[1]陈红池,翟莹,李秋霞,等.一种基于MDC-DCRN模型的低剂量CT图像去噪方法[J].中国医学物理学杂志,2025,42(9):1136-1146.[doi:DOI:10.3969/j.issn.1005-202X.2025.09.003]
　CHEN Hongchi,,et al.A denoising method for low-dose CT images based on MDC-DCRN model[J].Chinese Journal of Medical Physics,2025,42(9):1136-1146.[doi:DOI:10.3969/j.issn.1005-202X.2025.09.003]
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一种基于MDC-DCRN模型的低剂量CT图像去噪方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第9期
页码:
1136-1146
栏目:
医学影像物理
出版日期:
2025-09-30

文章信息/Info

Title:
A denoising method for low-dose CT images based on MDC-DCRN model
文章编号:
R318；TP391.41
作者:
陈红池1; 2; 3; 翟莹1; 2; 3; 李秋霞1; 2; 3; 李坊佐1; 2; 3
1.赣南医科大学医学信息工程学院， 江西 赣州 341000； 2.赣南医科大学心脑血管疾病防治教育部重点实验室， 江西 赣州341000； 3.组织工程江西省重点实验室（2024SSY06291）， 江西 赣州 341000
Author(s):
CHEN Hongchi1;  2;  3;  ZHAI Ying1;  2;  3;  LI Qiuxia1;  2;  3;  LI Fangzuo1;  2;  3
1. School of Medical and Information Engineering, Gannan Medical University, Ganzhou 341000, China 2.Key Laboratory of Prevention and Treatment of Cardiovascular and Cerebrovascular Diseases, Ministry of Education, Gannan Medical University, Ganzhou 341000, China 3. Jiangxi Provincial Key Laboratory of Tissue Engineering (2024SSY06291), Ganzhou 341000, China
关键词:
低剂量CT; 深度学习; 感知损失; 图像去噪; 伪影消除
Keywords:
Keywords: low-dose CT deep learning perceptual loss image denoising artifact reduction
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2025.09.003
文献标志码:
A
摘要:
目的：低剂量CT（LDCT）图像因存在大量噪声和伪影而严重影响临床诊断，为解决图像过度平滑、纹理细节丢失以及噪声伪影残留等问题，提出一种基于多尺度空洞卷积的反卷积-卷积残差网络（MDC-DCRN）用于LDCT重建图像去噪。方法：该网络采用反卷积-卷积的架构设计，以更好地保留图像细节，并引入多尺度空洞卷积模块增强对不同尺度特征的提取能力。此外，通过结合L1损失和感知损失的复合损失函数，有效缓解图像过度平滑问题。结果：在Mayo数据集上的实验结果表明，MDC-DCRN网络优于RED-CNN、EDCNN、WGAN-RAM和CTformer 4种经典去噪网络，能够有效去除噪声和伪影，恢复更多纹理细节信息。与LDCT图像相比，MDC-DCRN处理后图像的PSNR平均提高13.64%，SSIM平均提高4.57%，RMSE平均降低37.40%。结论：MDC-DCRN模型能够降低低剂量扫描产生的噪声，并有效地保留细节，为临床LDCT图像去噪提供一种新方法。
Abstract:
Abstract: Objective Low-dose CT (LDCT) images significantly affect clinical diagnosis due to the substantial presence of noise and artifacts. To address the challenges such as over-smoothing of images, loss of texture details, and the presence of residual noise artifacts, a deconvolution-convolution residual network integrating multi-scale dilated convolution (MDC-DCRN) is proposed for LDCT denoising. Methods The network employed deconvolution-convolution architecture to better preserve image details and integrated an MDC module to enhance the feature extraction capabilities at different scales. Moreover, the issue of excessive image smoothing was effectively mitigated by the composite loss function combining L1 loss and perceptual loss. Results The experimental results on the Mayo dataset demonstrated that MDC-DCRN outperformed 4 classic denoising methods, namely RED-CNN, EDCNN, WGAN-RAM, and CTformer. MDC-DCRN effectively eliminated noise and artifacts while recovering more texture detail information. Compared with LDCT images, the images processed by MDC-DCRN had an average increase of 13.64% in peak signal-to-noise ratio, an average increase of 4.57% in structural similarity index, and an average decrease of 37.40% in root mean square error. Conclusion The proposed MDC-DCRN model can effectively preserve details while reducing noise from low-dose scanning, offering a novel approach to clinical LDCT image denoising.

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备注/Memo

备注/Memo:
【收稿日期】2025-04-03 【基金项目】国家自然科学基金（11865003）；江西省自然科学基金（20224BAB201020）；赣南医科大学科研启动基金（QD201805）；研究生创新专项基金（YC2024-S838） 【作者简介】陈红池，硕士研究生，研究方向：基于深度学习的低剂量CT重建和图像处理，E-mail: chenhongchi1@163.com 【通信作者】李坊佐，博士，副教授，硕士生导师，研究方向：基于深度学习的CT重建和医学图像分割、分类与检测，E-mail: lfz880920@163.com
更新日期/Last Update: 2025-09-30