TY - GEN
T1 - Spatial warping of DWI data using sparse representation
AU - Yap, Pew Thian
AU - Shen, Dinggang
PY - 2012
Y1 - 2012
N2 - Registration of DWI data, unlike scalar image data, is complicated by the need of reorientation algorithms for keeping the orientation architecture of each voxel aligned with the rest of the image. This paper presents an algorithm for effective and efficient warping and reconstruction of diffusion-weighted imaging (DWI) signals for the purpose of spatial transformation. The key idea is to decompose the DWI signal profile, a function defined on a unit sphere, into a series of weighted fiber basis functions (FBFs), reorient these FBFs independently based on the local affine transformation, and then recompose the reoriented FBFs to obtain the final transformed DWI signal profile. We enforce a sparsity constraint on the weights of the FBFs during the decomposition to reflect the fact that the DWI signal profile typically gains its information from a limited number of fiber populations. A non-negative constraint is further imposed so that noise-induced negative lobes in the profile can be avoided. The proposed framework also explicitly models the isotropic component of the diffusion signals to avoid undesirable reorientation artifacts in signal reconstruction. In contrast to existing methods, the current algorithm is executed directly in the DWI signal space, thus allowing any diffusion models to be fitted to the data after transformation.
AB - Registration of DWI data, unlike scalar image data, is complicated by the need of reorientation algorithms for keeping the orientation architecture of each voxel aligned with the rest of the image. This paper presents an algorithm for effective and efficient warping and reconstruction of diffusion-weighted imaging (DWI) signals for the purpose of spatial transformation. The key idea is to decompose the DWI signal profile, a function defined on a unit sphere, into a series of weighted fiber basis functions (FBFs), reorient these FBFs independently based on the local affine transformation, and then recompose the reoriented FBFs to obtain the final transformed DWI signal profile. We enforce a sparsity constraint on the weights of the FBFs during the decomposition to reflect the fact that the DWI signal profile typically gains its information from a limited number of fiber populations. A non-negative constraint is further imposed so that noise-induced negative lobes in the profile can be avoided. The proposed framework also explicitly models the isotropic component of the diffusion signals to avoid undesirable reorientation artifacts in signal reconstruction. In contrast to existing methods, the current algorithm is executed directly in the DWI signal space, thus allowing any diffusion models to be fitted to the data after transformation.
UR - http://www.scopus.com/inward/record.url?scp=84988850594&partnerID=8YFLogxK
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U2 - 10.1007/978-3-642-33418-4_41
DO - 10.1007/978-3-642-33418-4_41
M3 - Conference contribution
C2 - 23286065
AN - SCOPUS:84988850594
SN - 9783642334177
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 331
EP - 338
BT - Medical Image Computing and Computer-Assisted Intervention - MICCAI2012 - 15th International Conference, Proceedings
A2 - Langs, Georg
A2 - Criminisi, Antonio
A2 - Langs, Georg
A2 - Menze, Bjoern H.
A2 - Ayache, Nicholas
A2 - Delingette, Hervé
A2 - Lu, Le
A2 - Menze, Bjoern H.
A2 - Tu, Zhuowen
A2 - Montillo, Albert
A2 - Golland, Polina
A2 - Mori, Kensaku
PB - Springer Verlag
T2 - 15th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2012
Y2 - 5 October 2012 through 5 October 2012
ER -