Frindel Carole, Robini Marc, Croisille Pierre, Zhu Yue-Min
CREATIS-LRMN, CNRS UMR 5220, INSERM U630, INSA of Lyon and University of Lyon1, Villeurbanne, France.
Med Image Anal. 2009 Jun;13(3):405-18. doi: 10.1016/j.media.2009.01.002. Epub 2009 Jan 20.
Diffusion tensor MRI (DT-MRI) is an imaging technique that is gaining importance in clinical applications. However, there is very little work concerning the human heart. When applying DT-MRI to in vivo human hearts, the data have to be acquired rapidly to minimize artefacts due to cardiac and respiratory motion and to improve patient comfort, often at the expense of image quality. This results in diffusion weighted (DW) images corrupted by noise, which can have a significant impact on the shape and orientation of tensors and leads to diffusion tensor (DT) datasets that are not suitable for fibre tracking. This paper compares regularization approaches that operate either on diffusion weighted images or on diffusion tensors. Experiments on synthetic data show that, for high signal-to-noise ratio (SNR), the methods operating on DW images produce the best results; they substantially reduce noise error propagation throughout the diffusion calculations. However, when the SNR is low, Rician Cholesky and Log-Euclidean DT regularization methods handle the bias introduced by Rician noise and ensure symmetry and positive definiteness of the tensors. Results based on a set of sixteen ex vivo human hearts show that the different regularization methods tend to provide equivalent results.
扩散张量磁共振成像（DT-MRI）是一种在临床应用中日益重要的成像技术。然而，关于人类心脏的相关研究却非常少。将DT-MRI应用于活体人类心脏时，必须快速采集数据，以尽量减少由于心脏和呼吸运动产生的伪影，并提高患者舒适度，而这往往是以牺牲图像质量为代价的。这会导致扩散加权（DW）图像被噪声干扰，这可能会对张量的形状和方向产生重大影响，并导致不适用于纤维追踪的扩散张量（DT）数据集。本文比较了对扩散加权图像或扩散张量进行操作的正则化方法。对合成数据的实验表明，对于高信噪比（SNR），对DW图像进行操作的方法能产生最佳结果；它们在整个扩散计算过程中能大幅减少噪声误差传播。然而，当SNR较低时，莱斯分布的乔列斯基分解和对数欧几里得DT正则化方法能处理莱斯噪声引入的偏差，并确保张量的对称性和正定特性。基于一组16个离体人类心脏的结果表明，不同的正则化方法往往能提供等效的结果。
