Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction

Wei Liu, Xiaozheng Liu, Xiaofu He, Zhenyu Zhou, Ying Wen, Yongdi Zhou, Bradley S. Peterson, Dongrong Xu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a reconstructed diffusion gradient (RDG) method for spatial normalization of diffusion tensor imaging (DTI) data that warps the raw imaging data and then estimates the associated gradient direction for reconstruction of normalized DTI in the template space. The RDG method adopts the backward mapping strategy for DTI normalization, with a specially designed approach to reconstruct a specific gradient direction in combination with the local deformation force. The method provides a voxel-based strategy to make the gradient direction align with the raw diffusion weighted imaging (DWI) volumes, ensuring correct estimation of the tensors in the warped space and thereby retaining the orientation information of the underlying structure. Compared with the existing tensor reorientation methods, experiments using both simulated and human data demonstrated that the RDG method provided more accurate tensor information. Our method can properly estimate the gradient direction in the template space that has been changed due to image transformation, and subsequently use the warped imaging data to directly reconstruct the warped tensor field in the template space, achieving the same goal as directly warping the tensor image. Moreover, the RDG method also can be used to spatially normalize data using the Q-ball imaging (QBI) model.

Original languageEnglish (US)
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Pages134-146
Number of pages13
Volume7509 LNCS
DOIs
StatePublished - 2012
Externally publishedYes
Event2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012 - Nice, France
Duration: Oct 1 2012Oct 5 2012

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume7509 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349

Other

Other2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012
CountryFrance
CityNice
Period10/1/1210/5/12

Fingerprint

Voxel
Gradient methods
Normalization
Tensors
Diffusion tensor imaging
Tensor
Gradient
Imaging
Gradient Method
Imaging techniques
Template
Image Transformation
Normalize
Warping
Estimate
Ball
Experiments
Experiment

Keywords

  • backward mapping
  • diffusion weighed imaging
  • tensor reorientation

ASJC Scopus subject areas

  • Computer Science(all)
  • Theoretical Computer Science

Cite this

Liu, W., Liu, X., He, X., Zhou, Z., Wen, Y., Zhou, Y., ... Xu, D. (2012). Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7509 LNCS, pp. 134-146). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS). https://doi.org/10.1007/978-3-642-33530-3_11

Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. / Liu, Wei; Liu, Xiaozheng; He, Xiaofu; Zhou, Zhenyu; Wen, Ying; Zhou, Yongdi; Peterson, Bradley S.; Xu, Dongrong.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS 2012. p. 134-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7509 LNCS).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Liu, W, Liu, X, He, X, Zhou, Z, Wen, Y, Zhou, Y, Peterson, BS & Xu, D 2012, Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). vol. 7509 LNCS, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7509 LNCS, pp. 134-146, 2nd International Workshop on Multimodal Brain Image Analysis, MBIA 2012, Held in Conjunction with the 15th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2012, Nice, France, 10/1/12. https://doi.org/10.1007/978-3-642-33530-3_11
Liu W, Liu X, He X, Zhou Z, Wen Y, Zhou Y et al. Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS. 2012. p. 134-146. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-642-33530-3_11
Liu, Wei ; Liu, Xiaozheng ; He, Xiaofu ; Zhou, Zhenyu ; Wen, Ying ; Zhou, Yongdi ; Peterson, Bradley S. ; Xu, Dongrong. / Spatial normalization of diffusion tensor images with voxel-wise reconstruction of the diffusion gradient direction. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Vol. 7509 LNCS 2012. pp. 134-146 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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