2022-07-01
影响因子：
7.7
通讯作者：
Ren, Huiying
中科院分区：
文献类型：
--
作者：
Han, Zefang;Shangguan, Hong;Ren, Huiying
生成对抗网络（GAN）在低剂量CT（LDCT）图像质量改善方面表现出巨大潜力。一般而言，生成器的浅层特征包含更多的边缘、纹理等浅层视觉信息，而生成器的深层特征包含更多的组织结构等深层语义信息。为了提高网络对不同类型信息的分类处理能力，提出了一种双编码单解码结构的GAN。在生成器的结构上，首先在编码器主通道中设计了金字塔非局部注意模块，通过增强具有自相似性的特征来提高特征提取的有效性;其次，提出了另一种具有浅层特征处理模块和深层特征处理模块的编码器，以提高生成器的编码能力;最后，融合主编码器特征、浅层视觉特征和深层语义特征，得到最终的CT图像去噪。由于在生成器中使用特征互补，所生成的图像的质量得到改善。为了提高贝叶斯网络的对抗性训练能力，提出了一种分层分裂的ResNet结构，提高了特征的丰富性，减少了贝叶斯网络中特征的冗余。实验结果表明，与传统的单编码器-单解码器的GAN相比，该方法在图像质量和医学诊断可接受性方面都有更好的表现。代码可在https://github.com/hanzefang/DESDGAN上找到。
Generative adversarial networks (GAN) have shown great potential for image quality improvement in low-dose CT (LDCT). In general, the shallow features of generator include more shallow visual information such as edges and texture, while the deep features of generator contain more deep semantic information such as organization structure. To improve the network's ability to categorically deal with different kinds of information, this paper proposes a new type of GAN with dual-encoder- single-decoder structure. In the structure of the generator, firstly, a pyramid non-local attention module in the main encoder channel is designed to improve the feature extraction effectiveness by enhancing the features with self-similarity; Secondly, another encoder with shallow feature processing module and deep feature processing module is proposed to improve the encoding capabilities of the generator; Finally, the final denoised CT image is generated by fusing main encoder's features, shallow visual features, and deep semantic features. The quality of the generated images is improved due to the use of feature complementation in the generator. In order to improve the adversarial training ability of discriminator, a hierarchical-split ResNet structure is proposed, which improves the feature's richness and reduces the feature's redundancy in discriminator. The experimental results show that compared with the traditional single-encoder- single-decoder based GAN, the proposed method performs better in both image quality and medical diagnostic acceptability. Code is available in https://github.com/hanzefang/DESDGAN.