Simultaneous Tensor and Fiber Registration (STFR) for Diffusion Tensor Images of the Brain
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- 作者：Zhong Xue (21)
Stephen T. C. Wong (21) - 刊名：Lecture Notes in Computer Science
- 出版年：2013
- 出版时间：2013
- 年：2013
- 卷：8090
- 期：1
- 页码：9-18
- 全文大小：956KB
- 参考文献：1. Eluvathingal, T.J., Chugani, H.T., Behen, M.E., Juhasz, C., Muzik, O., Maqbool, M., Chugani, D.C., Makki, M.: Abnormal brain connectivity in children after early severe socioemotional deprivation: A DTI study. Pediatrics聽117, 2093鈥?100 (2006) CrossRef
2. Keller, T.A., Kana, R.K., Just, M.A.: A developmental study of the structural integrity of white matter in autism. Neuroreport聽18, 23鈥?7 (2007) CrossRef
3. Yang, J., Shen, D., Davatzikos, C., Verma, R.: Diffusion tensor image registration using tensor geometry and orientation features. In: Metaxas, D., Axel, L., Fichtinger, G., Sz茅kely, G. (eds.) MICCAI 2008, Part II. LNCS, vol.聽5242, pp. 905鈥?13. Springer, Heidelberg (2008) CrossRef
4. Wang, Y., Gupta, A., Liu, Z., Zhang, H., Escolar, M.L., Gilmore, J.H., Gouttard, S., Fillard, P., Maltbie, E., Gerig, G., Styner, M.: DTI registration in atlas based fiber analysis of infantile Krabbe disease. Neuroimage聽55, 1577鈥?586 (2011) CrossRef
5. Zhang, H., Avants, B.B., Yushkevich, P.A., Woo, J.H., Wang, S., McCluskey, L.F., Elman, L.B., Melhem, E.R., Gee, J.C.: High-dimensional spatial normalization of diffusion tensor images improves the detection of white matter differences: An example study using amyotrophic lateral sclerosis. IEEE Trans. Med. Imaging聽26, 1585鈥?597 (2007) CrossRef
6. Ziyan, U., Sabuncu, M.R., O鈥橠onnell, L.J., Westin, C.-F.: Nonlinear registration of diffusion MR images based on fiber bundles. In: Ayache, N., Ourselin, S., Maeder, A. (eds.) MICCAI 2007, Part I. LNCS, vol.聽4791, pp. 351鈥?58. Springer, Heidelberg (2007) CrossRef
7. Xue, Z., Li, H., Guo, L., Wong, S.T.: A local fast marching-based diffusion tensor image registration algorithm by simultaneously considering spatial deformation and tensor orientation. Neuroimage聽52, 119鈥?30 (2010) CrossRef
8. Yeo, B.T., Vercauteren, T., Fillard, P., Peyrat, J.M., Pennec, X., Golland, P., Ayache, N., Clatz, O.: DT-REFinD: diffusion tensor registration with exact finite-strain differential. IEEE Trans. Med. Imaging聽28, 1914鈥?928 (2009) CrossRef
9. Wang, Q., Yap, P.-T., Wu, G., Shen, D.: Diffusion tensor image registration with combined tract and tensor features. In: Fichtinger, G., Martel, A., Peters, T. (eds.) MICCAI 2011, Part II. LNCS, vol.聽6892, pp. 200鈥?08. Springer, Heidelberg (2011) CrossRef
10. Alexander, D.C., Gee, J.C., Bajcsy, R.: Similarity measures for matching diffusion tensor images. In: British Machine Vision Conference, pp. 93鈥?02 (1999)
11. Karacali, B., Davatzikos, C.: Estimating topology preserving and smooth displacement fields. IEEE Trans. Med. Imaging聽23, 868鈥?80 (2004) CrossRef
12. Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, J.C.: Spatial transformations of diffusion tensor magnetic resonance images. IEEE Trans. Med. Imaging聽20, 1131鈥?139 (2001) CrossRef
13. Xue, Z., Shen, D., Davatzikos, C.: Statistical representation of high-dimensional deformation fields with application to statistically constrained 3D warping. Med. Image Anal.聽10, 740鈥?51 (2006) CrossRef - 作者单位：Zhong Xue (21)
Stephen T. C. Wong (21)
21. The Methodist Hospital Research Institute, Weill Cornell Medical College, Cornell University, Houston, Texas, USA - ISSN：1611-3349
文摘
Accurate registration of diffusion tensor imaging (DTI) data of the brain among different subjects facilitates automatic normalization of structural and neural connectivity information and helps quantify white matter fiber tract differences between normal and disease. Traditional DTI registration methods use either tensor information or orientation invariant features extracted from the tensors. Because tensors need to be re-oriented after warping, fibers extracted from the deformed DTI often suffer from discontinuity, indicating lack of fiber information preservation after registration. To remedy this problem and to improve the accuracy of DTI registration, in this paper, we introduce a simultaneous tensor and fiber registration (STFR) algorithm by matching both tensor and fiber tracts at each voxel and considering re-orientation with deformation simultaneously. Because there are multiple fiber tracts passing through each voxel, which may have different orientations such as fiber crossing, incorporating fiber information can preserve fiber information better than only using the tensor information. Additionally, fiber tracts also reflect the spatial neighborhood of each voxel. After implementing STFR, we compared the registration performance with the current state-of-the art tensor-based registration algorithm (called DTITK) using both simulated images and real images. The results showed that the proposed STFR algorithm evidently outperforms DTITK in terms of registration accuracy. Finally, using statistical parametric mapping (SPM) package, we illustrate that after normalizing the fractional anisotropy (FA) maps of both traditional developing (TD) and Autism spectrum disorder (ASD) subjects to a randomly selected template space, regions with significantly different FA highlighted by STFR are with less noise or false positive regions as compared with DTITK. STFR methodology can also be extended to high-angular-resolution diffusion imaging and Q-ball vector analysis.