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Items: 1 to 20 of 100

1.

Bayesian scalar-on-image regression with application to association between intracranial DTI and cognitive outcomes.

Huang L, Goldsmith J, Reiss PT, Reich DS, Crainiceanu CM.

Neuroimage. 2013 Dec;83:210-23. doi: 10.1016/j.neuroimage.2013.06.020. Epub 2013 Jun 17.

2.
3.

Automated detection of amnestic mild cognitive impairment in community-dwelling elderly adults: a combined spatial atrophy and white matter alteration approach.

Cui Y, Wen W, Lipnicki DM, Beg MF, Jin JS, Luo S, Zhu W, Kochan NA, Reppermund S, Zhuang L, Raamana PR, Liu T, Trollor JN, Wang L, Brodaty H, Sachdev PS.

Neuroimage. 2012 Jan 16;59(2):1209-17. doi: 10.1016/j.neuroimage.2011.08.013. Epub 2011 Aug 16.

PMID:
21864688
4.

Statistical detection of longitudinal changes between apparent diffusion coefficient images: application to multiple sclerosis.

Boisgontier H, Noblet V, Renard F, Heitz F, Rumbach L, Armspach JP.

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):959-66.

PMID:
20426081
5.

Joint fractional segmentation and multi-tensor estimation in diffusion MRI.

Hao X, Fletcher PT.

Inf Process Med Imaging. 2013;23:340-51.

PMID:
24683981
6.

Adaptive multi-level conditional random fields for detection and segmentation of small enhanced pathology in medical images.

Karimaghaloo Z, Arnold DL, Arbel T.

Med Image Anal. 2016 Jan;27:17-30. doi: 10.1016/j.media.2015.06.004. Epub 2015 Jul 11.

PMID:
26211811
7.

Registration and analysis of white matter group differences with a multi-fiber model.

Taquet M, Scherrer B, Commowick O, Peters J, Sahin M, Macq B, Warfield SK.

Med Image Comput Comput Assist Interv. 2012;15(Pt 3):313-20.

8.

Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.

Brosch T, Yoo Y, Li DK, Traboulsee A, Tam R.

Med Image Comput Comput Assist Interv. 2014;17(Pt 2):462-9.

PMID:
25485412
9.

Toward tract-specific fractional anisotropy (TSFA) at crossing-fiber regions with clinical diffusion MRI.

Mishra V, Guo X, Delgado MR, Huang H.

Magn Reson Med. 2015 Dec;74(6):1768-79. doi: 10.1002/mrm.25548. Epub 2014 Dec 1.

10.

Constrained data decomposition and regression for analyzing healthy aging from fiber tract diffusion properties.

Gouttard S, Prastawa M, Bullitt E, Lin W, Goodlett C, Gerig G.

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):321-8.

11.

Detection of DTI white matter abnormalities in multiple sclerosis patients.

Commowick O, Fillard P, Clatz O, Warfield SK.

Med Image Comput Comput Assist Interv. 2008;11(Pt 1):975-82.

12.

Automated vs. conventional tractography in multiple sclerosis: variability and correlation with disability.

Reich DS, Ozturk A, Calabresi PA, Mori S.

Neuroimage. 2010 Feb 15;49(4):3047-56. doi: 10.1016/j.neuroimage.2009.11.043. Epub 2009 Nov 26.

13.

Generalized likelihood ratio tests for change detection in diffusion tensor images: application to multiple sclerosis.

Boisgontier H, Noblet V, Heitz F, Rumbach L, Armspach JP.

Med Image Anal. 2012 Jan;16(1):325-38. doi: 10.1016/j.media.2011.08.007. Epub 2011 Sep 8.

PMID:
21963295
14.

Nonparametric Bayesian inference of the fiber orientation distribution from diffusion-weighted MR images.

Kaden E, Kruggel F.

Med Image Anal. 2012 May;16(4):876-88. doi: 10.1016/j.media.2012.01.004. Epub 2012 Feb 1.

PMID:
22381587
15.
16.

Thalamic-hippocampal-prefrontal disruption in relapsing-remitting multiple sclerosis.

Kern KC, Gold SM, Lee B, Montag M, Horsfall J, O'Connor MF, Sicotte NL.

Neuroimage Clin. 2014 Dec 27;8:440-7. doi: 10.1016/j.nicl.2014.12.015. eCollection 2015.

17.

Belief propagation based segmentation of white matter tracts in DTI.

Bazin PL, Bogovic J, Reich D, Prince JL, Pham DL.

Med Image Comput Comput Assist Interv. 2009;12(Pt 1):943-50.

18.

Diffusion MRI abnormalities detection with orientation distribution functions: a multiple sclerosis longitudinal study.

Commowick O, Maarouf A, Ferré JC, Ranjeva JP, Edan G, Barillot C.

Med Image Anal. 2015 May;22(1):114-23. doi: 10.1016/j.media.2015.02.005. Epub 2015 Mar 20.

19.

Improving DTI resolution from a single clinical acquisition: a statistical approach using spatial prior.

Gupta V, Ayache N, Pennec X.

Med Image Comput Comput Assist Interv. 2013;16(Pt 3):477-84.

PMID:
24505796
20.

DTI correlates of cognition in conventional MRI of normal-appearing brain in patients with clinical features of subacute combined degeneration and biochemically proven vitamin B(12) deficiency.

Gupta PK, Gupta RK, Garg RK, Rai Y, Roy B, Pandey CM, Malhotra HS, Narayana PA.

AJNR Am J Neuroradiol. 2014 May;35(5):872-7. doi: 10.3174/ajnr.A3785. Epub 2013 Nov 21.

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