Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies.
In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plet...
| Publicado en: | Journal of Imaging Informatics in Medicine Vol. 38; no. 2; pp. 902 - 931 |
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| Autores principales: | , , , , , , , |
| Formato: | algorithm diagnostic images equations & formulas pictorial review tables/charts Journal Article |
| Publicado: |
Springer Nature
Apr2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=184081724&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 184081724 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 29482925 NR3A jtl: Journal of Imaging Informatics in Medicine issn: 29482925 maglogo: N pubinfo: dt: Apr2025 vid: 38 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 184081724 184081724 184081724 10.1007/s10278-024-01213-8 184081724 ppf: 902 ppct: 29 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Unsupervised and Self-supervised Learning in Low-Dose Computed Tomography Denoising: Insights from Training Strategies. aug: au: Zhao, Feixiang Liu, Mingzhe Xiang, Mingrong Li, Dongfen Jiang, Xin Jin, Xiance Lin, Cai Wang, Ruili affil: https://ror.org/03dd7qj98 School of Data Science and Artificial Intelligence, Wenzhou University of Technology, Ouhai District, 325000, Wenzhou, Zhejiang, China sug: subj: Tomography, X-Ray Computed Methods Machine Learning Methods Deep Learning Methods Self Care Image Processing, Computer Assisted Methods Radiation Dosage Sensitivity and Specificity X-Rays Diagnosis, Computer Assisted ab: In recent years, X-ray low-dose computed tomography (LDCT) has garnered widespread attention due to its significant reduction in the risk of patient radiation exposure. However, LDCT images often contain a substantial amount of noises, adversely affecting diagnostic quality. To mitigate this, a plethora of LDCT denoising methods have been proposed. Among them, deep learning (DL) approaches have emerged as the most effective, due to their robust feature extraction capabilities. Yet, the prevalent use of supervised training paradigms is often impractical due to the challenges in acquiring low-dose and normal-dose CT pairs in clinical settings. Consequently, unsupervised and self-supervised deep learning methods have been introduced for LDCT denoising, showing considerable potential for clinical applications. These methods' efficacy hinges on training strategies. Notably, there appears to be no comprehensive reviews of these strategies. Our review aims to address this gap, offering insights and guidance for researchers and practitioners. Based on training strategies, we categorize the LDCT methods into six groups: (i) cycle consistency-based, (ii) score matching-based, (iii) statistical characteristics of noise-based, (iv) similarity-based, (v) LDCT synthesis model-based, and (vi) hybrid methods. For each category, we delve into the theoretical underpinnings, training strategies, strengths, and limitations. In addition, we also summarize the open source codes of the reviewed methods. Finally, the review concludes with a discussion on open issues and future research directions. pubtype: Academic Journal doctype: algorithm diagnostic images equations & formulas pictorial review tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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