Learnable PM diffusion coefficients and reformative coordinate attention network for low dose CT denoising

Various deep learning methods have recently been used for low dose CT (LDCT) denoising. Aggressive denoising may destroy the edge and fine anatomical structures of CT images. Therefore a key issue in LDCT denoising tasks is the difficulty of balancing noise/artifact suppression and edge/structure pr...

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Vydáno v:Physics in medicine & biology Ročník 68; číslo 24
Hlavní autoři: Zhang, Haowen, Zhang, Pengcheng, Cheng, Weiting, Li, Shu, Yan, Rongbiao, Hou, Ruifeng, Gui, Zhiguo, Liu, Yi, Chen, Yang
Médium: Journal Article
Jazyk:angličtina
Vydáno: England 21.12.2023
Témata:
Anisotropy
Image Processing, Computer-Assisted
Neural Networks, Computer
Signal-To-Noise Ratio
Tomography, X-Ray Computed
encoder–decoder structure
LDCT
attention mechanism
perceptual loss
Perona–Malik model
ISSN:1361-6560, 1361-6560
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Shrnutí:Various deep learning methods have recently been used for low dose CT (LDCT) denoising. Aggressive denoising may destroy the edge and fine anatomical structures of CT images. Therefore a key issue in LDCT denoising tasks is the difficulty of balancing noise/artifact suppression and edge/structure preservation. We proposed an LDCT denoising network based on the encoder-decoder structure, namely the Learnable PM diffusion coefficient and efficient attention network (PMA-Net). First, using the powerful feature modeling capability of partial differential equations, we constructed a multiple learnable edge module to generate precise edge information, incorporating the anisotropic image processing idea of Perona-Malik (PM) model into the neural network. Second, a multiscale reformative coordinate attention module was designed to extract multiscale information. Non-overlapping dilated convolution capturing abundant contextual content was combined with coordinate attention which could embed the spatial location information of important features into the channel attention map. Finally, we imposed additional constraints on the edge information using edge-enhanced multiscale perceptual loss to avoid structure loss and over-smoothing. Experiments are conducted on simulated and real datasets. The quantitative and qualitative results show that the proposed method has better performance in suppressing noise/artifacts and preserving edges/structures. This work proposes a novel edge feature extraction method that unfolds partial differential equation into neural networks, which contributes to the interpretability and clinical application value of neural network.
Bibliografie:ObjectType-Article-1
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content type line 23
ISSN:1361-6560
1361-6560
DOI:10.1088/1361-6560/aced33