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Items: 1 to 20 of 91

1.

Local spatio-temporal encoding of raw perfusion MRI for the prediction of final lesion in stroke.

Giacalone M, Rasti P, Debs N, Frindel C, Cho TH, Grenier E, Rousseau D.

Med Image Anal. 2018 Dec;50:117-126. doi: 10.1016/j.media.2018.08.008. Epub 2018 Sep 23.

PMID:
30268970
2.

Robustness of spatio-temporal regularization in perfusion MRI deconvolution: An application to acute ischemic stroke.

Giacalone M, Frindel C, Robini M, Cervenansky F, Grenier E, Rousseau D.

Magn Reson Med. 2017 Nov;78(5):1981-1990. doi: 10.1002/mrm.26573. Epub 2016 Dec 26.

PMID:
28019027
3.

A Machine Learning Approach to Perfusion Imaging With Dynamic Susceptibility Contrast MR.

McKinley R, Hung F, Wiest R, Liebeskind DS, Scalzo F.

Front Neurol. 2018 Sep 4;9:717. doi: 10.3389/fneur.2018.00717. eCollection 2018.

4.

On differentiation between vasogenic edema and non-enhancing tumor in high-grade glioma patients using a support vector machine classifier based upon pre and post-surgery MRI images.

Sengupta A, Agarwal S, Gupta PK, Ahlawat S, Patir R, Gupta RK, Singh A.

Eur J Radiol. 2018 Sep;106:199-208. doi: 10.1016/j.ejrad.2018.07.018. Epub 2018 Jul 20.

PMID:
30150045
5.

Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data.

Meyer S, Mueller K, Stuke K, Bisenius S, Diehl-Schmid J, Jessen F, Kassubek J, Kornhuber J, Ludolph AC, Prudlo J, Schneider A, Schuemberg K, Yakushev I, Otto M, Schroeter ML; FTLDc Study Group.

Neuroimage Clin. 2017 Feb 6;14:656-662. doi: 10.1016/j.nicl.2017.02.001. eCollection 2017.

6.

Voxel-based clustered imaging by multiparameter diffusion tensor images for glioma grading.

Inano R, Oishi N, Kunieda T, Arakawa Y, Yamao Y, Shibata S, Kikuchi T, Fukuyama H, Miyamoto S.

Neuroimage Clin. 2014 Aug 7;5:396-407. doi: 10.1016/j.nicl.2014.08.001. eCollection 2014.

7.

A 3-D spatio-temporal deconvolution approach for MR perfusion in the brain.

Frindel C, Robini MC, Rousseau D.

Med Image Anal. 2014 Jan;18(1):144-60. doi: 10.1016/j.media.2013.10.004. Epub 2013 Oct 16.

PMID:
24184525
8.

Multiclass Support Vector Machine-Based Lesion Mapping Predicts Functional Outcome in Ischemic Stroke Patients.

Forkert ND, Verleger T, Cheng B, Thomalla G, Hilgetag CC, Fiehler J.

PLoS One. 2015 Jun 22;10(6):e0129569. doi: 10.1371/journal.pone.0129569. eCollection 2015.

9.

Patch-based local learning method for cerebral blood flow quantification with arterial spin-labeling MRI.

Zhu H, He G, Wang Z.

Med Biol Eng Comput. 2018 Jun;56(6):951-956. doi: 10.1007/s11517-017-1735-6. Epub 2017 Nov 6.

PMID:
29105017
10.

Predicting primary progressive aphasias with support vector machine approaches in structural MRI data.

Bisenius S, Mueller K, Diehl-Schmid J, Fassbender K, Grimmer T, Jessen F, Kassubek J, Kornhuber J, Landwehrmeyer B, Ludolph A, Schneider A, Anderl-Straub S, Stuke K, Danek A, Otto M, Schroeter ML; FTLDc study group.

Neuroimage Clin. 2017 Feb 6;14:334-343. doi: 10.1016/j.nicl.2017.02.003. eCollection 2017.

11.

Acute stroke: a comparison of different CT perfusion algorithms and validation of ischaemic lesions by follow-up imaging.

Abels B, Villablanca JP, Tomandl BF, Uder M, Lell MM.

Eur Radiol. 2012 Dec;22(12):2559-67. doi: 10.1007/s00330-012-2529-8. Epub 2012 Jun 21.

PMID:
22717727
12.

Support vector machine classification of arterial volume-weighted arterial spin tagging images.

Shah YS, Hernandez-Garcia L, Jahanian H, Peltier SJ.

Brain Behav. 2016 Oct 7;6(12):e00549. doi: 10.1002/brb3.549. eCollection 2016 Dec.

13.

Cardiac magnetic resonance image-based classification of the risk of arrhythmias in post-myocardial infarction patients.

Kotu LP, Engan K, Borhani R, Katsaggelos AK, Ørn S, Woie L, Eftestøl T.

Artif Intell Med. 2015 Jul;64(3):205-15. doi: 10.1016/j.artmed.2015.06.001. Epub 2015 Jul 4.

PMID:
26239472
14.

Trial design and reporting standards for intra-arterial cerebral thrombolysis for acute ischemic stroke.

Higashida RT, Furlan AJ, Roberts H, Tomsick T, Connors B, Barr J, Dillon W, Warach S, Broderick J, Tilley B, Sacks D; Technology Assessment Committee of the American Society of Interventional and Therapeutic Neuroradiology; Technology Assessment Committee of the Society of Interventional Radiology.

Stroke. 2003 Aug;34(8):e109-37. Epub 2003 Jul 17. Erratum in: Stroke. 2003 Nov;34(11):2774.

PMID:
12869717
15.

Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases.

Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, Smid J, Nitrini R, Buchpiguel CA, Busatto GF.

Neuroimage Clin. 2017 Nov 9;17:628-641. doi: 10.1016/j.nicl.2017.10.026. eCollection 2018.

16.

Comparison of arterial spin labeling and dynamic susceptibility contrast perfusion MRI in patients with acute stroke.

Huang YC, Liu HL, Lee JD, Yang JT, Weng HH, Lee M, Yeh MY, Tsai YH.

PLoS One. 2013 Jul 16;8(7):e69085. doi: 10.1371/journal.pone.0069085. Print 2013.

17.
18.

Estimation of breast percent density in raw and processed full field digital mammography images via adaptive fuzzy c-means clustering and support vector machine segmentation.

Keller BM, Nathan DL, Wang Y, Zheng Y, Gee JC, Conant EF, Kontos D.

Med Phys. 2012 Aug;39(8):4903-17. doi: 10.1118/1.4736530.

19.

Machine learning in preoperative glioma MRI: survival associations by perfusion-based support vector machine outperforms traditional MRI.

Emblem KE, Due-Tonnessen P, Hald JK, Bjornerud A, Pinho MC, Scheie D, Schad LR, Meling TR, Zoellner FG.

J Magn Reson Imaging. 2014 Jul;40(1):47-54. doi: 10.1002/jmri.24390. Epub 2013 Nov 13.

PMID:
24753371
20.

A spatio-temporal deconvolution method to improve perfusion CT quantification.

He L, Orten B, Do S, Karl WC, Kambadakone A, Sahani DV, Pien H.

IEEE Trans Med Imaging. 2010 May;29(5):1182-91. doi: 10.1109/TMI.2010.2043536. Epub 2010 Apr 8.

PMID:
20378468

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