These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

141 related articles for article (PubMed ID: 38411690)

  • 1. Diagnostic performance and image quality of an image-based denoising algorithm applied to radiation dose-reduced CT in diagnosing acute appendicitis.
    Choi HU; Cho J; Hwang J; Lee S; Chang W; Park JH; Lee KH
    Abdom Radiol (NY); 2024 Jun; 49(6):1839-1849. PubMed ID: 38411690
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Effect of a novel denoising technique on image quality and diagnostic accuracy in low-dose CT in patients with suspected appendicitis.
    Kolb M; Storz C; Kim JH; Weiss J; Afat S; Nikolaou K; Bamberg F; Othman AE
    Eur J Radiol; 2019 Jul; 116():198-204. PubMed ID: 31153565
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Comparison of filtered back projection and iterative reconstruction in diagnosing appendicitis at 2-mSv CT.
    Park JH; Kim B; Kim MS; Kim HJ; Ko Y; Ahn S; Karul M; Fletcher JG; Lee KH
    Abdom Radiol (NY); 2016 Jul; 41(7):1227-36. PubMed ID: 27315093
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Combination of Deep Learning-Based Denoising and Iterative Reconstruction for Ultra-Low-Dose CT of the Chest: Image Quality and Lung-RADS Evaluation.
    Hata A; Yanagawa M; Yoshida Y; Miyata T; Tsubamoto M; Honda O; Tomiyama N
    AJR Am J Roentgenol; 2020 Dec; 215(6):1321-1328. PubMed ID: 33052702
    [No Abstract]   [Full Text] [Related]  

  • 5. Cycle-Consistent Generative Adversarial Network: Effect on Radiation Dose Reduction and Image Quality Improvement in Ultralow-Dose CT for Evaluation of Pulmonary Tuberculosis.
    Yan C; Lin J; Li H; Xu J; Zhang T; Chen H; Woodruff HC; Wu G; Zhang S; Xu Y; Lambin P
    Korean J Radiol; 2021 Jun; 22(6):983-993. PubMed ID: 33739634
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Value of deep learning reconstruction at ultra-low-dose CT for evaluation of urolithiasis.
    Zhang G; Zhang X; Xu L; Bai X; Jin R; Xu M; Yan J; Jin Z; Sun H
    Eur Radiol; 2022 Sep; 32(9):5954-5963. PubMed ID: 35357541
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Lung-Optimized Deep-Learning-Based Reconstruction for Ultralow-Dose CT.
    Goto M; Nagayama Y; Sakabe D; Emoto T; Kidoh M; Oda S; Nakaura T; Taguchi N; Funama Y; Takada S; Uchimura R; Hayashi H; Hatemura M; Kawanaka K; Hirai T
    Acad Radiol; 2023 Mar; 30(3):431-440. PubMed ID: 35738988
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Ultra-low-dose hepatic multiphase CT using deep learning-based image reconstruction algorithm focused on arterial phase in chronic liver disease: A non-inferiority study.
    Lee HJ; Kim JS; Lee JK; Lee HA; Pak S
    Eur J Radiol; 2023 Feb; 159():110659. PubMed ID: 36584563
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality.
    Yan C; Liang C; Xu J; Wu Y; Xiong W; Zheng H; Xu Y
    Eur Radiol; 2019 Oct; 29(10):5358-5366. PubMed ID: 30927099
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Image quality assessment of pediatric chest and abdomen CT by deep learning reconstruction.
    Yoon H; Kim J; Lim HJ; Lee MJ
    BMC Med Imaging; 2021 Oct; 21(1):146. PubMed ID: 34629049
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Low-dose CT for patients with clinically suspected acute appendicitis: optimal strength of sinogram affirmed iterative reconstruction for image quality and diagnostic performance.
    Kim SH; Yoon JH; Lee JH; Lim YJ; Kim OH; Ryu JH; Son JH
    Acta Radiol; 2015 Aug; 56(8):899-907. PubMed ID: 25118330
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Deep learning reconstruction for contrast-enhanced CT of the upper abdomen: similar image quality with lower radiation dose in direct comparison with iterative reconstruction.
    Nam JG; Hong JH; Kim DS; Oh J; Goo JM
    Eur Radiol; 2021 Aug; 31(8):5533-5543. PubMed ID: 33555354
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Deep-learning denoising minimizes radiation exposure in neck CT beyond the limits of conventional reconstruction.
    Plajer D; Hahn M; Chaika M; Mader M; Mueck J; Nikolaou K; Afat S; Brendlin AS
    Eur J Radiol; 2024 Sep; 178():111523. PubMed ID: 39013270
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Potential value of the PixelShine deep learning algorithm for increasing quality of 70 kVp+ASiR-V reconstruction pelvic arterial phase CT images.
    Tian SF; Liu AL; Liu JH; Liu YJ; Pan JD
    Jpn J Radiol; 2019 Feb; 37(2):186-190. PubMed ID: 30523499
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Evaluating Lung Changes in Long COVID: Ultra-Low-Dose vs. Standard-Dose CT Chest.
    Devkota S; Garg M; Debi U; Dhooria S; Dua A; Prabhakar N; Soni S; Maralakunte M; Gulati A; Singh T; Sandhu MS
    Br J Biomed Sci; 2024; 81():13385. PubMed ID: 39319349
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Model-based iterative reconstruction technique for ultralow-dose computed tomography of the lung: a pilot study.
    Yamada Y; Jinzaki M; Tanami Y; Shiomi E; Sugiura H; Abe T; Kuribayashi S
    Invest Radiol; 2012 Aug; 47(8):482-9. PubMed ID: 22766910
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Detection of urinary tract stones on submillisievert abdominopelvic CT imaging with deep-learning image reconstruction algorithm (DLIR).
    Prod'homme S; Bouzerar R; Forzini T; Delabie A; Renard C
    Abdom Radiol (NY); 2024 Jun; 49(6):1987-1995. PubMed ID: 38470506
    [TBL] [Abstract][Full Text] [Related]  

  • 18. A deep-learning method for the denoising of ultra-low dose chest CT in coronary artery calcium score evaluation.
    Klug M; Shemesh J; Green M; Mayer A; Kerpel A; Konen E; Marom EM
    Clin Radiol; 2022 Jul; 77(7):e509-e517. PubMed ID: 35414431
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Lung nodule detection performance in five observers on computed tomography (CT) with adaptive iterative dose reduction using three-dimensional processing (AIDR 3D) in a Japanese multicenter study: Comparison between ultra-low-dose CT and low-dose CT by receiver-operating characteristic analysis.
    Nagatani Y; Takahashi M; Murata K; Ikeda M; Yamashiro T; Miyara T; Koyama H; Koyama M; Sato Y; Moriya H; Noma S; Tomiyama N; Ohno Y; Murayama S;
    Eur J Radiol; 2015 Jul; 84(7):1401-12. PubMed ID: 25892051
    [TBL] [Abstract][Full Text] [Related]  

  • 20. Low-Dose (2-mSv) CT in Adolescents and Young Adults With Suspected Appendicitis: Advantages of Additional Review of Thin Sections Using Multiplanar Sliding-Slab Averaging Technique.
    Lee YJ; Kim B; Ko Y; Cho KE; Hong SS; Kim DH; Song H; Lee KH
    AJR Am J Roentgenol; 2015 Nov; 205(5):W485-91. PubMed ID: 26496570
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.