DTI Image Registration under Probabilistic Fiber Bundles Tractography Learning.

Zhe Guo,Xiuwei Zhang,Tao Lei,Yi Wang,Yangyu Fan

doi:10.1155/2016/4674658

Zhe Guo, Xiuwei Zhang + Show 3 more

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https://doi.org/10.1155/2016/4674658

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Abstract

Diffusion Tensor Imaging (DTI) image registration is an essential step for diffusion tensor image analysis. Most of the fiber bundle based registration algorithms use deterministic fiber tracking technique to get the white matter fiber bundles, which will be affected by the noise and volume. In order to overcome the above problem, we proposed a Diffusion Tensor Imaging image registration method under probabilistic fiber bundles tractography learning. Probabilistic tractography technique can more reasonably trace to the structure of the nerve fibers. The residual error estimation step in active sample selection learning is improved by modifying the residual error model using finite sample set. The calculated deformation field is then registered on the DTI images. The results of our proposed registration method are compared with 6 state-of-the-art DTI image registration methods under visualization and 3 quantitative evaluation standards. The experimental results show that our proposed method has a good comprehensive performance.

Highlights

Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging (MRI) technique which measures diffusion properties of water molecules in tissue to gained neural bundle images, which cannot be obtained by other imaging modalities [1]
In order to test the performance of the proposed registration method based on probabilistic fiber bundles tractography learning, in this paper, we compare our method with 6 stateof-the-art methods, which are 5 scalar based methods: Rigid [13], Affine [18], Elastic [14], SyN [26], FSL [17], and one tensor based method: DTI-TK [3]
The result of fiber bundles tracking will affect the accuracy of the following registration method as tracking result is the input of registration step; as a result, fiber bundles tracking is an important step in the whole algorithm system

Summary

Introduction

Diffusion Tensor Imaging (DTI) is a Magnetic Resonance Imaging (MRI) technique which measures diffusion properties of water molecules in tissue to gained neural bundle images, which cannot be obtained by other imaging modalities [1]. DTI image registration is an essential step for diffusion tensor image analysis. DTI registration is involved in many clinical diagnoses of disease diffusion tensor image analysis; all need image registration techniques [3]. For ordinary medical image registration, the corresponding points of two images will be transformed to have the exact consistency on the space position and the anatomical structure by space transformation; the registration process is essentially a multiparameter optimization problem [4]. Tensor image registration will encounter many ordinary medical image registration problems and includes some special difficulties due to the particularity of the DTI data

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