126 related articles for article (PubMed ID: 38224617)
1. Breaking boundaries in radiology: redefining AI diagnostics via raw data ahead of reconstruction.
He B; Sun C; Li H; Wang Y; She Y; Zhao M; Fang M; Zhu Y; Wang K; Liu Z; Wei Z; Mu W; Wang S; Tang Z; Wei J; Shao L; Tong L; Huang F; Tang M; Guo Y; Zhang H; Dong D; Chen C; Ma J; Tian J
Phys Med Biol; 2024 Mar; 69(7):. PubMed ID: 38224617
[No Abstract] [Full Text] [Related]
2. Comparison of Chest Radiograph Interpretations by Artificial Intelligence Algorithm vs Radiology Residents.
Wu JT; Wong KCL; Gur Y; Ansari N; Karargyris A; Sharma A; Morris M; Saboury B; Ahmad H; Boyko O; Syed A; Jadhav A; Wang H; Pillai A; Kashyap S; Moradi M; Syeda-Mahmood T
JAMA Netw Open; 2020 Oct; 3(10):e2022779. PubMed ID: 33034642
[TBL] [Abstract][Full Text] [Related]
3. Impact of an artificial intelligence deep-learning reconstruction algorithm for CT on image quality and potential dose reduction: A phantom study.
Greffier J; Si-Mohamed S; Frandon J; Loisy M; de Oliveira F; Beregi JP; Dabli D
Med Phys; 2022 Aug; 49(8):5052-5063. PubMed ID: 35696272
[TBL] [Abstract][Full Text] [Related]
4. Improved image quality and dose reduction in abdominal CT with deep-learning reconstruction algorithm: a phantom study.
Greffier J; Durand Q; Frandon J; Si-Mohamed S; Loisy M; de Oliveira F; Beregi JP; Dabli D
Eur Radiol; 2023 Jan; 33(1):699-710. PubMed ID: 35864348
[TBL] [Abstract][Full Text] [Related]
5. A novel simulation-driven reconstruction approach for x-ray computed tomography.
Hsieh J
Med Phys; 2022 Apr; 49(4):2245-2258. PubMed ID: 35102555
[TBL] [Abstract][Full Text] [Related]
6. Contribution of an artificial intelligence deep-learning reconstruction algorithm for dose optimization in lumbar spine CT examination: A phantom study.
Greffier J; Frandon J; Durand Q; Kammoun T; Loisy M; Beregi JP; Dabli D
Diagn Interv Imaging; 2023 Feb; 104(2):76-83. PubMed ID: 36100524
[TBL] [Abstract][Full Text] [Related]
7. Artificial intelligence for detecting small FDG-positive lung nodules in digital PET/CT: impact of image reconstructions on diagnostic performance.
Schwyzer M; Martini K; Benz DC; Burger IA; Ferraro DA; Kudura K; Treyer V; von Schulthess GK; Kaufmann PA; Huellner MW; Messerli M
Eur Radiol; 2020 Apr; 30(4):2031-2040. PubMed ID: 31822970
[TBL] [Abstract][Full Text] [Related]
8. Lung Nodule Detectability of Artificial Intelligence-assisted CT Image Reading in Lung Cancer Screening.
Zhang Y; Jiang B; Zhang L; Greuter MJW; de Bock GH; Zhang H; Xie X
Curr Med Imaging; 2022; 18(3):327-334. PubMed ID: 34365951
[TBL] [Abstract][Full Text] [Related]
9. A deep residual learning network for predicting lung adenocarcinoma manifesting as ground-glass nodule on CT images.
Gong J; Liu J; Hao W; Nie S; Zheng B; Wang S; Peng W
Eur Radiol; 2020 Apr; 30(4):1847-1855. PubMed ID: 31811427
[TBL] [Abstract][Full Text] [Related]
10. A cascade-based dual-domain data correction network for sparse view CT image reconstruction.
Li Q; Li R; Wang T; Cheng Y; Qiang Y; Wu W; Zhao J; Zhang D
Comput Biol Med; 2023 Oct; 165():107345. PubMed ID: 37603960
[TBL] [Abstract][Full Text] [Related]
11. Performance of an Artificial Intelligence-Based Platform Against Clinical Radiology Reports for the Evaluation of Noncontrast Chest CT.
Yacoub B; Kabakus IM; Schoepf UJ; Giovagnoli VM; Fischer AM; Wichmann JL; Martinez JD; Sharma P; Rapaka S; Sahbaee P; Hoelzer P; Burt JR; Varga-Szemes A; Emrich T
Acad Radiol; 2022 Feb; 29 Suppl 2():S108-S117. PubMed ID: 33714665
[TBL] [Abstract][Full Text] [Related]
12. Low-dose CT reconstruction method based on prior information of normal-dose image.
Chen Z; Zhang Q; Zhou C; Zhang M; Yang Y; Liu X; Zheng H; Liang D; Hu Z
J Xray Sci Technol; 2020; 28(6):1091-1111. PubMed ID: 33044223
[TBL] [Abstract][Full Text] [Related]
13. Innovative advances in pediatric radiology: computed tomography reconstruction techniques, photon-counting detector computed tomography, and beyond.
Mese I; Altintas Mese C; Demirsoy U; Anik Y
Pediatr Radiol; 2024 Jan; 54(1):1-11. PubMed ID: 38041712
[TBL] [Abstract][Full Text] [Related]
14. Deep Learning CT Image Reconstruction in Clinical Practice.
Arndt C; Güttler F; Heinrich A; Bürckenmeyer F; Diamantis I; Teichgräber U
Rofo; 2021 Mar; 193(3):252-261. PubMed ID: 33302311
[TBL] [Abstract][Full Text] [Related]
15. Object recognition in medical images via anatomy-guided deep learning.
Jin C; Udupa JK; Zhao L; Tong Y; Odhner D; Pednekar G; Nag S; Lewis S; Poole N; Mannikeri S; Govindasamy S; Singh A; Camaratta J; Owens S; Torigian DA
Med Image Anal; 2022 Oct; 81():102527. PubMed ID: 35830745
[TBL] [Abstract][Full Text] [Related]
16. Variability in CT lung-nodule quantification: Effects of dose reduction and reconstruction methods on density and texture based features.
Lo P; Young S; Kim HJ; Brown MS; McNitt-Gray MF
Med Phys; 2016 Aug; 43(8):4854. PubMed ID: 27487903
[TBL] [Abstract][Full Text] [Related]
17. Enhancing cardiac CT imaging quality: Precision metrics for assessing image quality for AI-powered reconstructions.
Longère B; Dacher JN
Diagn Interv Imaging; 2024 Mar; 105(3):85-86. PubMed ID: 38052674
[No Abstract] [Full Text] [Related]
18. A dual-domain neural network based on sinogram synthesis for sparse-view CT reconstruction.
Zhang P; Li K
Comput Methods Programs Biomed; 2022 Nov; 226():107168. PubMed ID: 36219892
[TBL] [Abstract][Full Text] [Related]
19. A fully automatic artificial intelligence-based CT image analysis system for accurate detection, diagnosis, and quantitative severity evaluation of pulmonary tuberculosis.
Yan C; Wang L; Lin J; Xu J; Zhang T; Qi J; Li X; Ni W; Wu G; Huang J; Xu Y; Woodruff HC; Lambin P
Eur Radiol; 2022 Apr; 32(4):2188-2199. PubMed ID: 34842959
[TBL] [Abstract][Full Text] [Related]
20. Effect of CT Reconstruction Algorithm on the Diagnostic Performance of Radiomics Models: A Task-Based Approach for Pulmonary Subsolid Nodules.
Kim H; Park CM; Gwak J; Hwang EJ; Lee SY; Jung J; Hong H; Goo JM
AJR Am J Roentgenol; 2019 Mar; 212(3):505-512. PubMed ID: 30476456
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
