Yao Libing, Wang Jiping, Wu Zhongyi, Du Qiang, Yang Xiaodong, Li Ming, Zheng Jian
School of Biomedical Engineering (Suzhou), Division of Life Sciences and Medicine, University of Science and Technology of China, Hefei, 230026, China.
Medical Imaging Department, Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, 215163, China.
Vis Comput Ind Biomed Art. 2024 Jun 12;7(1):14. doi: 10.1186/s42492-024-00165-8.
Low-dose computed tomography (LDCT) has gained increasing attention owing to its crucial role in reducing radiation exposure in patients. However, LDCT-reconstructed images often suffer from significant noise and artifacts, negatively impacting the radiologists' ability to accurately diagnose. To address this issue, many studies have focused on denoising LDCT images using deep learning (DL) methods. However, these DL-based denoising methods have been hindered by the highly variable feature distribution of LDCT data from different imaging sources, which adversely affects the performance of current denoising models. In this study, we propose a parallel processing model, the multi-encoder deep feature transformation network (MDFTN), which is designed to enhance the performance of LDCT imaging for multisource data. Unlike traditional network structures, which rely on continual learning to process multitask data, the approach can simultaneously handle LDCT images within a unified framework from various imaging sources. The proposed MDFTN consists of multiple encoders and decoders along with a deep feature transformation module (DFTM). During forward propagation in network training, each encoder extracts diverse features from its respective data source in parallel and the DFTM compresses these features into a shared feature space. Subsequently, each decoder performs an inverse operation for multisource loss estimation. Through collaborative training, the proposed MDFTN leverages the complementary advantages of multisource data distribution to enhance its adaptability and generalization. Numerous experiments were conducted on two public datasets and one local dataset, which demonstrated that the proposed network model can simultaneously process multisource data while effectively suppressing noise and preserving fine structures. The source code is available at https://github.com/123456789ey/MDFTN .
低剂量计算机断层扫描（LDCT）因其在减少患者辐射暴露方面的关键作用而受到越来越多的关注。然而，LDCT重建图像往往存在大量噪声和伪影，对放射科医生的准确诊断能力产生负面影响。为了解决这个问题，许多研究都集中在使用深度学习（DL）方法对LDCT图像进行去噪。然而，这些基于DL的去噪方法受到来自不同成像源的LDCT数据特征分布高度可变的阻碍，这对当前去噪模型的性能产生了不利影响。在本研究中，我们提出了一种并行处理模型，即多编码器深度特征变换网络（MDFTN），旨在提高多源数据的LDCT成像性能。与依赖持续学习来处理多任务数据的传统网络结构不同，该方法可以在统一框架内同时处理来自各种成像源的LDCT图像。所提出的MDFTN由多个编码器和解码器以及一个深度特征变换模块（DFTM）组成。在网络训练的前向传播过程中，每个编码器并行地从其各自的数据源提取不同的特征，DFTM将这些特征压缩到一个共享特征空间中。随后，每个解码器对多源损失估计执行逆操作。通过协同训练，所提出的MDFTN利用多源数据分布的互补优势来增强其适应性和泛化能力。在两个公共数据集和一个本地数据集上进行了大量实验，结果表明所提出的网络模型可以同时处理多源数据，同时有效地抑制噪声并保留精细结构。源代码可在https://github.com/123456789ey/MDFTN获取。
