103 related articles for article (PubMed ID: 32013069)
1. Validation of Deep Learning-Based Artifact Correction on Synthetic FLAIR Images in a Different Scanning Environment.
Ryu KH; Baek HJ; Gho SM; Ryu K; Kim DH; Park SE; Ha JY; Cho SB; Lee JS
J Clin Med; 2020 Jan; 9(2):. PubMed ID: 32013069
[TBL] [Abstract][Full Text] [Related]
2. Data-driven synthetic MRI FLAIR artifact correction via deep neural network.
Ryu K; Nam Y; Gho SM; Jang J; Lee HJ; Cha J; Baek HJ; Park J; Kim DH
J Magn Reson Imaging; 2019 Nov; 50(5):1413-1423. PubMed ID: 30884007
[TBL] [Abstract][Full Text] [Related]
3. Improving the Quality of Synthetic FLAIR Images with Deep Learning Using a Conditional Generative Adversarial Network for Pixel-by-Pixel Image Translation.
Hagiwara A; Otsuka Y; Hori M; Tachibana Y; Yokoyama K; Fujita S; Andica C; Kamagata K; Irie R; Koshino S; Maekawa T; Chougar L; Wada A; Takemura MY; Hattori N; Aoki S
AJNR Am J Neuroradiol; 2019 Feb; 40(2):224-230. PubMed ID: 30630834
[TBL] [Abstract][Full Text] [Related]
4. Image quality at synthetic brain magnetic resonance imaging in children.
Lee SM; Choi YH; Cheon JE; Kim IO; Cho SH; Kim WH; Kim HJ; Cho HH; You SK; Park SH; Hwang MJ
Pediatr Radiol; 2017 Nov; 47(12):1638-1647. PubMed ID: 28638982
[TBL] [Abstract][Full Text] [Related]
5. Generation of quantification maps and weighted images from synthetic magnetic resonance imaging using deep learning network.
Liu Y; Niu H; Ren P; Ren J; Wei X; Liu W; Ding H; Li J; Xia J; Zhang T; Lv H; Yin H; Wang Z
Phys Med Biol; 2022 Jan; 67(2):. PubMed ID: 34965516
[No Abstract] [Full Text] [Related]
6. T1-weighted fluid-attenuated inversion recovery and T1-weighted fast spin-echo contrast-enhanced imaging: a comparison in 20 patients with brain lesions.
Al-Saeed O; Ismail M; Athyal RP; Rudwan M; Khafajee S
J Med Imaging Radiat Oncol; 2009 Aug; 53(4):366-72. PubMed ID: 19695043
[TBL] [Abstract][Full Text] [Related]
7. The use of combined T
Fujiwara Y; Inoue Y; Kanamoto M; Ishida S; Adachi T; Kimura H
Radiol Phys Technol; 2019 Mar; 12(1):118-125. PubMed ID: 30666614
[TBL] [Abstract][Full Text] [Related]
8. Uncertainty-Aware and Lesion-Specific Image Synthesis in Multiple Sclerosis Magnetic Resonance Imaging: A Multicentric Validation Study.
Finck T; Li H; Schlaeger S; Grundl L; Sollmann N; Bender B; Bürkle E; Zimmer C; Kirschke J; Menze B; Mühlau M; Wiestler B
Front Neurosci; 2022; 16():889808. PubMed ID: 35557607
[TBL] [Abstract][Full Text] [Related]
9. Feasibility of Brain Imaging Using a Digital Surround Technology Body Coil: A Study Based on SRGAN-VGG Convolutional Neural Networks
Liu YW; Niu HJ; Yin HX; Xia JJ; Ren PL; Zhang TT; Li J; Lv H; Ding HY; Ren JL; Wang ZC
Annu Int Conf IEEE Eng Med Biol Soc; 2021 Nov; 2021():3734-3737. PubMed ID: 34892048
[TBL] [Abstract][Full Text] [Related]
10. Patient-specific 3D FLAIR for enhanced visualization of brain white matter lesions in multiple sclerosis.
Gabr RE; Pednekar AS; Govindarajan KA; Sun X; Riascos RF; Ramírez MG; Hasan KM; Lincoln JA; Nelson F; Wolinsky JS; Narayana PA
J Magn Reson Imaging; 2017 Aug; 46(2):557-564. PubMed ID: 27869333
[TBL] [Abstract][Full Text] [Related]
11. Deep learning enables reduced gadolinium dose for contrast-enhanced brain MRI.
Gong E; Pauly JM; Wintermark M; Zaharchuk G
J Magn Reson Imaging; 2018 Aug; 48(2):330-340. PubMed ID: 29437269
[TBL] [Abstract][Full Text] [Related]
12. Fluid-attenuated inversion recovery MRI synthesis from multisequence MRI using three-dimensional fully convolutional networks for multiple sclerosis.
Wei W; Poirion E; Bodini B; Durrleman S; Colliot O; Stankoff B; Ayache N
J Med Imaging (Bellingham); 2019 Jan; 6(1):014005. PubMed ID: 30820439
[TBL] [Abstract][Full Text] [Related]
13. KIKI-net: cross-domain convolutional neural networks for reconstructing undersampled magnetic resonance images.
Eo T; Jun Y; Kim T; Jang J; Lee HJ; Hwang D
Magn Reson Med; 2018 Nov; 80(5):2188-2201. PubMed ID: 29624729
[TBL] [Abstract][Full Text] [Related]
14. Optimal combination of FLAIR and T2-weighted MRI for improved lesion contrast in multiple sclerosis.
Gabr RE; Hasan KM; Haque ME; Nelson FM; Wolinsky JS; Narayana PA
J Magn Reson Imaging; 2016 Nov; 44(5):1293-1300. PubMed ID: 27126898
[TBL] [Abstract][Full Text] [Related]
15. Comparison of contrast-enhanced T1-weighted FLAIR with BLADE, and spin-echo T1-weighted sequences in intracranial MRI.
Alkan O; Kizilkiliç O; Yildirim T; Alibek S
Diagn Interv Radiol; 2009 Jun; 15(2):75-80. PubMed ID: 19517375
[TBL] [Abstract][Full Text] [Related]
16. Artifact correction in low-dose dental CT imaging using Wasserstein generative adversarial networks.
Hu Z; Jiang C; Sun F; Zhang Q; Ge Y; Yang Y; Liu X; Zheng H; Liang D
Med Phys; 2019 Apr; 46(4):1686-1696. PubMed ID: 30697765
[TBL] [Abstract][Full Text] [Related]
17. A deep learning- and partial least square regression-based model observer for a low-contrast lesion detection task in CT.
Gong H; Yu L; Leng S; Dilger SK; Ren L; Zhou W; Fletcher JG; McCollough CH
Med Phys; 2019 May; 46(5):2052-2063. PubMed ID: 30889282
[TBL] [Abstract][Full Text] [Related]
18. MRI Gibbs-ringing artifact reduction by means of machine learning using convolutional neural networks.
Zhang Q; Ruan G; Yang W; Liu Y; Zhao K; Feng Q; Chen W; Wu EX; Feng Y
Magn Reson Med; 2019 Dec; 82(6):2133-2145. PubMed ID: 31373061
[TBL] [Abstract][Full Text] [Related]
19. Contributions of an adiabatic initial inversion pulse and K-space re-ordered by inversion-time at each slice position (KRISP) to control of CSF artifacts and visualization of the brain in FLAIR magnetic resonance imaging.
Curati WL; Oatridge A; Herlihy AH; Hajnal JV; Puri BK; Bydder GM
Clin Radiol; 2001 May; 56(5):375-84. PubMed ID: 11384135
[TBL] [Abstract][Full Text] [Related]
20. MR-based synthetic CT generation using a deep convolutional neural network method.
Han X
Med Phys; 2017 Apr; 44(4):1408-1419. PubMed ID: 28192624
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
