IBBT-VisionLab, Department of Physics, Universiteit Antwerpen, Antwerpen, Wilrijk, 2610, Belgium.
Magn Reson Imaging. 2010 Dec;28(10):1497-506. doi: 10.1016/j.mri.2010.06.018. Epub 2010 Sep 15.
Diffusion weighted images (DWI), from which the corresponding diffusion tensor images (DTI) are estimated, are commonly acquired with anisotropic discretizations. Traditional methods to up-sample diffusion weighted images generally rely on scene-based interpolation and do not exploit structural information from the images. In this study, a DTI up-sampling framework is presented that incorporates the underlying anatomical shape information by means of non-rigid inter-slice registration. A strategy is proposed to reorient the interpolated tensor in order to maintain its proper orientation. Tests on phantom as well as on real data sets show that the proposed method is able to produce better results compared to scene based interpolation methods in terms of the accuracy of DWI/DTI interpolation, especially when diffusion tensor orientation is taken into account.
弥散加权图像(DWI)可从中估计出相应的弥散张量图像(DTI),通常采用各向异性离散化方法获取。传统的上采样弥散加权图像的方法通常依赖于基于场景的插值,而没有利用图像的结构信息。在这项研究中,提出了一种 DTI 上采样框架,通过非刚性切片间配准来纳入潜在的解剖形状信息。提出了一种策略来重新定向插值张量,以保持其正确的方向。在体模和真实数据集上的测试表明,与基于场景的插值方法相比,该方法在 DWI/ DTI 插值的准确性方面能够产生更好的结果,尤其是在考虑扩散张量方向的情况下。