Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism.
Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolu...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1902 - 1922 |
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| Autores principales: | , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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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=179554158&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554158 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554158 179554158 179554158 10.1007/s10278-024-00979-1 179554158 ppf: 1902 ppct: 20 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Low-Dose CT Image Super-resolution Network with Noise Inhibition Based on Feedback Feature Distillation Mechanism. aug: au: Chi, Jianning Wei, Xiaolin Sun, Zhiyi Yang, Yongming Yang, Bin affil: https://ror.org/03awzbc87 Faculty of Robot Science and Engineering, Northeastern University, Zhihui Street, 110169, Shenyang, Liaoning, China sug: subj: Tomography, X-Ray Computed Methods Image Enhancement Sensitivity and Specificity Algorithms Human Artifacts Image Interpretation, Computer Assisted Ablation Techniques Descriptive Statistics Funding Source ab: Low-dose computed tomography (LDCT) has been widely used in medical diagnosis. In practice, doctors often zoom in on LDCT slices for clearer lesions and issues, while, a simple zooming operation fails to suppress low-dose artifacts, leading to distorted details. Therefore, numerous LDCT super-resolution (SR) methods have been proposed to promote the quality of zooming without the increase of the dose in CT scanning. However, there are still some drawbacks that need to be addressed in existing methods. First, the region of interest (ROI) is not emphasized due to the lack of guidance in the reconstruction process. Second, the convolutional blocks extracting fix-resolution features fail to concentrate on the essential multi-scale features. Third, a single SR head cannot suppress the residual artifacts. To address these issues, we propose an LDCT CT joint SR and denoising reconstruction network. Our proposed network consists of global dual-guidance attention fusion modules (GDAFMs) and multi-scale anastomosis blocks (MABs). The GDAFM directs the network to focus on ROI by fusing the extra mask guidance and average CT image guidance, while the MAB introduces hierarchical features through anastomosis connections to leverage multi-scale features and promote the feature representation ability. To suppress radial residual artifacts, we optimize our network using the feedback feature distillation mechanism (FFDM) which shares the backbone to learn features corresponding to the denoising task. We apply the proposed method to the 3D-IRCADB and PANCREAS datasets to evaluate its ability on LDCT image SR reconstruction. The experimental results compared with state-of-the-art methods illustrate the superiority of our approach with respect to peak signal-to-noise (PSNR), structural similarity (SSIM), and qualitative observations. Our proposed LDCT joint SR and denoising reconstruction network has been extensively evaluated through ablation, quantitative, and qualitative experiments. The results demonstrate that our method can recover noise-free and detail-sharp images, resulting in better reconstruction results. Code is available at https://github.com/neu-szy/ldct_sr_dn_w_ffdm. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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