Matches in SemOpenAlex for { <https://semopenalex.org/work/W3110242322> ?p ?o ?g. }
- W3110242322 endingPage "20201086" @default.
- W3110242322 startingPage "20201086" @default.
- W3110242322 abstract "To investigate the feasibility of using deep learning image reconstruction (DLIR) to significantly reduce radiation dose and improve image quality in contrast-enhanced abdominal CT.This was a prospective study. 40 patients with hepatic lesions underwent abdominal CT using routine dose (120kV, noise index (NI) setting of 11 with automatic tube current modulation) in the arterial-phase (AP) and portal-phase (PP), and low dose (NI = 24) in the delayed-phase (DP). All images were reconstructed at 1.25 mm thickness using ASIR-V at 50% strength. In addition, images in DP were reconstructed using DLIR in high setting (DLIR-H). The CT value and standard deviation (SD) of hepatic parenchyma, spleen, paraspinal muscle and lesion were measured. The overall image quality includes subjective noise, sharpness, artifacts and diagnostic confidence were assessed by two radiologists blindly using a 5-point scale (1, unacceptable and 5, excellent). Dose between AP and DP was compared, and image quality among different reconstructions were compared using SPSS20.0.Compared to AP, DP significantly reduced radiation dose by 76% (0.76 ± 0.09 mSv vs 3.18 ± 0.48 mSv), DLIR-H DP images had lower image noise (14.08 ± 2.89 HU vs 16.67 ± 3.74 HU, p < 0.001) but similar overall image quality score as the ASIR-V50% AP images (3.88 ± 0.34 vs 4.05 ± 0.44, p > 0.05). For the DP images, DLIR-H significantly reduced image noise in hepatic parenchyma, spleen, muscle and lesion to (14.77 ± 2.61 HU, 14.26 ± 2.67 HU, 14.08 ± 2.89 HU and 16.25 ± 4.42 HU) from (24.95 ± 4.32 HU, 25.42 ± 4.99 HU, 23.99 ± 5.26 HU and 27.01 ± 7.11) with ASIR-V50%, respectively (all p < 0.001) and improved image quality score (3.88 ± 0.34 vs 2.87 ± 0.53; p < 0.05).DLIR-H significantly reduces image noise and generates images with clinically acceptable quality and diagnostic confidence with 76% dose reduction.(1) DLIR-H yielded a significantly lower image noise, higher CNR and higher overall image quality score and diagnostic confidence than the ASIR-V50% under low signal conditions. (2) Our study demonstrated that at 76% lower radiation dose, the DLIR-H DP images had similar overall image quality to the routine-dose ASIR-V50% AP images." @default.
- W3110242322 created "2020-12-07" @default.
- W3110242322 creator A5004973480 @default.
- W3110242322 creator A5005474942 @default.
- W3110242322 creator A5025109208 @default.
- W3110242322 creator A5031589074 @default.
- W3110242322 creator A5059492462 @default.
- W3110242322 creator A5061568119 @default.
- W3110242322 creator A5068876730 @default.
- W3110242322 creator A5082704879 @default.
- W3110242322 creator A5086833756 @default.
- W3110242322 date "2021-02-01" @default.
- W3110242322 modified "2023-10-17" @default.
- W3110242322 title "A study of using a deep learning image reconstruction to improve the image quality of extremely low-dose contrast-enhanced abdominal CT for patients with hepatic lesions" @default.
- W3110242322 cites W1975751275 @default.
- W3110242322 cites W1986970768 @default.
- W3110242322 cites W2014838985 @default.
- W3110242322 cites W2017579767 @default.
- W3110242322 cites W2058254206 @default.
- W3110242322 cites W2150358707 @default.
- W3110242322 cites W2195096812 @default.
- W3110242322 cites W2320212003 @default.
- W3110242322 cites W2530976097 @default.
- W3110242322 cites W2744056307 @default.
- W3110242322 cites W2793071709 @default.
- W3110242322 cites W2807264744 @default.
- W3110242322 cites W2902429977 @default.
- W3110242322 cites W2943365367 @default.
- W3110242322 cites W2970681634 @default.
- W3110242322 cites W2971198564 @default.
- W3110242322 cites W3033347005 @default.
- W3110242322 doi "https://doi.org/10.1259/bjr.20201086" @default.
- W3110242322 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7934287" @default.
- W3110242322 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33242256" @default.
- W3110242322 hasPublicationYear "2021" @default.
- W3110242322 type Work @default.
- W3110242322 sameAs 3110242322 @default.
- W3110242322 citedByCount "43" @default.
- W3110242322 countsByYear W31102423222021 @default.
- W3110242322 countsByYear W31102423222022 @default.
- W3110242322 countsByYear W31102423222023 @default.
- W3110242322 crossrefType "journal-article" @default.
- W3110242322 hasAuthorship W3110242322A5004973480 @default.
- W3110242322 hasAuthorship W3110242322A5005474942 @default.
- W3110242322 hasAuthorship W3110242322A5025109208 @default.
- W3110242322 hasAuthorship W3110242322A5031589074 @default.
- W3110242322 hasAuthorship W3110242322A5059492462 @default.
- W3110242322 hasAuthorship W3110242322A5061568119 @default.
- W3110242322 hasAuthorship W3110242322A5068876730 @default.
- W3110242322 hasAuthorship W3110242322A5082704879 @default.
- W3110242322 hasAuthorship W3110242322A5086833756 @default.
- W3110242322 hasBestOaLocation W31102423222 @default.
- W3110242322 hasConcept C115961682 @default.
- W3110242322 hasConcept C126838900 @default.
- W3110242322 hasConcept C141071460 @default.
- W3110242322 hasConcept C141379421 @default.
- W3110242322 hasConcept C149857219 @default.
- W3110242322 hasConcept C154945302 @default.
- W3110242322 hasConcept C2776502983 @default.
- W3110242322 hasConcept C2781156865 @default.
- W3110242322 hasConcept C2987700449 @default.
- W3110242322 hasConcept C2989005 @default.
- W3110242322 hasConcept C35772409 @default.
- W3110242322 hasConcept C41008148 @default.
- W3110242322 hasConcept C55020928 @default.
- W3110242322 hasConcept C71924100 @default.
- W3110242322 hasConcept C86190813 @default.
- W3110242322 hasConceptScore W3110242322C115961682 @default.
- W3110242322 hasConceptScore W3110242322C126838900 @default.
- W3110242322 hasConceptScore W3110242322C141071460 @default.
- W3110242322 hasConceptScore W3110242322C141379421 @default.
- W3110242322 hasConceptScore W3110242322C149857219 @default.
- W3110242322 hasConceptScore W3110242322C154945302 @default.
- W3110242322 hasConceptScore W3110242322C2776502983 @default.
- W3110242322 hasConceptScore W3110242322C2781156865 @default.
- W3110242322 hasConceptScore W3110242322C2987700449 @default.
- W3110242322 hasConceptScore W3110242322C2989005 @default.
- W3110242322 hasConceptScore W3110242322C35772409 @default.
- W3110242322 hasConceptScore W3110242322C41008148 @default.
- W3110242322 hasConceptScore W3110242322C55020928 @default.
- W3110242322 hasConceptScore W3110242322C71924100 @default.
- W3110242322 hasConceptScore W3110242322C86190813 @default.
- W3110242322 hasIssue "1118" @default.
- W3110242322 hasLocation W31102423221 @default.
- W3110242322 hasLocation W31102423222 @default.
- W3110242322 hasLocation W31102423223 @default.
- W3110242322 hasOpenAccess W3110242322 @default.
- W3110242322 hasPrimaryLocation W31102423221 @default.
- W3110242322 hasRelatedWork W1973616517 @default.
- W3110242322 hasRelatedWork W2098713331 @default.
- W3110242322 hasRelatedWork W2231516567 @default.
- W3110242322 hasRelatedWork W2347508331 @default.
- W3110242322 hasRelatedWork W2745571083 @default.
- W3110242322 hasRelatedWork W2889844088 @default.
- W3110242322 hasRelatedWork W2902544649 @default.
- W3110242322 hasRelatedWork W2913417926 @default.
- W3110242322 hasRelatedWork W3140533390 @default.
- W3110242322 hasRelatedWork W4319986476 @default.