164 related articles for article (PubMed ID: 32044437)
21. Deep learning-regularized, single-step quantitative susceptibility mapping quantification.
Wang Z; Mak HK; Cao P
NMR Biomed; 2023 Mar; 36(3):e4849. PubMed ID: 36259729
[TBL] [Abstract][Full Text] [Related]
22. Spinet-QSM: model-based deep learning with schatten p-norm regularization for improved quantitative susceptibility mapping.
Venkatesh V; Mathew RS; Yalavarthy PK
MAGMA; 2024 Apr; ():. PubMed ID: 38598165
[TBL] [Abstract][Full Text] [Related]
23. Quantitative susceptibility map reconstruction using annihilating filter-based low-rank Hankel matrix approach.
Ahn HS; Park SH; Ye JC
Magn Reson Med; 2020 Mar; 83(3):858-871. PubMed ID: 31468595
[TBL] [Abstract][Full Text] [Related]
24. Plug-and-Play latent feature editing for orientation-adaptive quantitative susceptibility mapping neural networks.
Gao Y; Xiong Z; Shan S; Liu Y; Rong P; Li M; Wilman AH; Pike GB; Liu F; Sun H
Med Image Anal; 2024 May; 94():103160. PubMed ID: 38552528
[TBL] [Abstract][Full Text] [Related]
25. Characterization of quantitative susceptibility mapping in the left ventricular myocardium.
Tyler A; Huang L; Kunze K; Neji R; Mooiweer R; Rogers C; Masci PG; Roujol S
J Cardiovasc Magn Reson; 2024 Summer; 26(1):101000. PubMed ID: 38237902
[TBL] [Abstract][Full Text] [Related]
26. 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]
27. Quantitative susceptibility mapping as a monitoring biomarker in cerebral cavernous malformations with recent hemorrhage.
Zeineddine HA; Girard R; Cao Y; Hobson N; Fam MD; Stadnik A; Tan H; Shen J; Chaudagar K; Shenkar R; Thompson RE; McBee N; Hanley D; Carroll T; Christoforidis GA; Awad IA
J Magn Reson Imaging; 2018 Apr; 47(4):1133-1138. PubMed ID: 28791783
[TBL] [Abstract][Full Text] [Related]
28. BFRnet: A deep learning-based MR background field removal method for QSM of the brain containing significant pathological susceptibility sources.
Zhu X; Gao Y; Liu F; Crozier S; Sun H
Z Med Phys; 2023 Nov; 33(4):578-590. PubMed ID: 36064695
[TBL] [Abstract][Full Text] [Related]
29. msQSM: Morphology-based self-supervised deep learning for quantitative susceptibility mapping.
He J; Peng Y; Fu B; Zhu Y; Wang L; Wang R
Neuroimage; 2023 Jul; 275():120181. PubMed ID: 37220799
[TBL] [Abstract][Full Text] [Related]
30. A robust multi-scale approach to quantitative susceptibility mapping.
Acosta-Cabronero J; Milovic C; Mattern H; Tejos C; Speck O; Callaghan MF
Neuroimage; 2018 Dec; 183():7-24. PubMed ID: 30075277
[TBL] [Abstract][Full Text] [Related]
31. Whole head quantitative susceptibility mapping using a least-norm direct dipole inversion method.
Sun H; Ma Y; MacDonald ME; Pike GB
Neuroimage; 2018 Oct; 179():166-175. PubMed ID: 29906634
[TBL] [Abstract][Full Text] [Related]
32. QSMART: Quantitative susceptibility mapping artifact reduction technique.
Yaghmaie N; Syeda WT; Wu C; Zhang Y; Zhang TD; Burrows EL; Brodtmann A; Moffat BA; Wright DK; Glarin R; Kolbe S; Johnston LA
Neuroimage; 2021 May; 231():117701. PubMed ID: 33484853
[TBL] [Abstract][Full Text] [Related]
33. Unsupervised resolution-agnostic quantitative susceptibility mapping using adaptive instance normalization.
Oh G; Bae H; Ahn HS; Park SH; Moon WJ; Ye JC
Med Image Anal; 2022 Jul; 79():102477. PubMed ID: 35605505
[TBL] [Abstract][Full Text] [Related]
34. DeepQSM - using deep learning to solve the dipole inversion for quantitative susceptibility mapping.
Bollmann S; Rasmussen KGB; Kristensen M; Blendal RG; Østergaard LR; Plocharski M; O'Brien K; Langkammer C; Janke A; Barth M
Neuroimage; 2019 Jul; 195():373-383. PubMed ID: 30935908
[TBL] [Abstract][Full Text] [Related]
35. Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range.
Wei H; Dibb R; Zhou Y; Sun Y; Xu J; Wang N; Liu C
NMR Biomed; 2015 Oct; 28(10):1294-303. PubMed ID: 26313885
[TBL] [Abstract][Full Text] [Related]
36. A subject-specific unsupervised deep learning method for quantitative susceptibility mapping using implicit neural representation.
Zhang M; Feng R; Li Z; Feng J; Wu Q; Zhang Z; Ma C; Wu J; Yan F; Liu C; Zhang Y; Wei H
Med Image Anal; 2024 Jul; 95():103173. PubMed ID: 38657424
[TBL] [Abstract][Full Text] [Related]
37. Quantitative Susceptibility Mapping of Intracerebral Hemorrhages at Various Stages.
Chang S; Zhang J; Liu T; Tsiouris AJ; Shou J; Nguyen T; Leifer D; Wang Y; Kovanlikaya I
J Magn Reson Imaging; 2016 Aug; 44(2):420-5. PubMed ID: 26718014
[TBL] [Abstract][Full Text] [Related]
38. QSMGAN: Improved Quantitative Susceptibility Mapping using 3D Generative Adversarial Networks with increased receptive field.
Chen Y; Jakary A; Avadiappan S; Hess CP; Lupo JM
Neuroimage; 2020 Feb; 207():116389. PubMed ID: 31760151
[TBL] [Abstract][Full Text] [Related]
39. Quantitative Susceptibility Mapping Using the Multiple Dipole-inversion Combination with k-space Segmentation Method.
Sato R; Shirai T; Taniguchi Y; Murase T; Bito Y; Ochi H
Magn Reson Med Sci; 2017 Oct; 16(4):340-350. PubMed ID: 28367904
[TBL] [Abstract][Full Text] [Related]
40. A method for estimating and removing streaking artifacts in quantitative susceptibility mapping.
Li W; Wang N; Yu F; Han H; Cao W; Romero R; Tantiwongkosi B; Duong TQ; Liu C
Neuroimage; 2015 Mar; 108():111-22. PubMed ID: 25536496
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]