Varadarajan Divya, Haldar Justin P
Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089.
Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:743-746. doi: 10.1109/ISBI.2018.8363680. Epub 2018 May 24.
The estimation of orientation distribution functions (ODFs) from diffusion MRI data is an important step in diffusion tractography, but existing estimation methods often depend on signal modeling assumptions that are violated by real data, lack theoretical characterization, and/or are only applicable to a small range of q-space sampling patterns. As a result, existing ODF estimation methods may be suboptimal. In this work, we propose a novel ODF estimation approach that learns a linear ODF estimator from training data. The training set contains ideal data samples paired with corresponding ideal ODFs, and the learning procedure reduces to a simple linear least-squares problem. This approach can accommodate arbitrary q-space sampling schemes, can be characterized theoretically, and is theoretically demonstrated to generalize far beyond the training set. The proposed approach is evaluated with simulated and diffusion data, where it is demonstrated to outperform common alternatives.
从扩散磁共振成像（MRI）数据估计方向分布函数（ODF）是扩散纤维束成像中的重要步骤，但现有的估计方法通常依赖于信号建模假设，而这些假设会被真实数据所违背，缺乏理论表征，和/或仅适用于小范围的q空间采样模式。因此，现有的ODF估计方法可能并非最优。在这项工作中，我们提出了一种新颖的ODF估计方法，该方法从训练数据中学习线性ODF估计器。训练集包含与相应理想ODF配对的理想数据样本，并且学习过程简化为一个简单的线性最小二乘问题。这种方法可以适应任意的q空间采样方案，具有理论表征，并且在理论上被证明可以广泛推广到训练集之外。我们用模拟数据和扩散数据对所提出的方法进行了评估，结果表明它优于常见的替代方法。
