Principal Warps: Thin-Plate Splines and the Decomposition of Deformations
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Versatile Support for Binding Native Code to Java
HPCN Europe 2000 Proceedings of the 8th International Conference on High-Performance Computing and Networking
A new point matching algorithm for non-rigid registration
Computer Vision and Image Understanding - Special issue on nonrigid image registration
Diffusion MRI Registration Using Orientation Distribution Functions
IPMI '09 Proceedings of the 21st International Conference on Information Processing in Medical Imaging
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Fast Tensor Image Morphing for Elastic Registration
MICCAI '09 Proceedings of the 12th International Conference on Medical Image Computing and Computer-Assisted Intervention: Part I
Registration of high angular resolution diffusion MRI images using 4th order tensors
MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I
Demons registration of high angular resolution diffusion images
ISBI'10 Proceedings of the 2010 IEEE international conference on Biomedical imaging: from nano to Macro
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In contrast to the more common Diffusion Tensor Imaging (DTI), High Angular Resolution Diffusion Imaging (HARDI) allows superior delineation of angular microstructures of brain white matter, and makes possible multiple-fiber modeling of each voxel for better characterization of brain connectivity. However, in the context of image registration, the question of how much information is needed for satisfactory alignment remains unanswered. Low order representation of the diffusivity information is generally more robust than the higher order representation, but the latter gives more information for correct fiber tract alignment. However, higher order representation, when naïvely utilized, might not necessarily be conducive to improving registration accuracy since similar structures with significant orientation differences prior to proper alignment might be mistakenly taken as non-matching structures. We propose in this paper a hierarchical spherical harmonics based registration algorithm which utilizes the wealth of information provided by HARDI in a more principled means. The image volumes are first registered using robust, relatively direction invariant features derived from the diffusion-attenuation profile, and their alignment is then refined using spherical harmonic (SH) representation of gradually increasing order. This progression of SH representation from non-directional, single-directional to multi-directional representation provides a systematic means of extracting directional information from the HARDI data. Experimental results show a significant increase in registration accuracy over a state-of-the-art DTI registration algorithm.