Noise suppression-guided image filtering for low-SNR CT reconstruction.
In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SAR...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 58; no. 11; pp. 2621 - 2630 |
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| Autores principales: | , , , |
| Formato: | Journal Article |
| Publicado: |
Springer Nature
Nov2020
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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=146433307&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 146433307 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Nov2020 vid: 58 iid: 11 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 146433307 146433307 NLM32839918 10.1007/s11517-020-02246-1 NLM32839918 146433307 ppf: 2621 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Noise suppression-guided image filtering for low-SNR CT reconstruction. aug: au: He, Yuanwei Zeng, Li Yu, Wei Gong, Changcheng affil: College of Mathematics and Statistics, Chongqing University, 401331, Chongqing, China sug: subj: Image Processing, Computer Assisted Methods Tomography, X-Ray Computed Methods Imaging, Three-Dimensional Methods Head Walnut Sensitivity and Specificity Algorithms Phantoms, Imaging Ferrans and Powers Quality of Life Index ab: In practical computed tomography (CT) applications, projections with low signal-to-noise ratio (SNR) are often encountered due to the reduction of radiation dose or device limitations. In these situations, classical reconstruction algorithms, like simultaneous algebraic reconstruction technique (SART), cannot reconstruct high-quality CT images. Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. In each iteration of NSGIFR, the output image of SART reserves more details and is used as input image of GIF, while the image denoised by BM3D serves as guidance image of GIF. Experimental results indicate that the proposed algorithm displays outstanding performance on preserving structures and suppressing noise for low-SNR CT reconstruction. NSGIFR can achieve more superior image quality than SART, POCS-TV and POCS-BM3D in terms of visual effect and quantitative analysis. Graphical abstract Block-matching and 3D filtering (BM3D)-based iterative reconstruction algorithm (POCS-BM3D) has remarkable effect in dealing with CT reconstruction from noisy projections. However, BM3D may restrain noise with excessive loss of details in the case of low-SNR CT reconstruction. In order to achieve a preferable trade-off between noise suppression and edge preservation, we introduce guided image filtering (GIF) into low-SNR CT reconstruction, and propose noise suppression-guided image filtering reconstruction (NSGIFR) algorithm. pubtype: Academic Journal doctype: Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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