Dilated Convolution ResNet with Boosting Attention Modules and Combined Loss Functions for LDCT Image Denoising

With the increasing concern regarding the radiation exposure of patients undergoing computed tomography (CT) scans, researchers have been using deep learning techniques to improve the quality of denoised low-dose CT (LDCT) images. In this paper, a cascaded dilated residual network (ResNet) with integrated attention modules, specifically spatial- and channel- attention modules, is proposed. This experiment demonstrated how these attention modules improved the denoised CT image by testing a simple ResNet with and without the modules. Further, an investigation regarding the effectiveness of per-pixel loss, perceptual loss via VGG16-Net, and structural dissimilarity loss functions is also covered through an ablation experiment. By knowing how these loss functions affect the output denoised images, a combination of the these loss function is then proposed which aims to prevent edge over-smoothing, enhance textural details and finally, preserve structural details on the denoised images. Finally, a bench testing was also done by comparing the visual and quantitative results of the proposed model with the state-of-the-art models such as block matching 3D (BM3D), patch-GAN and dilated convolution with edge detection layer (DRL-E-MP) for accuracy.

[1]  Javad Alirezaie,et al.  Low Dose CT Image Denoising Using Boosting Attention Fusion GAN with Perceptual Loss , 2021, 2021 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC).

[2]  R. Reiazi,et al.  Lifetime attributable cancer risk related to prevalent CT scan procedures in pediatric medical imaging centers , 2021, International journal of radiation biology.

[3]  Aznul Qalid Md Sabri,et al.  A review on Deep Learning approaches for low-dose Computed Tomography restoration , 2021, Complex & Intelligent Systems.

[4]  Dianlin Hu,et al.  Low-Dose CT Imaging via Cascaded ResUnet with Spectrum Loss. , 2021, Methods.

[5]  Prabir Kumar Biswas,et al.  Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising , 2020, IEEE Transactions on Medical Imaging.

[6]  Ian Goodfellow,et al.  Generative adversarial networks , 2020, Commun. ACM.

[7]  Min Guo,et al.  Boosting attention fusion generative adversarial network for image denoising , 2020, Neural Computing and Applications.

[8]  Alessandro Foi,et al.  Collaborative Filtering of Correlated Noise: Exact Transform-Domain Variance for Improved Shrinkage and Patch Matching , 2020, IEEE Transactions on Image Processing.

[9]  Sepehr Ataei,et al.  Cascaded Convolutional Neural Networks with Perceptual Loss for Low Dose CT Denoising , 2020, 2020 International Joint Conference on Neural Networks (IJCNN).

[10]  Javad Alirezaie,et al.  Deep Learning for Low-Dose CT Denoising Using Perceptual Loss and Edge Detection Layer , 2019, Journal of Digital Imaging.

[11]  Paul Babyn,et al.  Sharpness-Aware Low-Dose CT Denoising Using Conditional Generative Adversarial Network , 2017, Journal of Digital Imaging.

[12]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[13]  Alessandro Foi,et al.  Image Denoising by Sparse 3-D Transform-Domain Collaborative Filtering , 2007, IEEE Transactions on Image Processing.

[14]  Yuan Xu,et al.  Image Restoration for Low-Dose CT via Transfer Learning and Residual Network , 2020, IEEE Access.