TY - JOUR
T1 - Very high-resolution morphometry using mass-preserving deformations and HAMMER elastic registration
AU - Shen, Dinggang
AU - Davatzikos, Christos
N1 - Funding Information:
We thank Dr. Susan Resnick and the BLSA for providing the data sets. This work was supported in part by NIH Grant R01 AG14971 and by NIH Contract AG-93-07.
PY - 2003
Y1 - 2003
N2 - This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.
AB - This article presents a very high-resolution voxel-based morphometric method, by using a mass-preserving deformation mechanism and a fully automated spatial normalization approach, referred to as HAMMER. By using a hierarchical attribute-based deformation strategy, HAMMER partly overcomes limitations of several existing spatial normalization methods, and it achieves a level of accuracy that makes possible morphometric measurements of spatial specificity close to the voxel dimensions. The proposed method is validated by a series of experiments, with both simulated and real brain images.
UR - http://www.scopus.com/inward/record.url?scp=0037229905&partnerID=8YFLogxK
U2 - 10.1006/nimg.2002.1301
DO - 10.1006/nimg.2002.1301
M3 - Article
C2 - 12507441
AN - SCOPUS:0037229905
SN - 1053-8119
VL - 18
SP - 28
EP - 41
JO - NeuroImage
JF - NeuroImage
IS - 1
ER -