Nonrigid coregistration of diffusion tensor images using a viscous fluid model and mutual information.
In this paper, a nonrigid coregistration algorithm based on a viscous fluid model is proposed that has been optimized for diffusion tensor images (DTI), in which image correspondence is measured by the mutual information criterion. Several coregistration strategies are introduced and evaluated both on simulated data and on brain intersubject DTI data. Two tensor reorientation methods have been incorporated and quantitatively evaluated. Simulation as well as experimental results show that the proposed viscous fluid model can provide a high coregistration accuracy, although the tensor reorientation was observed to be highly sensitive to the local deformation field. Nevertheless, this coregistration method has demonstrated to significantly improve spatial alignment compared to affine image matching.
Van Hecke
Wim
W
VisionLab, Department of Physics, University of Antwerp, 2610 Antwerp, Belgium.
Leemans
Alexander
A
D'Agostino
Emiliano
E
De Backer
Steve
S
Vandervliet
Evert
E
Parizel
Paul M
PM
Sijbers
Jan
J
D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D001834:Body Water / Q000378:metabolism; D001921:Brain / Q000033:anatomy & histology*; D003198:Computer Simulation; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008297:Male; D008875:Middle Aged; D008959:Models, Neurological; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique; D014783:Viscosity