TY - JOUR
T1 - Intermediate templates guided groupwise registration of diffusion tensor images
AU - Jia, Hongjun
AU - Yap, Pew Thian
AU - Wu, Guorong
AU - Wang, Qian
AU - Shen, Dinggang
N1 - Funding Information:
This work was supported in part by NIH grants EB006733, EB008760, EB008374, MH088520 and EB009634.
PY - 2011/1/15
Y1 - 2011/1/15
N2 - Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image.
AB - Registration of a population of diffusion tensor images (DTIs) is one of the key steps in medical image analysis, and it plays an important role in the statistical analysis of white matter related neurological diseases. However, pairwise registration with respect to a pre-selected template may not give precise results if the selected template deviates significantly from the distribution of images. To cater for more accurate and consistent registration, a novel framework is proposed for groupwise registration with the guidance from one or more intermediate templates determined from the population of images. Specifically, we first use a Euclidean distance, defined as a combinative measure based on the FA map and ADC map, for gauging the similarity of each pair of DTIs. A fully connected graph is then built with each node denoting an image and each edge denoting the distance between a pair of images. The root template image is determined automatically as the image with the overall shortest path length to all other images on the minimum spanning tree (MST) of the graph. Finally, a sequence of registration steps is applied to progressively warping each image towards the root template image with the help of intermediate templates distributed along its path to the root node on the MST. Extensive experimental results using diffusion tensor images of real subjects indicate that registration accuracy and fiber tract alignment are significantly improved, compared with the direct registration from each image to the root template image.
KW - Diffusion tensor image
KW - Fiber tract alignment
KW - Groupwise registration
KW - Image registration
KW - Intermediate templates
KW - Minimum spanning tree (MST)
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U2 - 10.1016/j.neuroimage.2010.09.019
DO - 10.1016/j.neuroimage.2010.09.019
M3 - Article
C2 - 20851197
AN - SCOPUS:78649717885
VL - 54
SP - 928
EP - 939
JO - NeuroImage
JF - NeuroImage
SN - 1053-8119
IS - 2
ER -