Matches in SemOpenAlex for { <https://semopenalex.org/work/W2952743108> ?p ?o ?g. }
Showing items 1 to 91 of
91
with 100 items per page.
- W2952743108 abstract "Conebeam CT using a circular trajectory is quite often used for various applications due to its relative simple geometry. For conebeam geometry, Feldkamp, Davis and Kress algorithm is regarded as the standard reconstruction method, but this algorithm suffers from so-called conebeam artifacts as the cone angle increases. Various model-based iterative reconstruction methods have been developed to reduce the cone-beam artifacts, but these algorithms usually require multiple applications of computational expensive forward and backprojections. In this paper, we develop a novel deep learning approach for accurate conebeam artifact removal. In particular, our deep network, designed on the differentiated backprojection domain, performs a data-driven inversion of an ill-posed deconvolution problem associated with the Hilbert transform. The reconstruction results along the coronal and sagittal directions are then combined using a spectral blending technique to minimize the spectral leakage. Experimental results show that our method outperforms the existing iterative methods despite significantly reduced runtime complexity." @default.
- W2952743108 created "2019-06-27" @default.
- W2952743108 creator A5012644755 @default.
- W2952743108 creator A5040656804 @default.
- W2952743108 creator A5061853496 @default.
- W2952743108 date "2019-06-17" @default.
- W2952743108 modified "2023-09-23" @default.
- W2952743108 title "Differentiated Backprojection Domain Deep Learning for Conebeam Artifact Removal" @default.
- W2952743108 cites W1565327149 @default.
- W2952743108 cites W1882111414 @default.
- W2952743108 cites W1901129140 @default.
- W2952743108 cites W1963882359 @default.
- W2952743108 cites W1966990770 @default.
- W2952743108 cites W1972150100 @default.
- W2952743108 cites W1977849232 @default.
- W2952743108 cites W1983148127 @default.
- W2952743108 cites W2018085986 @default.
- W2952743108 cites W2054324425 @default.
- W2952743108 cites W2060686929 @default.
- W2952743108 cites W2068654355 @default.
- W2952743108 cites W2077236721 @default.
- W2952743108 cites W2078862924 @default.
- W2952743108 cites W2108298552 @default.
- W2952743108 cites W2133665775 @default.
- W2952743108 cites W2157812230 @default.
- W2952743108 cites W2163605009 @default.
- W2952743108 cites W2166858223 @default.
- W2952743108 cites W2328084817 @default.
- W2952743108 cites W2528907249 @default.
- W2952743108 cites W2556016755 @default.
- W2952743108 cites W2574952845 @default.
- W2952743108 cites W2584483805 @default.
- W2952743108 cites W2777802649 @default.
- W2952743108 cites W2779783589 @default.
- W2952743108 cites W2795777276 @default.
- W2952743108 cites W2893096016 @default.
- W2952743108 cites W2912265583 @default.
- W2952743108 cites W2963392702 @default.
- W2952743108 doi "https://doi.org/10.48550/arxiv.1906.06854" @default.
- W2952743108 hasPublicationYear "2019" @default.
- W2952743108 type Work @default.
- W2952743108 sameAs 2952743108 @default.
- W2952743108 citedByCount "0" @default.
- W2952743108 crossrefType "posted-content" @default.
- W2952743108 hasAuthorship W2952743108A5012644755 @default.
- W2952743108 hasAuthorship W2952743108A5040656804 @default.
- W2952743108 hasAuthorship W2952743108A5061853496 @default.
- W2952743108 hasBestOaLocation W29527431081 @default.
- W2952743108 hasConcept C106131492 @default.
- W2952743108 hasConcept C108583219 @default.
- W2952743108 hasConcept C113407356 @default.
- W2952743108 hasConcept C11413529 @default.
- W2952743108 hasConcept C141379421 @default.
- W2952743108 hasConcept C154945302 @default.
- W2952743108 hasConcept C159694833 @default.
- W2952743108 hasConcept C174576160 @default.
- W2952743108 hasConcept C2779010991 @default.
- W2952743108 hasConcept C28799612 @default.
- W2952743108 hasConcept C31972630 @default.
- W2952743108 hasConcept C41008148 @default.
- W2952743108 hasConcept C75172450 @default.
- W2952743108 hasConceptScore W2952743108C106131492 @default.
- W2952743108 hasConceptScore W2952743108C108583219 @default.
- W2952743108 hasConceptScore W2952743108C113407356 @default.
- W2952743108 hasConceptScore W2952743108C11413529 @default.
- W2952743108 hasConceptScore W2952743108C141379421 @default.
- W2952743108 hasConceptScore W2952743108C154945302 @default.
- W2952743108 hasConceptScore W2952743108C159694833 @default.
- W2952743108 hasConceptScore W2952743108C174576160 @default.
- W2952743108 hasConceptScore W2952743108C2779010991 @default.
- W2952743108 hasConceptScore W2952743108C28799612 @default.
- W2952743108 hasConceptScore W2952743108C31972630 @default.
- W2952743108 hasConceptScore W2952743108C41008148 @default.
- W2952743108 hasConceptScore W2952743108C75172450 @default.
- W2952743108 hasLocation W29527431081 @default.
- W2952743108 hasOpenAccess W2952743108 @default.
- W2952743108 hasPrimaryLocation W29527431081 @default.
- W2952743108 hasRelatedWork W2060018053 @default.
- W2952743108 hasRelatedWork W2136397649 @default.
- W2952743108 hasRelatedWork W2906703443 @default.
- W2952743108 hasRelatedWork W3033459146 @default.
- W2952743108 hasRelatedWork W3046774880 @default.
- W2952743108 hasRelatedWork W4285529901 @default.
- W2952743108 hasRelatedWork W4294892452 @default.
- W2952743108 hasRelatedWork W4379382345 @default.
- W2952743108 hasRelatedWork W982580560 @default.
- W2952743108 hasRelatedWork W3137059050 @default.
- W2952743108 isParatext "false" @default.
- W2952743108 isRetracted "false" @default.
- W2952743108 magId "2952743108" @default.
- W2952743108 workType "article" @default.