215 related articles for article (PubMed ID: 35905811)
1. 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]
2. 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]
3. 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]
4. Human brain atlas for automated region of interest selection in quantitative susceptibility mapping: application to determine iron content in deep gray matter structures.
Lim IA; Faria AV; Li X; Hsu JT; Airan RD; Mori S; van Zijl PC
Neuroimage; 2013 Nov; 82():449-69. PubMed ID: 23769915
[TBL] [Abstract][Full Text] [Related]
5. 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]
6. 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]
7. 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]
8. 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]
9. Single-step calculation of susceptibility through multiple orientation sampling.
Chen L; Cai S; van Zijl PCM; Li X
NMR Biomed; 2021 Jul; 34(7):e4517. PubMed ID: 33822416
[TBL] [Abstract][Full Text] [Related]
10. A modulated closed form solution for quantitative susceptibility mapping--a thorough evaluation and comparison to iterative methods based on edge prior knowledge.
Khabipova D; Wiaux Y; Gruetter R; Marques JP
Neuroimage; 2015 Feb; 107():163-174. PubMed ID: 25463463
[TBL] [Abstract][Full Text] [Related]
11. 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]
12. Learning-based single-step quantitative susceptibility mapping reconstruction without brain extraction.
Wei H; Cao S; Zhang Y; Guan X; Yan F; Yeom KW; Liu C
Neuroimage; 2019 Nov; 202():116064. PubMed ID: 31377323
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. 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]
15. A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).
Jo M; Oh SH
Med Phys; 2020 Mar; 47(3):1151-1160. PubMed ID: 31883389
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Multi-atlas tool for automated segmentation of brain gray matter nuclei and quantification of their magnetic susceptibility.
Li X; Chen L; Kutten K; Ceritoglu C; Li Y; Kang N; Hsu JT; Qiao Y; Wei H; Liu C; Miller MI; Mori S; Yousem DM; van Zijl PCM; Faria AV
Neuroimage; 2019 May; 191():337-349. PubMed ID: 30738207
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Multi-Echo Quantitative Susceptibility Mapping for Strategically Acquired Gradient Echo (STAGE) Imaging.
Gharabaghi S; Liu S; Wang Y; Chen Y; Buch S; Jokar M; Wischgoll T; Kashou NH; Zhang C; Wu B; Cheng J; Haacke EM
Front Neurosci; 2020; 14():581474. PubMed ID: 33192267
[TBL] [Abstract][Full Text] [Related]
20. Deep learning-based quantitative susceptibility mapping (QSM) in the presence of fat using synthetically generated multi-echo phase training data.
Hanspach J; Bollmann S; Grigo J; Karius A; Uder M; Laun FB
Magn Reson Med; 2022 Oct; 88(4):1548-1560. PubMed ID: 35713187
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]