Abstract
Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective correspondence matching. The attribute vector captures complementary information from both the global connectivity structure given by the fiber tracts and the local anatomical architecture given by the tensor regional descriptors. We incorporate this attribute vector into a multi-scale registration framework where the moving image is warped to the space of the fixed image under the guidance of tract information at a more global level (coarse scales), followed by alignment refinement using regional tensor distribution features at a more local level (fine scales). Experimental results indicate that this framework yields marked improvement over DT image registration using volumetric information alone.
| Original language | English |
|---|---|
| Title of host publication | Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention |
| Pages | 200-208 |
| Number of pages | 9 |
| Volume | 14 |
| Edition | Pt 2 |
| Publication status | Published - 2011 Dec 1 |
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ASJC Scopus subject areas
- Medicine(all)
Cite this
Diffusion tensor image registration with combined tract and tensor features. / Wang, Qian; Yap, Pew Thian; Wu, Guorong; Shen, Dinggang.
Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention. Vol. 14 Pt 2. ed. 2011. p. 200-208.Research output: Chapter in Book/Report/Conference proceeding › Chapter
}
TY - CHAP
T1 - Diffusion tensor image registration with combined tract and tensor features.
AU - Wang, Qian
AU - Yap, Pew Thian
AU - Wu, Guorong
AU - Shen, Dinggang
PY - 2011/12/1
Y1 - 2011/12/1
N2 - Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective correspondence matching. The attribute vector captures complementary information from both the global connectivity structure given by the fiber tracts and the local anatomical architecture given by the tensor regional descriptors. We incorporate this attribute vector into a multi-scale registration framework where the moving image is warped to the space of the fixed image under the guidance of tract information at a more global level (coarse scales), followed by alignment refinement using regional tensor distribution features at a more local level (fine scales). Experimental results indicate that this framework yields marked improvement over DT image registration using volumetric information alone.
AB - Registration of diffusion tensor (DT) images is indispensible, especially in white-matter studies involving a significant amount of data. This task is however faced with challenging issues such as the generally low SNR of diffusion-weighted images and the relatively high complexity of tensor representation. To improve the accuracy of DT image registration, we design an attribute vector that encapsulates both tract and tensor information to serve as a voxel morphological signature for effective correspondence matching. The attribute vector captures complementary information from both the global connectivity structure given by the fiber tracts and the local anatomical architecture given by the tensor regional descriptors. We incorporate this attribute vector into a multi-scale registration framework where the moving image is warped to the space of the fixed image under the guidance of tract information at a more global level (coarse scales), followed by alignment refinement using regional tensor distribution features at a more local level (fine scales). Experimental results indicate that this framework yields marked improvement over DT image registration using volumetric information alone.
UR - http://www.scopus.com/inward/record.url?scp=82255193231&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=82255193231&partnerID=8YFLogxK
M3 - Chapter
VL - 14
SP - 200
EP - 208
BT - Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
ER -