BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

118 related articles for article (PubMed ID: 38393883)

  • 1. Performance evaluation of deep learning image reconstruction algorithm for dual-energy spectral CT imaging: A phantom study.
    Li H; Li Z; Gao S; Hu J; Yang Z; Peng Y; Sun J
    J Xray Sci Technol; 2024; 32(3):513-528. PubMed ID: 38393883
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Evaluation of Image Quality and Detectability of Deep Learning Image Reconstruction (DLIR) Algorithm in Single- and Dual-energy CT.
    Zhong J; Shen H; Chen Y; Xia Y; Shi X; Lu W; Li J; Xing Y; Hu Y; Ge X; Ding D; Jiang Z; Yao W
    J Digit Imaging; 2023 Aug; 36(4):1390-1407. PubMed ID: 37071291
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.
    Greffier J; Hamard A; Pereira F; Barrau C; Pasquier H; Beregi JP; Frandon J
    Eur Radiol; 2020 Jul; 30(7):3951-3959. PubMed ID: 32100091
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Task-based characterization of a deep learning image reconstruction and comparison with filtered back-projection and a partial model-based iterative reconstruction in abdominal CT: A phantom study.
    Racine D; Becce F; Viry A; Monnin P; Thomsen B; Verdun FR; Rotzinger DC
    Phys Med; 2020 Aug; 76():28-37. PubMed ID: 32574999
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Deep learning imaging reconstruction of reduced-dose 40 keV virtual monoenergetic imaging for early detection of colorectal cancer liver metastases.
    Li S; Yuan L; Lu T; Yang X; Ren W; Wang L; Zhao J; Deng J; Liu X; Xue C; Sun Q; Zhang W; Zhou J
    Eur J Radiol; 2023 Nov; 168():111128. PubMed ID: 37816301
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Impact of noise reduction on radiation dose reduction potential of virtual monochromatic spectral images: Comparison of phantom images with conventional 120 kVp images using deep learning image reconstruction and hybrid iterative reconstruction.
    Masuda S; Yamada Y; Minamishima K; Owaki Y; Yamazaki A; Jinzaki M
    Eur J Radiol; 2022 Apr; 149():110198. PubMed ID: 35168172
    [TBL] [Abstract][Full Text] [Related]  

  • 7. A Characterization of Deep Learning Reconstruction Applied to Dual-Energy Computed Tomography Monochromatic and Material Basis Images.
    Nikolau EP; Toia GV; Nett B; Tang J; Szczykutowicz TP
    J Comput Assist Tomogr; 2023 May-Jun 01; 47(3):437-444. PubMed ID: 37185008
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Quantitative and qualitative assessments of deep learning image reconstruction in low-keV virtual monoenergetic dual-energy CT.
    Xu JJ; Lönn L; Budtz-Jørgensen E; Hansen KL; Ulriksen PS
    Eur Radiol; 2022 Oct; 32(10):7098-7107. PubMed ID: 35895120
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Deep-learning image reconstruction for image quality evaluation and accurate bone mineral density measurement on quantitative CT: A phantom-patient study.
    Li Y; Jiang Y; Yu X; Ren B; Wang C; Chen S; Ma D; Su D; Liu H; Ren X; Yang X; Gao J; Wu Y
    Front Endocrinol (Lausanne); 2022; 13():884306. PubMed ID: 36034436
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Detectability of Small Low-Attenuation Lesions With Deep Learning CT Image Reconstruction: A 24-Reader Phantom Study.
    Toia GV; Zamora DA; Singleton M; Liu A; Tan E; Leng S; Shuman WP; Kanal KM; Mileto A
    AJR Am J Roentgenol; 2023 Feb; 220(2):283-295. PubMed ID: 36129222
    [No Abstract]   [Full Text] [Related]  

  • 11. Improving lesion conspicuity in abdominal dual-energy CT with deep learning image reconstruction: a prospective study with five readers.
    Zhong J; Wang L; Shen H; Li J; Lu W; Shi X; Xing Y; Hu Y; Ge X; Ding D; Yan F; Du L; Yao W; Zhang H
    Eur Radiol; 2023 Aug; 33(8):5331-5343. PubMed ID: 36976337
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning reconstruction CT for liver metastases: low-dose dual-energy vs standard-dose single-energy.
    Lyu P; Li Z; Chen Y; Wang H; Liu N; Liu J; Zhan P; Liu X; Shang B; Wang L; Gao J
    Eur Radiol; 2024 Jan; 34(1):28-38. PubMed ID: 37532899
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Assessment of low-dose paranasal sinus CT imaging using a new deep learning image reconstruction technique in children compared to adaptive statistical iterative reconstruction V (ASiR-V).
    Li Y; Liu X; Zhuang XH; Wang MJ; Song XF
    BMC Med Imaging; 2022 Jun; 22(1):106. PubMed ID: 35658908
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Phantom task-based image quality assessment of three generations of rapid kV-switching dual-energy CT systems on virtual monoenergetic images.
    Greffier J; Viry A; Barbotteau Y; Frandon J; Loisy M; de Oliveira F; Beregi JP; Dabli D
    Med Phys; 2022 Apr; 49(4):2233-2244. PubMed ID: 35184293
    [TBL] [Abstract][Full Text] [Related]  

  • 15. A Deep Learning Image Reconstruction Algorithm for Improving Image Quality and Hepatic Lesion Detectability in Abdominal Dual-Energy Computed Tomography: Preliminary Results.
    Chu B; Gan L; Shen Y; Song J; Liu L; Li J; Liu B
    J Digit Imaging; 2023 Dec; 36(6):2347-2355. PubMed ID: 37580484
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Spatial resolution, noise properties, and detectability index of a deep learning reconstruction algorithm for dual-energy CT of the abdomen.
    Thor D; Titternes R; Poludniowski G
    Med Phys; 2023 May; 50(5):2775-2786. PubMed ID: 36774193
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Iterative reconstruction
    Qu T; Guo Y; Li J; Cao L; Li Y; Chen L; Sun J; Lu X; Guo J
    Br J Radiol; 2022 Dec; 95(1140):20220196. PubMed ID: 36341682
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Impact of iterative reconstructions on image quality and detectability of focal liver lesions in low-energy monochromatic images.
    Greffier J; Frandon J; Hamard A; Teissier JM; Pasquier H; Beregi JP; Dabli D
    Phys Med; 2020 Sep; 77():36-42. PubMed ID: 32771702
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Can 1.25 mm thin-section images generated with Deep Learning Image Reconstruction technique replace standard-of-care 5 mm images in abdominal CT?
    Cao J; Mroueh N; Pisuchpen N; Parakh A; Lennartz S; Pierce TT; Kambadakone AR
    Abdom Radiol (NY); 2023 Oct; 48(10):3253-3264. PubMed ID: 37369922
    [TBL] [Abstract][Full Text] [Related]  

  • 20. CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.
    Yang K; Cao J; Pisuchpen N; Kambadakone A; Gupta R; Marschall T; Li X; Liu B
    Eur Radiol; 2023 Apr; 33(4):2439-2449. PubMed ID: 36350391
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 6.