Clinical DT-MRI estimation, smoothing, and fiber tracking with log-euclidean metrics
Clinical DT-MRI estimation, smoothing, and fiber tracking with log-euclidean metrics
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DOI：
10.1109/tmi.2007.899173
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发表时间：
2007-11-01
影响因子：
10.6
通讯作者：
Ayache, Nicholas
中科院分区：
文献类型：
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作者：
Fillard, Pierre;Pennec, Xavier;Ayache, Nicholas
扩散张量磁共振成像（DT-MRI或DTI）是一种在临床应用中日益重要的成像方式。然而，在临床环境中，必须快速采集数据，这通常是以牺牲图像质量为代价的。这通常会导致DTI数据集不适合复杂的后处理，如纤维跟踪。我们提出了一个新的变分框架，以提高在这种临床背景下的DT-MRI的估计。大多数现有的估计方法依赖于对数高斯噪声（图像上的高斯噪声）或高斯噪声，其不反映具有低信噪比（SNR）的MR图像中的噪声的Rician性质。使用这些方法，Rician噪声引起收缩效应：当使用其他噪声模型进行估计时，张量体积被低估。在本文中，我们提出了一个最大似然策略，充分利用了假设的Rician噪声。为了进一步减少噪声的影响，我们最佳地利用空间相关性耦合的估计与各向异性先前提出的张量场本身的空间规律性，这导致在最大后验估计。优化这样的非线性标准需要适应张量计算的工具。我们表明，黎曼度量张量，更具体地说，对数欧几里德度量，是一个很好的候选人，这个标准可以有效地优化。对合成数据的实验表明，即使在信噪比很低的情况下，该方法也能正确地处理收缩效应，并始终保证张量的正定性。真实的临床数据的结果证明了所提出的方法的真实性，并显示出有前途的改善纤维跟踪在大脑和脊髓。
Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly often at the expense of the image quality. This often results in DTI datasets that are not suitable for complex postprocessing like fiber tracking. We propose a new variational framework to improve the estimation of DT-MRI in this clinical context. Most of the existing estimation methods rely on a log-Gaussian noise (Gaussian noise on the image logarithms), or a Gaussian noise, that do not reflect the Rician nature of the noise in MR images with a low signal-to-noise ratio (SNR). With these methods, the Rician noise induces a shrinking effect: the tensor volume is underestimated when other noise models are used for the estimation. In this paper, we propose a maximum likelihood strategy that fully exploits the assumption of a Rician noise. To further reduce the influence of the noise, we optimally exploit the spatial correlation by coupling the estimation with an anisotropic prior previously proposed on the spatial regularity of the tensor field itself, which results in a maximum a posteriori estimation. Optimizing such a nonlinear criterion requires adapted tools for tensor computing. We show that Riemannian metrics for tensors, and more specifically the log-Euclidean metrics, are a good candidate and that this criterion can be efficiently optimized. Experiments on synthetic data show that our method correctly handles the shrinking effect even with very low SNR, and that the positive definiteness of tensors is always ensured. Results on real clinical data demonstrate the truthfulness of the proposed approach and show promising improvements of fiber tracking in the brain and the spinal cord.