133 related articles for article (PubMed ID: 38552528)
1. 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]
2. Instant tissue field and magnetic susceptibility mapping from MRI raw phase using Laplacian enhanced deep neural networks.
Gao Y; Xiong Z; Fazlollahi A; Nestor PJ; Vegh V; Nasrallah F; Winter C; Pike GB; Crozier S; Liu F; Sun H
Neuroimage; 2022 Oct; 259():119410. PubMed ID: 35753595
[TBL] [Abstract][Full Text] [Related]
3. Comparison of quantitative susceptibility mapping methods for iron-sensitive susceptibility imaging at 7T: An evaluation in healthy subjects and patients with Huntington's disease.
Yao J; Morrison MA; Jakary A; Avadiappan S; Chen Y; Luitjens J; Glueck J; Driscoll T; Geschwind MD; Nelson AB; Villanueva-Meyer JE; Hess CP; Lupo JM
Neuroimage; 2023 Jan; 265():119788. PubMed ID: 36476567
[TBL] [Abstract][Full Text] [Related]
4. Towards in vivo ground truth susceptibility for single-orientation deep learning QSM: A multi-orientation gradient-echo MRI dataset.
Shi Y; Feng R; Li Z; Zhuang J; Zhang Y; Wei H
Neuroimage; 2022 Nov; 261():119522. PubMed ID: 35905811
[TBL] [Abstract][Full Text] [Related]
5. Deep grey matter quantitative susceptibility mapping from small spatial coverages using deep learning.
Zhu X; Gao Y; Liu F; Crozier S; Sun H
Z Med Phys; 2022 May; 32(2):188-198. PubMed ID: 34312047
[TBL] [Abstract][Full Text] [Related]
6. Affine transformation edited and refined deep neural network for quantitative susceptibility mapping.
Xiong Z; Gao Y; Liu F; Sun H
Neuroimage; 2023 Feb; 267():119842. PubMed ID: 36586542
[TBL] [Abstract][Full Text] [Related]
7. Quantitative susceptibility mapping using deep neural network: QSMnet.
Yoon J; Gong E; Chatnuntawech I; Bilgic B; Lee J; Jung W; Ko J; Jung H; Setsompop K; Zaharchuk G; Kim EY; Pauly J; Lee J
Neuroimage; 2018 Oct; 179():199-206. PubMed ID: 29894829
[TBL] [Abstract][Full Text] [Related]
8. MoDL-QSM: Model-based deep learning for quantitative susceptibility mapping.
Feng R; Zhao J; Wang H; Yang B; Feng J; Shi Y; Zhang M; Liu C; Zhang Y; Zhuang J; Wei H
Neuroimage; 2021 Oct; 240():118376. PubMed ID: 34246768
[TBL] [Abstract][Full Text] [Related]
9. Accelerating quantitative susceptibility and R2* mapping using incoherent undersampling and deep neural network reconstruction.
Gao Y; Cloos M; Liu F; Crozier S; Pike GB; Sun H
Neuroimage; 2021 Oct; 240():118404. PubMed ID: 34280526
[TBL] [Abstract][Full Text] [Related]
10. Comparison of quantitative susceptibility mapping methods on evaluating radiation-induced cerebral microbleeds and basal ganglia at 3T and 7T.
Chen Y; Genc O; Poynton CB; Banerjee S; Hess CP; Lupo JM
NMR Biomed; 2022 May; 35(5):e4666. PubMed ID: 35075701
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. xQSM: quantitative susceptibility mapping with octave convolutional and noise-regularized neural networks.
Gao Y; Zhu X; Moffat BA; Glarin R; Wilman AH; Pike GB; Crozier S; Liu F; Sun H
NMR Biomed; 2021 Mar; 34(3):e4461. PubMed ID: 33368705
[TBL] [Abstract][Full Text] [Related]
13. Multi-echo dipole inversion for magnetic susceptibility mapping.
Kames C; Doucette J; Rauscher A
Magn Reson Med; 2023 Jun; 89(6):2391-2401. PubMed ID: 36695283
[TBL] [Abstract][Full Text] [Related]
14. 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]
15. 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]
16. Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs.
Si W; Guo Y; Zhang Q; Zhang J; Wang Y; Feng Y
Front Neurosci; 2023; 17():1165446. PubMed ID: 37383103
[TBL] [Abstract][Full Text] [Related]
17. Overview of quantitative susceptibility mapping using deep learning: Current status, challenges and opportunities.
Jung W; Bollmann S; Lee J
NMR Biomed; 2022 Apr; 35(4):e4292. PubMed ID: 32207195
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Quantitative susceptibility mapping through model-based deep image prior (MoDIP).
Xiong Z; Gao Y; Liu Y; Fazlollahi A; Nestor P; Liu F; Sun H
Neuroimage; 2024 May; 291():120583. PubMed ID: 38554781
[TBL] [Abstract][Full Text] [Related]
20. Chaos and COSMOS-Considerations on QSM methods with multiple and single orientations and effects from local anisotropy.
Gkotsoulias DG; Jäger C; Müller R; Gräßle T; Olofsson KM; Møller T; Unwin S; Crockford C; Wittig RM; Bilgic B; Möller HE
Magn Reson Imaging; 2024 Jul; 110():104-111. PubMed ID: 38631534
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]