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 *

183 related articles for article (PubMed ID: 36647338)

  • 61. Low-Dose CT Image Synthesis for Domain Adaptation Imaging Using a Generative Adversarial Network With Noise Encoding Transfer Learning.
    Li M; Wang J; Chen Y; Tang Y; Wu Z; Qi Y; Jiang H; Zheng J; Tsui BMW
    IEEE Trans Med Imaging; 2023 Sep; 42(9):2616-2630. PubMed ID: 37030685
    [TBL] [Abstract][Full Text] [Related]  

  • 62. RESEARCH PROGRESS OF DEEP LEARNING IN LOW-DOSE CT IMAGE DENOISING.
    Zhang F; Liu J; Liu Y; Zhang X
    Radiat Prot Dosimetry; 2023 Mar; 199(4):337-346. PubMed ID: 36588464
    [TBL] [Abstract][Full Text] [Related]  

  • 63. CT energy weighting in the presence of scatter and limited energy resolution.
    Schmidt TG
    Med Phys; 2010 Mar; 37(3):1056-67. PubMed ID: 20384241
    [TBL] [Abstract][Full Text] [Related]  

  • 64. A platform-independent method to reduce CT truncation artifacts using discriminative dictionary representations.
    Chen Y; Budde A; Li K; Li Y; Hsieh J; Chen GH
    Med Phys; 2017 Jan; 44(1):121-131. PubMed ID: 28102942
    [TBL] [Abstract][Full Text] [Related]  

  • 65. Artifact-Assisted multi-level and multi-scale feature fusion attention network for low-dose CT denoising.
    Cui X; Guo Y; Zhang X; Shangguan H; Liu B; Wang A
    J Xray Sci Technol; 2022; 30(5):875-889. PubMed ID: 35694948
    [TBL] [Abstract][Full Text] [Related]  

  • 66. [Sinogram interpolation combined with unsupervised image-to-image translation network for CT metal artifact correction].
    Yu J; Zhang K; Jin S; Su Z; Xu X; Zhang H
    Nan Fang Yi Ke Da Xue Xue Bao; 2023 Jul; 43(7):1214-1223. PubMed ID: 37488804
    [TBL] [Abstract][Full Text] [Related]  

  • 67. An experimental method to correct low-frequency concentric artifacts in photon counting CT.
    Feng M; Ji X; Zhang R; Treb K; Dingle AM; Li K
    Phys Med Biol; 2021 Aug; 66(17):. PubMed ID: 34315142
    [TBL] [Abstract][Full Text] [Related]  

  • 68. Total-body low-dose CT image denoising using a prior knowledge transfer technique with a contrastive regularization mechanism.
    Fu M; Duan Y; Cheng Z; Qin W; Wang Y; Liang D; Hu Z
    Med Phys; 2023 May; 50(5):2971-2984. PubMed ID: 36542423
    [TBL] [Abstract][Full Text] [Related]  

  • 69. Deep learning method for reducing metal artifacts in dental cone-beam CT using supplementary information from intra-oral scan.
    Hyun CM; Bayaraa T; Yun HS; Jang TJ; Park HS; Seo JK
    Phys Med Biol; 2022 Aug; 67(17):. PubMed ID: 35944531
    [No Abstract]   [Full Text] [Related]  

  • 70. Deep Sinogram Completion With Image Prior for Metal Artifact Reduction in CT Images.
    Yu L; Zhang Z; Li X; Xing L
    IEEE Trans Med Imaging; 2021 Jan; 40(1):228-238. PubMed ID: 32956044
    [TBL] [Abstract][Full Text] [Related]  

  • 71. A metal artifact reduction method for a dental CT based on adaptive local thresholding and prior image generation.
    Hegazy MA; Cho MH; Lee SY
    Biomed Eng Online; 2016 Nov; 15(1):119. PubMed ID: 27814775
    [TBL] [Abstract][Full Text] [Related]  

  • 72. Removing ring artifacts in cone-beam CT via TV-Stokes and unidirectional total variation model.
    Wu H; Li J; Wang H
    Med Phys; 2019 Apr; 46(4):1719-1727. PubMed ID: 30723939
    [TBL] [Abstract][Full Text] [Related]  

  • 73. [CT ring artifact reduction using an improved wavelet filtering in the sinogram domain].
    Guo H; Zeng D; Zhang H; Huang J; Zhang J; Ma J
    Nan Fang Yi Ke Da Xue Xue Bao; 2015 Aug; 35(9):1258-62. PubMed ID: 26403734
    [TBL] [Abstract][Full Text] [Related]  

  • 74. Comparison of ring artifact correction methods for flat-detector CT.
    Prell D; Kyriakou Y; Kalender WA
    Phys Med Biol; 2009 Jun; 54(12):3881-95. PubMed ID: 19491452
    [TBL] [Abstract][Full Text] [Related]  

  • 75. 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]  

  • 76. 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]  

  • 77. Inter-plane artifact suppression in tomosynthesis using 3D CT image data.
    Kim JG; Jin SO; Cho MH; Lee SY
    Biomed Eng Online; 2011 Dec; 10():106. PubMed ID: 22151538
    [TBL] [Abstract][Full Text] [Related]  

  • 78. Metal artifact reduction in 2D CT images with self-supervised cross-domain learning.
    Yu L; Zhang Z; Li X; Ren H; Zhao W; Xing L
    Phys Med Biol; 2021 Aug; 66(17):. PubMed ID: 34330119
    [TBL] [Abstract][Full Text] [Related]  

  • 79. Synchrotron microtomography image restoration via regularization representation and deep CNN prior.
    Li Y; Han S; Zhao Y; Li F; Ji D; Zhao X; Liu D; Jian J; Hu C
    Comput Methods Programs Biomed; 2022 Nov; 226():107181. PubMed ID: 36257200
    [TBL] [Abstract][Full Text] [Related]  

  • 80. Single-pass metal artifact reduction using a dual-layer flat panel detector.
    Shi L; Bennett NR; Shiroma A; Sun M; Zhang J; Colbeth R; Star-Lack J; Lu M; Wang AS
    Med Phys; 2021 Oct; 48(10):6482-6496. PubMed ID: 34374461
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 10.