212 related articles for article (PubMed ID: 36588464)
41. Deep-learning-based denoising of X-ray differential phase and dark-field images.
Ren K; Gu Y; Luo M; Chen H; Wang Z
Eur J Radiol; 2023 Jun; 163():110835. PubMed ID: 37098281
[TBL] [Abstract][Full Text] [Related]
42. X-ray CT image denoising with MINF: A modularized iterative network framework for data from multiple dose levels.
Du Q; Tang Y; Wang J; Hou X; Wu Z; Li M; Yang X; Zheng J
Comput Biol Med; 2023 Jan; 152():106419. PubMed ID: 36527781
[TBL] [Abstract][Full Text] [Related]
43. Projection space denoising with bilateral filtering and CT noise modeling for dose reduction in CT.
Manduca A; Yu L; Trzasko JD; Khaylova N; Kofler JM; McCollough CM; Fletcher JG
Med Phys; 2009 Nov; 36(11):4911-9. PubMed ID: 19994500
[TBL] [Abstract][Full Text] [Related]
44. Self-adaption and texture generation: A hybrid loss function for low-dose CT denoising.
Wang Z; Liu M; Cheng X; Zhu J; Wang X; Gong H; Liu M; Xu L
J Appl Clin Med Phys; 2023 Sep; 24(9):e14113. PubMed ID: 37571834
[TBL] [Abstract][Full Text] [Related]
45. Degradation Adaption Local-to-Global Transformer for Low-Dose CT Image Denoising.
Wang H; Chi J; Wu C; Yu X; Wu H
J Digit Imaging; 2023 Aug; 36(4):1894-1909. PubMed ID: 37118101
[TBL] [Abstract][Full Text] [Related]
46. An investigation of quantitative accuracy for deep learning based denoising in oncological PET.
Lu W; Onofrey JA; Lu Y; Shi L; Ma T; Liu Y; Liu C
Phys Med Biol; 2019 Aug; 64(16):165019. PubMed ID: 31307019
[TBL] [Abstract][Full Text] [Related]
47. Low-dose CT denoising using a Progressive Wasserstein generative adversarial network.
Wang G; Hu X
Comput Biol Med; 2021 Aug; 135():104625. PubMed ID: 34246157
[TBL] [Abstract][Full Text] [Related]
48. Feasibility of Deep Learning-Based Noise and Artifact Reduction in Coronal Reformation of Contrast-Enhanced Chest Computed Tomography.
Kang EJ; Park HS; Jeon K; Lee JW; Lim JK
J Comput Assist Tomogr; 2022 Jul-Aug 01; 46(4):593-603. PubMed ID: 35617647
[TBL] [Abstract][Full Text] [Related]
49. A self-supervised guided knowledge distillation framework for unpaired low-dose CT image denoising.
Wang J; Tang Y; Wu Z; Du Q; Yao L; Yang X; Li M; Zheng J
Comput Med Imaging Graph; 2023 Jul; 107():102237. PubMed ID: 37116340
[TBL] [Abstract][Full Text] [Related]
50. Denoising of polychromatic CT images based on their own noise properties.
Kim JH; Chang Y; Ra JB
Med Phys; 2016 May; 43(5):2251. PubMed ID: 27147337
[TBL] [Abstract][Full Text] [Related]
51. 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]
52. Noise Conscious Training of Non Local Neural Network Powered by Self Attentive Spectral Normalized Markovian Patch GAN for Low Dose CT Denoising.
Bera S; Biswas PK
IEEE Trans Med Imaging; 2021 Dec; 40(12):3663-3673. PubMed ID: 34224348
[TBL] [Abstract][Full Text] [Related]
53. Detector shifting and deep learning based ring artifact correction method for low-dose CT.
Liu Y; Wei C; Xu Q
Med Phys; 2023 Jul; 50(7):4308-4324. PubMed ID: 36647338
[TBL] [Abstract][Full Text] [Related]
54. Image quality of ultralow-dose chest CT using deep learning techniques: potential superiority of vendor-agnostic post-processing over vendor-specific techniques.
Nam JG; Ahn C; Choi H; Hong W; Park J; Kim JH; Goo JM
Eur Radiol; 2021 Jul; 31(7):5139-5147. PubMed ID: 33415436
[TBL] [Abstract][Full Text] [Related]
55. 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]
56. Noise reduction to decrease radiation dose and improve conspicuity of hepatic lesions at contrast-enhanced 80-kV hepatic CT using projection space denoising.
Ehman EC; Guimarães LS; Fidler JL; Takahashi N; Ramirez-Giraldo JC; Yu L; Manduca A; Huprich JE; McCollough CH; Holmes D; Harmsen WS; Fletcher JG
AJR Am J Roentgenol; 2012 Feb; 198(2):405-11. PubMed ID: 22268185
[TBL] [Abstract][Full Text] [Related]
57. The use of deep learning towards dose optimization in low-dose computed tomography: A scoping review.
Immonen E; Wong J; Nieminen M; Kekkonen L; Roine S; Törnroos S; Lanca L; Guan F; Metsälä E
Radiography (Lond); 2022 Feb; 28(1):208-214. PubMed ID: 34325998
[TBL] [Abstract][Full Text] [Related]
58. 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]
59. A novel denoising method for CT images based on U-net and multi-attention.
Zhang J; Niu Y; Shangguan Z; Gong W; Cheng Y
Comput Biol Med; 2023 Jan; 152():106387. PubMed ID: 36495750
[TBL] [Abstract][Full Text] [Related]
60. End-to-end deep learning for interior tomography with low-dose x-ray CT.
Han Y; Wu D; Kim K; Li Q
Phys Med Biol; 2022 May; 67(11):. PubMed ID: 35390782
[No Abstract] [Full Text] [Related]
[Previous] [Next] [New Search]