FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2313206576> ?p ?o ?g. }

• W2313206576 endingPage "421" @default.
• W2313206576 startingPage "415" @default.
• W2313206576 abstract "Objective Artifacts from dental hardware affect image quality and the visualization of lesions in the oral cavity and oropharynx in computed tomography (CT). Therefore, magnetic resonance imaging is considered the imaging modality of choice in this region. Standard methods for metal artifact reduction (MAR) in CT replace the metal-affected raw data by interpolation, which is prone to new artifacts. We developed a generalized normalization technique for MAR (NMAR) that aims to suppress algorithm-induced artifacts and validated the performance of this algorithm in a clinical trial. Material and Methods A 3-dimensional forward projection identifies the metal-affected raw data in the original projections after metal is segmented in the image domain by thresholding. A prior image is used to normalize the projections before interpolation. The original raw data are divided pixel-wise by the projection data of the prior image and, after interpolation, are denormalized again. Data from 19 consecutive patients with metal artifacts from dental hardware were reconstructed with standard filtered backprojection (FBP), linear interpolation MAR (LIMAR), and NMAR. The image quality of slices containing metal was analyzed for the severity of artifacts and diagnostic value; magnetic resonance imaging performed the same day on a 3-T system served as a reference standard in all cases. Results A total of 260 slices containing metal dental hardware were analyzed. A total of 164 slices were nondiagnostic with FBP, 157 slices with LIMAR, and 87 slices with NMAR. The mean (SD) number of slices per patient with severe artifacts was 10.1 (3.7), 9.6 (4.6), and 5.4 (3.6) and the mean (SD) number of slices with artifacts affecting diagnostic confidence was 3.3 (1.7), 4.9 (2.9), and 3.7 (1.9) for FBP, LIMAR, and NMAR, respectively (P < 0.001). Pairwise comparison did not show significant differences between FBP and LIMAR (P = 0.40), but there were significant differences between FBP and NMAR as well as LIMAR and NMAR (both P < 0.001). Interobserver agreement was excellent (κ = 0.974). Two malignant lesions were unmasked with NMAR image reconstructions. No algorithm-related artifacts were detected in regions that did not contain metal in NMAR images. Conclusion Normalized MAR has the potential to improve image quality in patients with artifacts from dental hardware and to improve the diagnostic accuracy of CT of the oral cavity and oropharynx." @default.
• W2313206576 created "2016-06-24" @default.
• W2313206576 creator A5015473744 @default.
• W2313206576 creator A5019247249 @default.
• W2313206576 creator A5082305673 @default.
• W2313206576 creator A5082428769 @default.
• W2313206576 creator A5086792229 @default.
• W2313206576 creator A5089472381 @default.
• W2313206576 creator A5091816459 @default.
• W2313206576 date "2012-07-01" @default.
• W2313206576 modified "2023-10-12" @default.
• W2313206576 title "Normalized Metal Artifact Reduction in Head and Neck Computed Tomography" @default.
• W2313206576 cites W1972105774 @default.
• W2313206576 cites W1972849804 @default.
• W2313206576 cites W1974872296 @default.
• W2313206576 cites W1978397329 @default.
• W2313206576 cites W1987289672 @default.
• W2313206576 cites W1992705935 @default.
• W2313206576 cites W1993828782 @default.
• W2313206576 cites W1996992165 @default.
• W2313206576 cites W2000441297 @default.
• W2313206576 cites W2018829440 @default.
• W2313206576 cites W2019559044 @default.
• W2313206576 cites W2025995141 @default.
• W2313206576 cites W2030027103 @default.
• W2313206576 cites W2035654516 @default.
• W2313206576 cites W2042580235 @default.
• W2313206576 cites W2047658579 @default.
• W2313206576 cites W2065588252 @default.
• W2313206576 cites W2078610757 @default.
• W2313206576 cites W2079127706 @default.
• W2313206576 cites W2087484885 @default.
• W2313206576 cites W2095757123 @default.
• W2313206576 cites W2098494036 @default.
• W2313206576 cites W2105010462 @default.
• W2313206576 cites W2136789364 @default.
• W2313206576 cites W2143204661 @default.
• W2313206576 cites W2154129661 @default.
• W2313206576 cites W2154280873 @default.
• W2313206576 cites W2157833176 @default.
• W2313206576 cites W2161106860 @default.
• W2313206576 cites W2165380849 @default.
• W2313206576 doi "https://doi.org/10.1097/rli.0b013e3182532f17" @default.
• W2313206576 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/22659592" @default.
• W2313206576 hasPublicationYear "2012" @default.
• W2313206576 type Work @default.
• W2313206576 sameAs 2313206576 @default.
• W2313206576 citedByCount "64" @default.
• W2313206576 countsByYear W23132065762013 @default.
• W2313206576 countsByYear W23132065762014 @default.
• W2313206576 countsByYear W23132065762015 @default.
• W2313206576 countsByYear W23132065762016 @default.
• W2313206576 countsByYear W23132065762017 @default.
• W2313206576 countsByYear W23132065762018 @default.
• W2313206576 countsByYear W23132065762019 @default.
• W2313206576 countsByYear W23132065762020 @default.
• W2313206576 countsByYear W23132065762021 @default.
• W2313206576 countsByYear W23132065762022 @default.
• W2313206576 countsByYear W23132065762023 @default.
• W2313206576 crossrefType "journal-article" @default.
• W2313206576 hasAuthorship W2313206576A5015473744 @default.
• W2313206576 hasAuthorship W2313206576A5019247249 @default.
• W2313206576 hasAuthorship W2313206576A5082305673 @default.
• W2313206576 hasAuthorship W2313206576A5082428769 @default.
• W2313206576 hasAuthorship W2313206576A5086792229 @default.
• W2313206576 hasAuthorship W2313206576A5089472381 @default.
• W2313206576 hasAuthorship W2313206576A5091816459 @default.
• W2313206576 hasConcept C115961682 @default.
• W2313206576 hasConcept C126838900 @default.
• W2313206576 hasConcept C136886441 @default.
• W2313206576 hasConcept C137800194 @default.
• W2313206576 hasConcept C143409427 @default.
• W2313206576 hasConcept C144024400 @default.
• W2313206576 hasConcept C153180895 @default.
• W2313206576 hasConcept C154945302 @default.
• W2313206576 hasConcept C171836373 @default.
• W2313206576 hasConcept C191178318 @default.
• W2313206576 hasConcept C19165224 @default.
• W2313206576 hasConcept C2779010991 @default.
• W2313206576 hasConcept C2779751349 @default.
• W2313206576 hasConcept C2989005 @default.
• W2313206576 hasConcept C31972630 @default.
• W2313206576 hasConcept C36464697 @default.
• W2313206576 hasConcept C41008148 @default.
• W2313206576 hasConcept C55020928 @default.
• W2313206576 hasConcept C71924100 @default.
• W2313206576 hasConceptScore W2313206576C115961682 @default.
• W2313206576 hasConceptScore W2313206576C126838900 @default.
• W2313206576 hasConceptScore W2313206576C136886441 @default.
• W2313206576 hasConceptScore W2313206576C137800194 @default.
• W2313206576 hasConceptScore W2313206576C143409427 @default.
• W2313206576 hasConceptScore W2313206576C144024400 @default.
• W2313206576 hasConceptScore W2313206576C153180895 @default.
• W2313206576 hasConceptScore W2313206576C154945302 @default.
• W2313206576 hasConceptScore W2313206576C171836373 @default.
• W2313206576 hasConceptScore W2313206576C191178318 @default.
• W2313206576 hasConceptScore W2313206576C19165224 @default.
• W2313206576 hasConceptScore W2313206576C2779010991 @default.

• next