Image Processing Reference
In-Depth Information
after the first attack (6, 12 and 18 months) to see when a better discrimination occurs.
We will also investigate what is the minimal number of time-points needed to fit cor-
rectly the data and discriminate between the patient groups. An early distinction with
a minimal number of time-points could permit earlier initiation of treatment for MS
subjects.
References
1.
Kerbrat, A., Aubert-Broche, B., Fonov, V., et al.: Reduced head and brain size for age and
disproportionately smaller thalami in child-onset MS. Neurology
78
, 194-201 (2012)
2.
Aubert-Broche, B., Fonov, V., Ghassemi, R., et al.: Regional brain atrophy in children
with multiple sclerosis. NeuroImage
58
, 409-415 (2011)
3.
Aubert-Broche, B., Fonov, V., Guizard, N., Banwell, B., Narayanan, S., Arnold, D.L.,
Collins, D.L.: Brain growth rate is reduced in paediatric-onset multiple sclerosis. In: Euro-
pean Committees for Treatment and Research in Multiple Sclerosis 2012, Lyon, France
(2012)
4.
Mesaros, S., Rocca, M.A., Absinta, M., et al.: Evidence of thalamic gray matter loss in pe-
diatric multiple sclerosis. Neurology
70
, 1107-1112 (2008)
5.
Polman, C.H., Reingold, S.C., Edan, G., et al.: Diagnostic criteria for multiple sclerosis:
2005 revisions to the “McDonald Criteria”. Ann. Neurol.
58
, 840-846 (2005)
6.
Evans, A.C.: Brain Development Cooperative G. The NIH MRI study of normal brain de-
velopment. NeuroImage
30
, 184-202 (2006)
7.
Aubert-Broche, B., Fonov, V.S., Garcia-Lorenzo, D., et al.: A new method for structural
volume analysis of longitudinal brain MRI data and its application in studying the growth
trajectories of anatomical brain structures in childhood. NeuroImage
82C
, 393-402 (2013)
8.
Coupe, P., Manjon, J.V., Gedamu, E., Arnold, D., Robles, M., Collins, D.L.: Robust
Rician noise estimation for MR images. Medical Image Analysis
14
, 483-493 (2010)
9.
Sled, J.G., Zijdenbos, A.P., Evans, A.C.: A nonparametric method for automatic correction
of intensity nonuniformity in MRI data. IEEE Transactions on Medical Imaging
17
, 87-97
(1998)
10.
Fonov, V., Evans, A.C., Botteron, K., et al.: Unbiased average age-appropriate atlases for
pediatric studies. NeuroImage
54
, 313-327 (2011)
11.
Francis, S.: Automatic lesion identification in MRI of multiple sclerosis patients. McGill
University (2004)
12.
Collins, D.L., Neelin, P., Peters, T.M., Evans, A.C.: Automatic 3D intersubject registration
of MR volumetric data in standardized Talairach space. Journal of Computer Assisted To-
mography
18
, 192-205 (1994)
13.
Eskildsen, S.F., Coupe, P., Fonov, V., et al.: BEaST: brain extraction based on nonlocal
segmentation technique. NeuroImage
59
, 2362-2373 (2012)
14.
Garcia-Lorenzo, D., Prima, S., Arnold, D.L., Collins, D.L., Barillot, C.: Trimmed-
likelihood estimation for focal lesions and tissue segmentation in multisequence MRI for
multiple sclerosis. IEEE Transactions on Medical Imaging
30
, 1455-1467 (2011)
15.
Bernal-Rusiel, J.L., Greve, D.N., Reuter, M., Fischl, B., Sabuncu, M.R.: For the Alz-
heimer's Disease Neuroimaging I. Statistical analysis of longitudinal neuroimage data with
Linear Mixed Effects models. NeuroImage
66C
, 249-260 (2012)
16.
Cheng, J., Edwards, L.J., Maldonado-Molina, M.M., Komro, K.A., Muller, K.E.: Real lon-
gitudinal data analysis for real people: building a good enough mixed model. Statistics in
Medicine
29
, 504-520 (2010)
Search WWH ::
Custom Search