203 related articles for article (PubMed ID: 23261654)
1. The impact of denoising on independent component analysis of functional magnetic resonance imaging data.
Pignat JM; Koval O; Van De Ville D; Voloshynovskiy S; Michel C; Pun T
J Neurosci Methods; 2013 Feb; 213(1):105-22. PubMed ID: 23261654
[TBL] [Abstract][Full Text] [Related]
2. Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing.
Wink AM; Roerdink JB
IEEE Trans Med Imaging; 2004 Mar; 23(3):374-87. PubMed ID: 15027530
[TBL] [Abstract][Full Text] [Related]
3. Iterative approach of dual regression with a sparse prior enhances the performance of independent component analysis for group functional magnetic resonance imaging (fMRI) data.
Kim YH; Kim J; Lee JH
Neuroimage; 2012 Dec; 63(4):1864-89. PubMed ID: 22939873
[TBL] [Abstract][Full Text] [Related]
4. Wavelet-domain TI Wiener-like filtering for complex MR data denoising.
Hu K; Cheng Q; Gao X
Magn Reson Imaging; 2016 Oct; 34(8):1128-40. PubMed ID: 27238055
[TBL] [Abstract][Full Text] [Related]
5. Evaluation and optimization of fMRI single-subject processing pipelines with NPAIRS and second-level CVA.
Zhang J; Anderson JR; Liang L; Pulapura SK; Gatewood L; Rottenberg DA; Strother SC
Magn Reson Imaging; 2009 Feb; 27(2):264-78. PubMed ID: 18849131
[TBL] [Abstract][Full Text] [Related]
6. Analyzing fMRI experiments with structural adaptive smoothing procedures.
Tabelow K; Polzehl J; Voss HU; Spokoiny V
Neuroimage; 2006 Oct; 33(1):55-62. PubMed ID: 16891126
[TBL] [Abstract][Full Text] [Related]
7. Discussion on the choice of separated components in fMRI data analysis by spatial independent component analysis.
Chen H; Yao D
Magn Reson Imaging; 2004 Jul; 22(6):827-33. PubMed ID: 15234451
[TBL] [Abstract][Full Text] [Related]
8. Gaussian mixture model-based noise reduction in resting state fMRI data.
Garg G; Prasad G; Coyle D
J Neurosci Methods; 2013 Apr; 215(1):71-7. PubMed ID: 23499197
[TBL] [Abstract][Full Text] [Related]
9. WASICA: An effective wavelet-shrinkage based ICA model for brain fMRI data analysis.
Wang N; Zeng W; Shi Y; Ren T; Jing Y; Yin J; Yang J
J Neurosci Methods; 2015 May; 246():75-96. PubMed ID: 25791013
[TBL] [Abstract][Full Text] [Related]
10. Statistical analysis of functional MRI data in the wavelet domain.
Ruttimann UE; Unser M; Rawlings RR; Rio D; Ramsey NF; Mattay VS; Hommer DW; Frank JA; Weinberger DR
IEEE Trans Med Imaging; 1998 Apr; 17(2):142-54. PubMed ID: 9688147
[TBL] [Abstract][Full Text] [Related]
11. Non-white noise in fMRI: does modelling have an impact?
Lund TE; Madsen KH; Sidaros K; Luo WL; Nichols TE
Neuroimage; 2006 Jan; 29(1):54-66. PubMed ID: 16099175
[TBL] [Abstract][Full Text] [Related]
12. Wavelet domain non-linear filtering for MRI denoising.
Anand CS; Sahambi JS
Magn Reson Imaging; 2010 Jul; 28(6):842-61. PubMed ID: 20418039
[TBL] [Abstract][Full Text] [Related]
13. [Denoising worm artifacts of elastogram using 2-D wavelet shrinkage].
Cui S; Liu D
Sheng Wu Yi Xue Gong Cheng Xue Za Zhi; 2011 Jun; 28(3):460-4. PubMed ID: 21774202
[TBL] [Abstract][Full Text] [Related]
14. Unified SPM-ICA for fMRI analysis.
Hu D; Yan L; Liu Y; Zhou Z; Friston KJ; Tan C; Wu D
Neuroimage; 2005 Apr; 25(3):746-55. PubMed ID: 15808976
[TBL] [Abstract][Full Text] [Related]
15. Independent vector analysis (IVA): multivariate approach for fMRI group study.
Lee JH; Lee TW; Jolesz FA; Yoo SS
Neuroimage; 2008 Mar; 40(1):86-109. PubMed ID: 18165105
[TBL] [Abstract][Full Text] [Related]
16. Probabilistic independent component analysis for functional magnetic resonance imaging.
Beckmann CF; Smith SM
IEEE Trans Med Imaging; 2004 Feb; 23(2):137-52. PubMed ID: 14964560
[TBL] [Abstract][Full Text] [Related]
17. The EM Method in a Probabilistic Wavelet-Based MRI Denoising.
Martin-Fernandez M; Villullas S
Comput Math Methods Med; 2015; 2015():182659. PubMed ID: 26089959
[TBL] [Abstract][Full Text] [Related]
18. Comparison of TCA and ICA techniques in fMRI data processing.
Zhao X; Glahn D; Tan LH; Li N; Xiong J; Gao JH
J Magn Reson Imaging; 2004 Apr; 19(4):397-402. PubMed ID: 15065162
[TBL] [Abstract][Full Text] [Related]
19. Semi-blind ICA of fMRI: A method for utilizing hypothesis-derived time courses in a spatial ICA analysis.
Calhoun VD; Adali T; Stevens MC; Kiehl KA; Pekar JJ
Neuroimage; 2005 Apr; 25(2):527-38. PubMed ID: 15784432
[TBL] [Abstract][Full Text] [Related]
20. Multivariate analysis of neuronal interactions in the generalized partial least squares framework: simulations and empirical studies.
Lin FH; McIntosh AR; Agnew JA; Eden GF; Zeffiro TA; Belliveau JW
Neuroimage; 2003 Oct; 20(2):625-42. PubMed ID: 14568440
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]