Construction of a 4D brain atlas and growth model using diffeomorphic registration

TY - CHAP

T1 - Construction of a 4D brain atlas and growth model using diffeomorphic registration

AU - Schuh, Andreas

AU - Murgasova, Maria

AU - Makropoulos, Antonios

AU - Ledig, Christian

AU - Counsell, Serena

AU - Hajnal, Jo

AU - Aljabar, Paul

AU - Rueckert, Daniel

PY - 2015/1/3

Y1 - 2015/1/3

N2 - Atlases of the human brain have numerous applications in neurological imaging such as the analysis of brain growth. Publicly available atlases of the developing brain have previously been constructed using the arithmetic mean of free-form deformations which were obtained by asymmetric pairwise registration of brain images. Most of these atlases represent cross-sections of the growth process only. In this work, we use the Log-Euclidean mean of inverse consistent transformations which belong to the one-parameter subgroup of diffeomorphisms, as it more naturally represents average morphology. During the registration, similarity is evaluated symmetrically for the images to be aligned. As both images are equally affected by the deformation and interpolation, asymmetric bias is reduced. We further propose to represent longitudinal change by exploiting the numerous transformations computed during the atlas construction in order to derive a deformation model of mean growth. Based on brain images of 118 neonates, we constructed an atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation. The evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.

AB - Atlases of the human brain have numerous applications in neurological imaging such as the analysis of brain growth. Publicly available atlases of the developing brain have previously been constructed using the arithmetic mean of free-form deformations which were obtained by asymmetric pairwise registration of brain images. Most of these atlases represent cross-sections of the growth process only. In this work, we use the Log-Euclidean mean of inverse consistent transformations which belong to the one-parameter subgroup of diffeomorphisms, as it more naturally represents average morphology. During the registration, similarity is evaluated symmetrically for the images to be aligned. As both images are equally affected by the deformation and interpolation, asymmetric bias is reduced. We further propose to represent longitudinal change by exploiting the numerous transformations computed during the atlas construction in order to derive a deformation model of mean growth. Based on brain images of 118 neonates, we constructed an atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation. The evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.

UR - http://www.scopus.com/inward/record.url?scp=84927918096&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-14905-9_3

DO - 10.1007/978-3-319-14905-9_3

M3 - Chapter

AN - SCOPUS:84927918096

SN - 978-3-319-14904-2

VL - 8682

SP - 27

EP - 37

BT - Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data

PB - Springer International Publishing Switzerland

T2 - 3rd International Workshop on Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data, STIA 2014 in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2014

Y2 - 18 September 2014 through 18 September 2014

ER -