Diffusion tensor image registration using hybrid connectivity and tensor features

Qian Wang, Pew Thian Yap, Guorong Wu, Dinggang Shen

Research output: Contribution to journalArticle

4 Citations (Scopus)

Abstract

Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Hum Brain Mapp 35:3529-3546, 2014.

Original languageEnglish
Pages (from-to)3529-3546
Number of pages18
JournalHuman Brain Mapping
Volume35
Issue number7
DOIs
Publication statusPublished - 2014 Jan 1

Fingerprint

Diffusion Tensor Imaging
Brain
Anatomy

ASJC Scopus subject areas

  • Clinical Neurology
  • Anatomy
  • Neurology
  • Radiology Nuclear Medicine and imaging
  • Radiological and Ultrasound Technology

Cite this

Diffusion tensor image registration using hybrid connectivity and tensor features. / Wang, Qian; Yap, Pew Thian; Wu, Guorong; Shen, Dinggang.

In: Human Brain Mapping, Vol. 35, No. 7, 01.01.2014, p. 3529-3546.

Research output: Contribution to journalArticle

Wang, Qian ; Yap, Pew Thian ; Wu, Guorong ; Shen, Dinggang. / Diffusion tensor image registration using hybrid connectivity and tensor features. In: Human Brain Mapping. 2014 ; Vol. 35, No. 7. pp. 3529-3546.
@article{21eb002b3a2a446ba7d31ef1bf87ccc0,
title = "Diffusion tensor image registration using hybrid connectivity and tensor features",
abstract = "Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Hum Brain Mapp 35:3529-3546, 2014.",
keywords = "Connectivity features, Correspondence detection, Diffusion tensor image registration, Tensor features",
author = "Qian Wang and Yap, {Pew Thian} and Guorong Wu and Dinggang Shen",
year = "2014",
month = "1",
day = "1",
doi = "10.1002/hbm.22419",
language = "English",
volume = "35",
pages = "3529--3546",
journal = "Human Brain Mapping",
issn = "1065-9471",
publisher = "Wiley-Liss Inc.",
number = "7",

}

TY - JOUR

T1 - Diffusion tensor image registration using hybrid connectivity and tensor features

AU - Wang, Qian

AU - Yap, Pew Thian

AU - Wu, Guorong

AU - Shen, Dinggang

PY - 2014/1/1

Y1 - 2014/1/1

N2 - Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Hum Brain Mapp 35:3529-3546, 2014.

AB - Most existing diffusion tensor imaging (DTI) registration methods estimate structural correspondences based on voxelwise matching of tensors. The rich connectivity information that is given by DTI, however, is often neglected. In this article, we propose to integrate complementary information given by connectivity features and tensor features for improved registration accuracy. To utilize connectivity information, we place multiple anchors representing different brain anatomies in the image space, and define the connectivity features for each voxel as the geodesic distances from all anchors to the voxel under consideration. The geodesic distance, which is computed in relation to the tensor field, encapsulates information of brain connectivity. We also extract tensor features for every voxel to reflect the local statistics of tensors in its neighborhood. We then combine both connectivity features and tensor features for registration of tensor images. From the images, landmarks are selected automatically and their correspondences are determined based on their connectivity and tensor feature vectors. The deformation field that deforms one tensor image to the other is iteratively estimated and optimized according to the landmarks and their associated correspondences. Experimental results show that, by using connectivity features and tensor features simultaneously, registration accuracy is increased substantially compared with the cases using either type of features alone. Hum Brain Mapp 35:3529-3546, 2014.

KW - Connectivity features

KW - Correspondence detection

KW - Diffusion tensor image registration

KW - Tensor features

UR - http://www.scopus.com/inward/record.url?scp=84902154609&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84902154609&partnerID=8YFLogxK

U2 - 10.1002/hbm.22419

DO - 10.1002/hbm.22419

M3 - Article

C2 - 24293159

AN - SCOPUS:84902154609

VL - 35

SP - 3529

EP - 3546

JO - Human Brain Mapping

JF - Human Brain Mapping

SN - 1065-9471

IS - 7

ER -