A comprehensive survey on deep learning techniques in CT image quality improvement.
High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 60; no. 10; pp. 2757 - 2771 |
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| Autores principales: | , , , , , |
| Formato: | review Journal Article |
| Publicado: |
Springer Nature
Oct2022
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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=159003924&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 159003924 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Oct2022 vid: 60 iid: 10 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 159003924 158473409 159003924 NLM35962932 159003924 10.1007/s11517-022-02631-y NLM35962932 159003924 ppf: 2757 ppct: 14 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A comprehensive survey on deep learning techniques in CT image quality improvement. aug: au: Li, Disen Ma, Limin Li, Jining Qi, Shouliang Yao, Yudong Teng, Yueyang affil: College of Medicine and Biological Information Engineering, Northeastern University, 110819, Shenyang, China sug: subj: Algorithms Tomography, X-Ray Computed Methods Image Processing, Computer Assisted Methods Quality Improvement Funding Source ab: High-quality computed tomography (CT) images are key to clinical diagnosis. However, the current quality of an image is limited by reconstruction algorithms and other factors and still needs to be improved. When using CT, a large quantity of imaging data, including intermediate data and final images, that can reflect important physical processes in a statistical sense are accumulated. However, traditional imaging techniques cannot make full use of them. Recently, deep learning, in which the large quantity of imaging data can be utilized and patterns can be learned by a hierarchical structure, has provided new ideas for CT image quality improvement. Many researchers have proposed a large number of deep learning algorithms to improve CT image quality, especially in the field of image postprocessing. This survey reviews these algorithms and identifies future directions. pubtype: Academic Journal doctype: review Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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