Model Image-Based Metal Artifact Reduction for Computed Tomography.
Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a...
| Publicado en: | Journal of Digital Imaging Vol. 33; no. 1; pp. 71 - 83 |
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| Autores principales: | , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Springer Nature
Feb2020
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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=142164503&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 142164503 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Feb2020 vid: 33 iid: 1 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 142164503 142164503 142164503 10.1007/s10278-019-00210-6 142164503 ppf: 71 ppct: 12 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Model Image-Based Metal Artifact Reduction for Computed Tomography. aug: au: Luzhbin, Dmytro Wu, Jay affil: Department of Biomedical Imaging and Radiological Sciences, National Yang-Ming University, No. 155, Sec. 2, Linong Street, 11221, Taipei, Taiwan, Republic of China sug: subj: Metals Artifacts Tomography, X-Ray Computed Methods Diagnostic Imaging Algorithms Methods Phantoms, Imaging Human Water Radiotherapy Tomography, Emission-Computed Tomography, Emission-Computed, Single-Photon ab: Metal implants often produce severe artifacts in the reconstructed computed tomography (CT) images, causing information and image detail loss and making the CT images diagnostically unusable. In order to eliminate the metal artifacts and enhance the diagnostic value of the reconstructed CT images, a post-processing metal artifact reduction algorithm, based on a tissue-class model segmented by thresholding and k-means clustering with spatial information, is proposed. The image inpainting technique is incorporated into the algorithm to improve the segmentation accuracy for CT images severely corrupted by metal artifacts. A study of a water phantom and of two sets of clinical CT images was performed to test the algorithm performance. The proposed method effectively eliminates typical metal artifacts, restores the average CT numbers of different tissues to the proper levels, and preserves the edge and contrast information, thus allowing the accurate reconstruction of the tissue attenuation map. The quality of the artifact-corrected CT images allows them to be subsequently used in other clinical applications, such as three-dimensional rendering, dose estimation for radiotherapy, attenuation correction for PET and SPECT, etc. The algorithm does not rely on the use of the raw sinogram and so is not limited by the proprietary format restrictions. 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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