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Enhancement based convolutional dictionary network with adaptive window for low-dose CT denoising.
Liu, Yi; Yan, Rongbiao; Liu, Yuhang; Zhang, Pengcheng; Chen, Yang; Gui, Zhiguo.
Afiliación
  • Liu Y; The State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
  • Yan R; The State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
  • Liu Y; The State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
  • Zhang P; The State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
  • Chen Y; The Key Laboratory of Computer Network and Information Integration, Southeast University, Ministry of Education, Nanjing, China.
  • Gui Z; The State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan, China.
J Xray Sci Technol ; 31(6): 1165-1187, 2023.
Article en En | MEDLINE | ID: mdl-37694333
BACKGROUND: Recently, one promising approach to suppress noise/artifacts in low-dose CT (LDCT) images is the CNN-based approach, which learns the mapping function from LDCT to normal-dose CT (NDCT). However, most CNN-based methods are purely data-driven, thus lacking sufficient interpretability and often losing details. OBJECTIVE: To solve this problem, we propose a deep convolutional dictionary learning method for LDCT denoising, in which a novel convolutional dictionary learning model with adaptive window (CDL-AW) is designed, and a corresponding enhancement-based convolutional dictionary learning network (called ECDAW-Net) is constructed to unfold the CDL-AW model iteratively using the proximal gradient descent technique. METHODS: In detail, the adaptive window-constrained convolutional dictionary atom is proposed to alleviate spectrum leakage caused by data truncation during convolution. Furthermore, in the ECDAW-Net, a multi-scale edge extraction module that consists of LoG and Sobel convolution layers is proposed in the unfolding iteration, to supplement lost textures and details. Additionally, to further improve the detail retention ability, the ECDAW-Net is trained by the compound loss function of the pixel-level MSE loss and the proposed patch-level loss, which can assist to retain richer structural information. RESULTS: Applying ECDAW-Net to the Mayo dataset, we obtained the highest peak signal-to-noise ratio (33.94) and sub-optimal structural similarity (0.92). CONCLUSIONS: Compared with some state-of-art methods, the interpretable ECDAW-Net performs well in suppressing noise/artifacts and preserving textures of tissue.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Tomografía Computarizada por Rayos X Idioma: En Revista: J Xray Sci Technol Asunto de la revista: RADIOLOGIA Año: 2023 Tipo del documento: Article País de afiliación: China Pais de publicación: Países Bajos