期刊文献+

Diffusion tensor interpolation profile control using non-uniform motion on a Riemannian geodesic 被引量:2

Diffusion tensor interpolation profile control using non-uniform motion on a Riemannian geodesic
原文传递
导出
摘要 Tensor interpolation is a key step in the processing algorithms of diffusion tensor imaging (DTI), such as registration and tractography. The diffusion tensor (DT) in biological tissues is assumed to be positive definite. However, the tensor interpolations in most clinical applications have used a Euclidian scheme that does not take this assumption into account. Several Rie-mannian schemes were developed to overcome this limitation. Although each of the Riemannian schemes uses different metrics, they all result in a ‘fixed’ interpolation profile that cannot adapt to a variety of diffusion patterns in biological tissues. In this paper, we propose a DT interpolation scheme to control the interpolation profile, and explore its feasibility in clinical applications. The profile controllability comes from the non-uniform motion of interpolation on the Riemannian geodesic. The interpolation experiment with medical DTI data shows that the profile control improves the interpolation quality by assessing the reconstruction errors with the determinant error, Euclidean norm, and Riemannian norm. Tensor interpolation is a key step in the processing algorithms of diffusion tensor imaging (DTI), such as registration and tractography. The diffusion tensor (DT) in biological tissues is assumed to be positive definite. However, the tensor interpo- lations in most clinical applications have used a Euclidian scheme that does not take this assumption into account. Several Rie- mannian schemes were developed to overcome this limitation. Although each of the Riemannian schemes uses different metrics, they all result in a 'fixed' interpolation profile that cannot adapt to a variety of diffusion patterns in biological tissues. In this paper, we propose a DT interpolation scheme to control the interpolation profile, and explore its feasibility in clinical applications. The profile controllability comes from the non-uniform motion of interpolation on the Riemannian geodesic. The interpolation experiment with medical DTI data shows that the profile control improves the interpolation quality by assessing the reconstruction errors with the determinant error, Euclidean norm, and Riemannian norm.
出处 《Journal of Zhejiang University-Science C(Computers and Electronics)》 SCIE EI 2012年第2期90-98,共9页 浙江大学学报C辑(计算机与电子(英文版)
基金 Project (No. 60772092) supported by the National Natural Science Foundation of China
关键词 数据插值 扩散张量 匀速运动 黎曼 生物组织 欧几里德 临床应用 DTI Diffusion tensor (DT), DT imaging (DTI), DT interpolation, Interpolation profile control, Riemannian geodesic
  • 相关文献

参考文献21

  • 1Alexander, D.C., Barker, GJ., 2005. Optimal imaging pa?rameters for fiber-orientation estimation in diffusion MRI. NeuroImage, 27(2):357-367. [doi:10.1016/j.neuroimage. 2005.04.008] Alexander, D.C., Pierpaoli, C., Basser, P.J., Gee, lC., 2001.
  • 2Spatial transformations of diffusion tensor MR images. IEEE Trans. Med. Imag., 20(11): 1131-1139. [doi:10. 1109/42.963816].
  • 3Arsigny, v.. Fillard, P., Pennec, X., Ayache, N., 2006. Log-Euclidean metrics for fast and simple calculus on diffusion tensors. Magn. Reson. Med., 56(2):411-421. [doi:1 0.1 002/mrm.20965].
  • 4Bansal, R., Staib, L.H., Xu, D.R., Laine, A.F., Royal, r, Pe?terson, B.S., 2008. Using perturbation theory to compute the morphological similarity of diffusion tensors. IEEE Trans. Med. Imag., 27(5):589-607. [doi:10.1109ITMI. 2007.912391].
  • 5Basser, P.J., Mattiello, J., Bihan, D.L., 1994. MR diffusion tensor spectroscopy and imaging. Biophys. J, 66(1 ):259- 267. [doi:10.1016/S0006-3495(94)80775-1].
  • 6Batchelor, P.G., Moakher, M., Atkinson, D., Calamante, F., Connelly, A., 2005. A rigorous framework for diffusion tensor calculus. Magn. Reson. Med., 53(1):221-225. [doi: 1 0.1 002/mrm.20334].
  • 7Chefd'Hotel, c., Tschumperle, D., Deriche, R., Faugeras, 0., 2004. Regularizing flows for constrained matrix-valued images. J Math. Imag. Vis.,20(l-2):147-162. [doi:10. 1023/B:JMIV.0000011920.58935.9c].
  • 8Filley, C.M., 2001. The Behavioral Neurology of White Matter. Oxford University Press, New York, p.299.
  • 9Fletcher, P.T, Sarang, J., 2007. Riemannian geometry for the statistical analysis of diffusion tensor data. Signal Proc?ess., 87(2):250-262. [doi: 1 0.1 016/j.sigpro.2005.12.018].
  • 10Hoptrnan, MJ., Nierenberg, L, Bertisch, H.C., Catalano, D., Ardekani, B.A., Branch, c.x., DeLisi, L.E., 2008. A DTI study of white matter microstructure in individuals at high genetic risk for schizophrenia. Schizophr. Res., 106(2-3): 115-124. [doi:10.1016/j.schres.2008.07.023].

同被引文献7

引证文献2

二级引证文献5

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部