AN OVER-COMPLETE DICTIONARY BASED REGULARIZED RECONSTRUCTION OF A FIELD OF ENSEMBLE AVERAGE PROPAGATORS.
AN OVER-COMPLETE DICTIONARY BASED REGULARIZED RECONSTRUCTION OF A FIELD OF ENSEMBLE AVERAGE PROPAGATORS.
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DOI：
10.1109/isbi.2012.6235711
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发表时间：
2012-07-12
期刊：
影响因子：
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通讯作者：
Entezari A
中科院分区：
文献类型：
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作者：
Ye W;Vemuri BC;Entezari A
在本文中，我们提出了一个基于字典的框架重建的领域的合奏平均传播（EAP），给出了一个高角分辨率的扩散MRI数据集。现有技术通常考虑EAP场的逐体素重建，从而导致跨场的噪声重建。我们提出了一个字典学习框架，用于在整个领域实现平滑的EAP重建，其中，字典原子通过使用自适应样条核的初始回归从数据中学习。该公式涉及两个阶段的优化，其中第一阶段涉及使用基于K-SVD的更新来优化稀疏字典，第二阶段涉及使用跨场的基于非局部均值的正则化的二次成本函数优化。新颖之处在于基于字典的重建以及基于NLM的正则化，有助于保留重建字段中的特征。我们记录的实验结果的合成数据交叉纤维和真实的视交叉数据集，证明了所提出的方法的优点。
In this paper we present a dictionary-based framework for the reconstruction of a field of ensemble average propagators (EAPs), given a high angular resolution diffusion MRI data set. Existing techniques often consider voxel-wise reconstruction of the EAP field thereby leading to a noisy reconstruction across the field. We present a dictionary learning framework for achieving a smooth EAP reconstruction across the field wherein, the dictionary atoms are learned from the data via an initial regression using adaptive spline kernels. The formulation involves a two stage optimization where the first stage involves optimizing for a sparse dictionary using a K-SVD based updating and the second stage involves a quadratic cost function optimization with a non-local means based regularization across the field. The novelty lies in a dictionary based reconstruction as well as an NLM-based regularization that helps preserving features in the reconstructed field. We document experimental results on synthetic data from crossing fibers and real optic chiasm data set that demonstrate the advantages of the proposed approach.