TY - JOUR
T1 - Analysis of automated methods for spatial normalization of lesioned brains
AU - Ripollés, P.
AU - Marco-Pallarés, J.
AU - de Diego-Balaguer, R.
AU - Miró, J.
AU - Falip, M.
AU - Juncadella, M.
AU - Rubio, F.
AU - Rodriguez-Fornells, A.
N1 - Funding Information:
ALI toolbox was kindly provided by Mohamed Seghier. We want to thank M. Seghier and E. Cámara for their suggestions and comments on a previous version of the present article. This work was funded by an Obra Social La Caixa grant to P. Ripollés and supported by Grants from the Spanish Government PSI2008-03885 to Ruth de Diego-Balaguer, PSI2009-09101 to Josep Marco-Pallarés and PSI2008-03901 , La Marato de TV3 (Neuroscience Program) and the Catalan Governments ( SGR 2009 SGR 93 ) to Antoni Rodriguez-Fornells. Josep Marco-Pallarés is supported by the Ramon y Cajal program of the Spanish Department of Science . We want to specially thank Diana Lopez-Barroso, David Cucurell, Nuria Rojo, Julià Amengual and Cesar Garrido for their help scanning the patients analyzed in the present study. Finally, we want also to thank the very constructive comments of the reviewers of the present manuscript.
PY - 2012/4/2
Y1 - 2012/4/2
N2 - Normalization of brain images is a crucial step in MRI data analysis, especially when dealing with abnormal brains. Although cost function masking (CFM) appears to successfully solve this problem and seems to be necessary for patients with chronic stroke lesions, this procedure is very time consuming. The present study sought to find viable, fully automated alternatives to cost function masking, such as Automatic Lesion Identification (ALI) and Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL). It also sought to quantitatively assess, for the first time, Symmetrical Normalization (SyN) with constrained cost function masking. The second aim of this study was to investigate the normalization process in a group of drug-resistant epileptic patients with large resected regions (temporal lobe and amygdala) and in a group of stroke patients. A dataset of 500 artificially generated lesions was created using ten patients with brain-resected regions (temporal lobectomy), ten stroke patients and twenty five-healthy subjects. The results indicated that although a fully automated method such as DARTEL using New Segment with an extra prior (the mean of the white matter and cerebro-spinal fluid) obtained the most accurate normalization in both patient groups, it produced a shrinkage in lesion volume when compared to Unified Segmentation with CFM. Taken together, these findings suggest that further research is needed in order to improve automatic normalization processes in brains with large lesions and to completely abandon manual, time consuming normalization methods.
AB - Normalization of brain images is a crucial step in MRI data analysis, especially when dealing with abnormal brains. Although cost function masking (CFM) appears to successfully solve this problem and seems to be necessary for patients with chronic stroke lesions, this procedure is very time consuming. The present study sought to find viable, fully automated alternatives to cost function masking, such as Automatic Lesion Identification (ALI) and Diffeomorphic Anatomical Registration using Exponentiated Lie algebra (DARTEL). It also sought to quantitatively assess, for the first time, Symmetrical Normalization (SyN) with constrained cost function masking. The second aim of this study was to investigate the normalization process in a group of drug-resistant epileptic patients with large resected regions (temporal lobe and amygdala) and in a group of stroke patients. A dataset of 500 artificially generated lesions was created using ten patients with brain-resected regions (temporal lobectomy), ten stroke patients and twenty five-healthy subjects. The results indicated that although a fully automated method such as DARTEL using New Segment with an extra prior (the mean of the white matter and cerebro-spinal fluid) obtained the most accurate normalization in both patient groups, it produced a shrinkage in lesion volume when compared to Unified Segmentation with CFM. Taken together, these findings suggest that further research is needed in order to improve automatic normalization processes in brains with large lesions and to completely abandon manual, time consuming normalization methods.
KW - Cost function masking
KW - Diffeomorphic
KW - Epilepsy
KW - Normalization
KW - Stroke
KW - Unified Segmentation
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U2 - 10.1016/j.neuroimage.2012.01.094
DO - 10.1016/j.neuroimage.2012.01.094
M3 - Article
C2 - 22305954
AN - SCOPUS:84856812361
SN - 1053-8119
VL - 60
SP - 1296
EP - 1306
JO - NeuroImage
JF - NeuroImage
IS - 2
ER -