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Self-supervised tomographic image noise suppression via residual image prior network.
Pan, Jiayi; Chang, Dingyue; Wu, Weiwen; Chen, Yang; Wang, Shaoyu.
Afiliación
  • Pan J; School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong, China. Electronic address: panjy59@mail2.sysu.edu.cn.
  • Chang D; Institute of Materials, China Academy of Engineering Physics, Mianyang, Sichuan, China.
  • Wu W; School of Biomedical Engineering, Sun Yat-Sen University, Shenzhen, Guangdong, China. Electronic address: wuweiw7@mail.sysu.edu.cn.
  • Chen Y; School of Computer Science and Engineering, Southeast University, Nanjing, Jiangsu, China. Electronic address: chenyang.list@seu.edu.cn.
  • Wang S; School of Information Engineering, Nanchang University, Nanchang, Jiangxi, China. Electronic address: wangsy1@cqu.edu.cn.
Comput Biol Med ; 179: 108837, 2024 Sep.
Article en En | MEDLINE | ID: mdl-38991317
ABSTRACT
Computed tomography (CT) denoising is a challenging task in medical imaging that has garnered considerable attention. Supervised networks require a lot of noisy-clean image pairs, which are always unavailable in clinical settings. Existing self-supervised algorithms for suppressing noise with paired noisy images have limitations, such as ignoring the residual between similar image pairs during training and insufficiently learning the spectrum information of images. In this study, we propose a Residual Image Prior Network (RIP-Net) to sufficiently model the residual between the paired similar noisy images. Our approach offers new insights into the field by addressing the limitations of existing methods. We first establish a mathematical theorem clarifying the non-equivalence between similar-image-based self-supervised learning and supervised learning. It helps us better understand the strengths and limitations of self-supervised learning. Secondly, we introduce a novel regularization term to model a low-frequency residual image prior. This can improve the accuracy and robustness of our model. Finally, we design a well-structured denoising network capable of exploring spectrum information while simultaneously sensing context messages. The network has dual paths for modeling high and low-frequency compositions in the raw noisy image. Additionally, context perception modules capture local and global interactions to produce high-quality images. The comprehensive experiments on preclinical photon-counting CT, clinical brain CT, and low-dose CT datasets, demonstrate that our RIP-Net is superior to other unsupervised denoising methods.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Tomografía Computarizada por Rayos X Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Algoritmos / Tomografía Computarizada por Rayos X Límite: Humans Idioma: En Revista: Comput Biol Med Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos