TIMER: Tensor Image Morphing for Elastic Registration

Pew Thian Yap, Guorong Wu, Hongtu Zhu, Weili Lin, Dinggang Shen

Research output: Contribution to journalArticle

49 Citations (Scopus)

Abstract

We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006).

Original languageEnglish
Pages (from-to)549-563
Number of pages15
JournalNeuroImage
Volume47
Issue number2
DOIs
Publication statusPublished - 2009 Aug 15
Externally publishedYes

Fingerprint

Diffusion Tensor Imaging
Information Dissemination
Atrophy
Noise

Keywords

  • Diffusion tensor imaging
  • Elastic registration
  • Log-Euclidean manifold
  • Tensor boundaries
  • Tensor regional distributions

ASJC Scopus subject areas

  • Cognitive Neuroscience
  • Neurology

Cite this

TIMER : Tensor Image Morphing for Elastic Registration. / Yap, Pew Thian; Wu, Guorong; Zhu, Hongtu; Lin, Weili; Shen, Dinggang.

In: NeuroImage, Vol. 47, No. 2, 15.08.2009, p. 549-563.

Research output: Contribution to journalArticle

Yap, Pew Thian ; Wu, Guorong ; Zhu, Hongtu ; Lin, Weili ; Shen, Dinggang. / TIMER : Tensor Image Morphing for Elastic Registration. In: NeuroImage. 2009 ; Vol. 47, No. 2. pp. 549-563.
@article{c8ebdbc6d85b4bf880fc75215012b1cc,
title = "TIMER: Tensor Image Morphing for Elastic Registration",
abstract = "We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006).",
keywords = "Diffusion tensor imaging, Elastic registration, Log-Euclidean manifold, Tensor boundaries, Tensor regional distributions",
author = "Yap, {Pew Thian} and Guorong Wu and Hongtu Zhu and Weili Lin and Dinggang Shen",
year = "2009",
month = "8",
day = "15",
doi = "10.1016/j.neuroimage.2009.04.055",
language = "English",
volume = "47",
pages = "549--563",
journal = "NeuroImage",
issn = "1053-8119",
publisher = "Academic Press Inc.",
number = "2",

}

TY - JOUR

T1 - TIMER

T2 - Tensor Image Morphing for Elastic Registration

AU - Yap, Pew Thian

AU - Wu, Guorong

AU - Zhu, Hongtu

AU - Lin, Weili

AU - Shen, Dinggang

PY - 2009/8/15

Y1 - 2009/8/15

N2 - We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006).

AB - We propose a novel diffusion tensor imaging (DTI) registration algorithm, called Tensor Image Morphing for Elastic Registration (TIMER), which leverages the hierarchical guidance of regional distributions and local boundaries, both extracted directly from the tensors. Currently available DTI registration methods generally extract tensor scalar features from each tensor to construct scalar maps. Subsequently, regional integration and other operations such as edge detection are performed to extract more features to guide the registration. However, there are two major limitations with these approaches. First, the computed regional features might not reflect the actual regional tensor distributions. Second, by the same token, gradient maps calculated from the tensor-derived scalar feature maps might not represent the actual tissue tensor boundaries. To overcome these limitations, we propose a new approach which extracts regional and edge information directly from a tensor neighborhood. Regional tensor distribution information, such as mean and variance, is computed in a multiscale fashion directly from the tensors by taking into account the voxel neighborhood of different sizes, and hence capturing tensor information at different scales, which in turn can be employed to hierarchically guide the registration. Such multiscale scheme can help alleviate the problem of local minimum and is also more robust to noise since one can better determine the statistical properties of each voxel by taking into account the properties of its surrounding. Also incorporated in our method is edge information extracted directly from the tensors, which is crucial to facilitate registration of tissue boundaries. Experiments involving real subjects, simulated subjects, fiber tracking, and atrophy detection indicate that TIMER performs better than the other methods (Yang et al., 2008; Zhang et al., 2006).

KW - Diffusion tensor imaging

KW - Elastic registration

KW - Log-Euclidean manifold

KW - Tensor boundaries

KW - Tensor regional distributions

UR - http://www.scopus.com/inward/record.url?scp=67349227431&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=67349227431&partnerID=8YFLogxK

U2 - 10.1016/j.neuroimage.2009.04.055

DO - 10.1016/j.neuroimage.2009.04.055

M3 - Article

C2 - 19398022

AN - SCOPUS:67349227431

VL - 47

SP - 549

EP - 563

JO - NeuroImage

JF - NeuroImage

SN - 1053-8119

IS - 2

ER -