Denoising Low-Dose CT Images Using a Multi-Layer Convolutional Analysis-Based Sparse Encoder Network

Imaging in the field of low-dose computed tomography (LDCT) tend to be rather noisy and artificial but is diagnostically useful. One approach to improve the quality of LDCT images is to use deep learning (DL) techniques. DL-based methods produce state-of-the-art performance in low-level medical imag...

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Vydáno v:2022 15th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI) s. 1 - 6
Hlavní autoři: Kang, Yanqin, Liu, Jin, Liu, Tao, Qiang, Jun
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 05.11.2022
Témata:
Computed tomography
Computer aided software engineering
Convolution
Convolutional codes
CT image
denoise
Image edge detection
noise-artifacts
residual network
sparse encoder
Uncertainty
Visualization
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Shrnutí:Imaging in the field of low-dose computed tomography (LDCT) tend to be rather noisy and artificial but is diagnostically useful. One approach to improve the quality of LDCT images is to use deep learning (DL) techniques. DL-based methods produce state-of-the-art performance in low-level medical image restoration tasks but remain defect to interpret due to their black-box constructions. In this paper, we present a simple yet effective LDCT image denoising model by combining the advantages of a residual strategy and a multilayer convolutional analysis-based sparse encoder (CASE). Inspired by convolutional sparse coding (CSC), we constructed a multilayer CASE to sufficiently capture and represent hierarchical image features and designed CASE-net to achieve improved LDCT noise artifact suppression. Moreover, a hybrid loss function, e.g. mean absolute error (MAE) loss, edge loss and perceptual loss, was used to achieve better denoising effects. Experiments on the MAYO and UIH datasets demonstrated the performance of our framework. The results prove that the proposed approach can restrain noise and artifacts and maintain tissue structure during the LDCT imaging.
DOI:10.1109/CISP-BMEI56279.2022.9980070