TY - GEN
T1 - Construction of neonatal diffusion atlases via spatio-angular consistency
AU - Saghafi, Behrouz
AU - Chen, Geng
AU - Shi, Feng
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2016
Y1 - 2016
N2 - Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for DWI atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free. Instead of generating an atlas for each gradient direction independently and hence neglecting inter-image correlation, we propose to construct the atlas by jointly considering diffusion-weighted images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results verify that our atlas, constructed for neonatal data, reveals more structural details compared with the average atlas especially in the cortical regions. Our atlas also yields greater accuracy when used for image normalization.
AB - Atlases constructed using diffusion-weighted imaging (DWI) are important tools for studying human brain development. Atlas construction is in general a two-step process involving image registration and image fusion. The focus of most studies so far has been on improving registration thus image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this paper, we propose a patch-based method for DWI atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free. Instead of generating an atlas for each gradient direction independently and hence neglecting inter-image correlation, we propose to construct the atlas by jointly considering diffusion-weighted images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results verify that our atlas, constructed for neonatal data, reveals more structural details compared with the average atlas especially in the cortical regions. Our atlas also yields greater accuracy when used for image normalization.
UR - http://www.scopus.com/inward/record.url?scp=84992491596&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84992491596&partnerID=8YFLogxK
U2 - 10.1007/978-3-319-47118-1_2
DO - 10.1007/978-3-319-47118-1_2
M3 - Conference contribution
AN - SCOPUS:84992491596
SN - 9783319471174
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 9
EP - 16
BT - Patch-Based Techniques in Medical Imaging - 2nd International Workshop, Patch-MI 2016 held in conjunction with MICCAI 2016, Proceedings
A2 - Coupe, Pierrick
A2 - Munsell, Brent C.
A2 - Rueckert, Daniel
A2 - Zhan, Yiqiang
A2 - Wu, Guorong
PB - Springer Verlag
T2 - 2nd International Workshop on Patch-Based Techniques in Medical Imaging, Patch-MI 2016 held in conjunction with 19th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2016
Y2 - 17 October 2016 through 17 October 2016
ER -