Ye Chuyang, Prince Jerry L
National Laboratory of Pattern Recognition & Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD, USA.
Med Image Comput Comput Assist Interv. 2017 Sep;10433:575-583. doi: 10.1007/978-3-319-66182-7_66. Epub 2017 Sep 4.
(dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.
扩散磁共振成像（dMRI）是目前唯一用于对大脑白质束进行无创成像的工具。纤维方向（FO）是从dMRI计算得出的用于束重建的关键特征。由于体素中的纤维方向数量通常较少，基于字典的稀疏重建已被用于估计纤维方向。然而，在存在噪声的情况下准确估计复杂的纤维方向配置仍然具有挑战性。在这项工作中，我们探索在基于字典的框架中使用深度网络进行纤维方向估计，并提出一种名为纤维方向估计深度网络（FORDN）的算法。FORDN由两个步骤组成。首先，我们使用一个较小的字典对粗略的基本纤维方向进行编码以表示扩散信号。为了估计字典原子的混合分数，设计了一个深度网络来解决稀疏重建问题。其次，粗略的纤维方向为最终的纤维方向估计提供信息，在此过程中使用一个较大的字典对密集的纤维方向基进行编码，并解决一个加权 -范数正则化最小二乘问题，以鼓励与网络输出一致的纤维方向。FORDN在模拟和典型临床dMRI数据上进行了评估，并与使用稀疏重建估计纤维方向的现有最先进算法进行了比较。结果证明了使用深度网络进行纤维方向估计的优势。
