FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2293250945> ?p ?o ?g. }

• W2293250945 endingPage "590" @default.
• W2293250945 startingPage "582" @default.
• W2293250945 abstract "The aim of this study was to perform an objective and subjective image analysis of traditional and advanced noise-optimized virtual monoenergetic imaging (VMI) algorithms and standard linearly blended images in third-generation dual-source dual-energy computed tomography angiography (DE-CTA) of the thorax and abdomen.Thoracoabdominal DE-CTA examinations of 55 patients (36 male; mean age, 64.2 ± 12.7 years) were included in this retrospective institutional review board-approved study. Dual-energy computed tomography angiography data were reconstructed using standard linearly blended M_0.6 (merging 60% low kiloelectron volt [90 kV] with 40% high kiloelectron volt [150 kV] spectrum), traditional (VMI), and advanced VMI (VMI+) algorithms. Monoenergetic series were calculated ranging from 40 to 120 keV with 10 keV increments. Attenuation and standard deviation of 8 arteries and various anatomical landmarks of the thorax and abdomen were measured to calculate contrast-to-noise ratio values. Two radiologists subjectively assessed image quality, contrast conditions, noise, and visualization of small arterial branches using 5-point Likert scales.Vascular attenuation of VMI and VMI+ series showed a gradual increase from high to low kiloelectron volt levels without significant differences between both algorithms (P < 0.894). VMI+ 40-keV series showed the highest contrast-to-noise ratio for both thoracic and abdominal DE-CTA (P < 0.001), albeit revealing higher noise than M_0.6 images (objectively and subjectively, P < 0.001) and were rated best for visualization of small arterial branches in the subjective analysis (P < 0.109). Substantially increased noise was found for VMI 40 and 50 keV series compared with all other reconstructions (objectively and subjectively, P < 0.001). VMI+ images at 100 keV+ were rated best regarding image noise (P < 0.843), whereas VMI+ reconstructions at 70 keV were found to have superior subjective image quality (P < 0.031) compared with other series except for 60 and 80 keV VMI+ series (P < 0.587). Contrast conditions at 50 keV VMI+ were rated superior compared with 60 to 100 keV VMI and VMI+ reconstructions (P < 0.012).General image quality of DE-CTA examinations can be substantially improved using the VMI+ algorithm with observer preference of 70 keV, while 40 to 50 keV series provide superior contrast and improved visualization of small arterial branches compared with traditional VMI and standard linearly blended series." @default.
• W2293250945 created "2016-06-24" @default.
• W2293250945 creator A5001094178 @default.
• W2293250945 creator A5034275151 @default.
• W2293250945 creator A5035318279 @default.
• W2293250945 creator A5047067093 @default.
• W2293250945 creator A5073424291 @default.
• W2293250945 creator A5081983586 @default.
• W2293250945 creator A5087303728 @default.
• W2293250945 creator A5090779510 @default.
• W2293250945 date "2016-09-01" @default.
• W2293250945 modified "2023-10-02" @default.
• W2293250945 title "Comprehensive Comparison of Virtual Monoenergetic and Linearly Blended Reconstruction Techniques in Third-Generation Dual-Source Dual-Energy Computed Tomography Angiography of the Thorax and Abdomen" @default.
• W2293250945 cites W1003450378 @default.
• W2293250945 cites W1726548180 @default.
• W2293250945 cites W1879072041 @default.
• W2293250945 cites W1972105774 @default.
• W2293250945 cites W1973616517 @default.
• W2293250945 cites W1978795726 @default.
• W2293250945 cites W1991941661 @default.
• W2293250945 cites W2005398583 @default.
• W2293250945 cites W2016674043 @default.
• W2293250945 cites W2017352280 @default.
• W2293250945 cites W2017630418 @default.
• W2293250945 cites W2019627565 @default.
• W2293250945 cites W2029272997 @default.
• W2293250945 cites W2030543668 @default.
• W2293250945 cites W2034291124 @default.
• W2293250945 cites W2034477831 @default.
• W2293250945 cites W2051639025 @default.
• W2293250945 cites W2053154970 @default.
• W2293250945 cites W2067484116 @default.
• W2293250945 cites W2087863278 @default.
• W2293250945 cites W2090146992 @default.
• W2293250945 cites W2110230886 @default.
• W2293250945 cites W2133624681 @default.
• W2293250945 cites W2138416265 @default.
• W2293250945 cites W2139856958 @default.
• W2293250945 cites W2142562207 @default.
• W2293250945 cites W2145055257 @default.
• W2293250945 cites W2151012954 @default.
• W2293250945 cites W2323643803 @default.
• W2293250945 cites W2325838239 @default.
• W2293250945 cites W2332580908 @default.
• W2293250945 cites W2332852860 @default.
• W2293250945 cites W2409953222 @default.
• W2293250945 cites W2411925825 @default.
• W2293250945 doi "https://doi.org/10.1097/rli.0000000000000272" @default.
• W2293250945 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/26953565" @default.
• W2293250945 hasPublicationYear "2016" @default.
• W2293250945 type Work @default.
• W2293250945 sameAs 2293250945 @default.
• W2293250945 citedByCount "108" @default.
• W2293250945 countsByYear W22932509452016 @default.
• W2293250945 countsByYear W22932509452017 @default.
• W2293250945 countsByYear W22932509452018 @default.
• W2293250945 countsByYear W22932509452019 @default.
• W2293250945 countsByYear W22932509452020 @default.
• W2293250945 countsByYear W22932509452021 @default.
• W2293250945 countsByYear W22932509452022 @default.
• W2293250945 countsByYear W22932509452023 @default.
• W2293250945 crossrefType "journal-article" @default.
• W2293250945 hasAuthorship W2293250945A5001094178 @default.
• W2293250945 hasAuthorship W2293250945A5034275151 @default.
• W2293250945 hasAuthorship W2293250945A5035318279 @default.
• W2293250945 hasAuthorship W2293250945A5047067093 @default.
• W2293250945 hasAuthorship W2293250945A5073424291 @default.
• W2293250945 hasAuthorship W2293250945A5081983586 @default.
• W2293250945 hasAuthorship W2293250945A5087303728 @default.
• W2293250945 hasAuthorship W2293250945A5090779510 @default.
• W2293250945 hasConcept C105702510 @default.
• W2293250945 hasConcept C115961682 @default.
• W2293250945 hasConcept C120665830 @default.
• W2293250945 hasConcept C121332964 @default.
• W2293250945 hasConcept C126838900 @default.
• W2293250945 hasConcept C134018914 @default.
• W2293250945 hasConcept C154945302 @default.
• W2293250945 hasConcept C163716698 @default.
• W2293250945 hasConcept C2776502983 @default.
• W2293250945 hasConcept C2776541429 @default.
• W2293250945 hasConcept C2776886416 @default.
• W2293250945 hasConcept C2779983558 @default.
• W2293250945 hasConcept C2780643987 @default.
• W2293250945 hasConcept C2781347138 @default.
• W2293250945 hasConcept C2989005 @default.
• W2293250945 hasConcept C2994545362 @default.
• W2293250945 hasConcept C35772409 @default.
• W2293250945 hasConcept C41008148 @default.
• W2293250945 hasConcept C55020928 @default.
• W2293250945 hasConcept C71924100 @default.
• W2293250945 hasConcept C97834683 @default.
• W2293250945 hasConcept C99498987 @default.
• W2293250945 hasConceptScore W2293250945C105702510 @default.
• W2293250945 hasConceptScore W2293250945C115961682 @default.
• W2293250945 hasConceptScore W2293250945C120665830 @default.
• W2293250945 hasConceptScore W2293250945C121332964 @default.
• W2293250945 hasConceptScore W2293250945C126838900 @default.
• W2293250945 hasConceptScore W2293250945C134018914 @default.

• next