Keywords:
Anatomy, Neuroradiology brain, MR, Segmentation, Education and training
Authors:
M. Laganà1, S. Carrara1, M. Olmi2, M. Cresti2, L. Forzoni3, P. Cecconi4; 1Milano/IT, 2Genova/IT, 3Firenze/IT, 4Como (CO)/IT
DOI:
10.1594/ecr2018/C-1387
References
[1] C.
M.
Collins and M.
B.
Smith.
Signal-to-Noise Ratio and Absorbed Power as Functions of Main Magnetic Field Strength,
and Definition of “90°” RF Pulse for the Head in the Birdcage Coil.
Magnetic Resonance in Medicine 45:684–691 (2001)
[2] M.
Lustig,
D.
Donoho,
J.
M.
Pauly.
Sparse MRI: The Application of Compressed Sensing for Rapid MR Imaging.
Magnetic Resonance in Medicine 58:1182–1195 (2007)
[3] S.M.
Smith.
Fast robust automated brain extraction.
Hum Brain Mapp 2002 Nov;17(3):143-155.
[4] S.M.
Smith,
Y.
Zhang,
M.
Jenkinson,
J.
Chen,
P.M.
Matthews,
A.
Federico,
et al.
Accurate,
robust,
and automated longitudinal and cross-sectional brain change analysis.
Neuroimage 2002 Sep;17:479-489.
[5] B.Patenaude,
S.M.Smith,
D.Kennedy,
and M.
Jenkinson.
A Bayesian Model of Shape and Appearance for Subcortical Brain NeuroImage,
56:907-922,
2011.
[6] M.
Laganà,
M.
Rovaris,
A.
Ceccarelli,
C.
Venturelli,
S.
Marini and G.
Baselli.
DTI Parameter Optimisation for Acquisition at 1.5T: SNR Analysis and Clinical Application.
Computational Intelligence and Neuroscience 2010:254032 (2010)
[7] M.G.
Dwyer,
N.P.
Bergsland,
R.
Zivadinov.
Improved longitudinal gray and white matter atrophy assessment via application of a 4-dimensional hidden Markov random field model.
Neuroimage 2014.