TY - JOUR
T1 - Spatial transformation of DWI data using non-negative sparse representation
AU - Yap, Pew Thian
AU - Shen, Dinggang
N1 - Funding Information:
Manuscript received April 12, 2012; revised May 28, 2012; accepted June 05, 2012. Date of publication June 13, 2012; date of current version October 26, 2012. This work was supported in part by a University of North Carolina start-up fund and in part by the National Institutes of Health under Grant EB006733, Grant EB008374, Grant EB009634, Grant MH088520, and Grant AG041721. Asterisk indicates corresponding author. *P.-T. Yap is with the Department of Radiology and the Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC 27599 USA.
PY - 2012
Y1 - 2012
N2 - This paper presents an algorithm to transform and reconstruct diffusion-weighted imaging (DWI) data for alignment of micro-structures in association with spatial transformations. The key idea is to decompose the diffusion-attenuated signal profile, a function defined on a unit sphere, into a series of weighted diffusion basis functions (DBFs), reorient these weighted DBFs independently based on a local affine transformation, and then recompose the reoriented weighted DBFs to obtain the final transformed signal profile. The decomposition is performed in a sparse representation framework in recognition of the fact that each diffusion signal profile is often resulting from a small number of fiber populations. A non-negative constraint is further imposed so that noise-induced negative lobes in the signal profile can be avoided. The proposed framework also explicitly models the isotropic component of the diffusion-attenuated signals to avoid undesirable artifacts during transformation. In contrast to existing methods, the current algorithm executes the transformation directly in the signal space, thus allowing any diffusion models to be fitted to the data after transformation.
AB - This paper presents an algorithm to transform and reconstruct diffusion-weighted imaging (DWI) data for alignment of micro-structures in association with spatial transformations. The key idea is to decompose the diffusion-attenuated signal profile, a function defined on a unit sphere, into a series of weighted diffusion basis functions (DBFs), reorient these weighted DBFs independently based on a local affine transformation, and then recompose the reoriented weighted DBFs to obtain the final transformed signal profile. The decomposition is performed in a sparse representation framework in recognition of the fact that each diffusion signal profile is often resulting from a small number of fiber populations. A non-negative constraint is further imposed so that noise-induced negative lobes in the signal profile can be avoided. The proposed framework also explicitly models the isotropic component of the diffusion-attenuated signals to avoid undesirable artifacts during transformation. In contrast to existing methods, the current algorithm executes the transformation directly in the signal space, thus allowing any diffusion models to be fitted to the data after transformation.
KW - Diffusion-weighted imaging
KW - reorientation
KW - spatial transformation
UR - http://www.scopus.com/inward/record.url?scp=84877610678&partnerID=8YFLogxK
U2 - 10.1109/TMI.2012.2204766
DO - 10.1109/TMI.2012.2204766
M3 - Article
C2 - 22711770
AN - SCOPUS:84877610678
VL - 31
SP - 2035
EP - 2049
JO - IEEE Transactions on Medical Imaging
JF - IEEE Transactions on Medical Imaging
SN - 0278-0062
IS - 11
M1 - 6217319
ER -