Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization.
Multimodal Image Reconstruction Using Supplementary Structural Information in Total Variation Regularization.
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DOI：
10.1007/s11220-014-0097-5
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发表时间：
2014
影响因子：
2.2
通讯作者：
Lee PD
中科院分区：
文献类型：
--
作者：
Kazantsev D;Lionheart WR;Withers PJ;Lee PD
在本文中，我们提出了一种迭代重建算法，该算法利用使用一种模态收集的数据集中的可用信息来提高使用另一种模态收集的数据集的分辨率和信噪比。该方法仅对结构信息进行操作，从而增加了其在各种应用中的适用性。因此，该方法的主要目的是在正则化框架内利用可用的补充数据。主要数据集和补充数据集的来源可以通过互补成像模式获得，其中可以获得不同类型的信息（例如在医学成像中：解剖和功能）。结果表明，从补充图像中提取结构信息（水平集方向）可以提高另一图像的分辨率。值得注意的是，该方法增强了两幅图像共同的边缘，而不抑制仅在主图像中显示高对比度的特征。在我们的迭代算法中，我们在修改的总变异惩罚项中使用可用的结构信息。通过数值实验证明了该方法的优越性和可行性。
In this paper, we propose an iterative reconstruction algorithm which uses available information from one dataset collected using one modality to increase the resolution and signal-to-noise ratio of one collected by another modality. The method operates on the structural information only which increases its suitability across various applications. Consequently, the main aim of this method is to exploit available supplementary data within the regularization framework. The source of primary and supplementary datasets can be acquired using complementary imaging modes where different types of information are obtained (e.g. in medical imaging: anatomical and functional). It is shown by extracting structural information from the supplementary image (direction of level sets) one can enhance the resolution of the other image. Notably, the method enhances edges that are common to both images while not suppressing features that show high contrast in the primary image alone. In our iterative algorithm we use available structural information within a modified total variation penalty term. We provide numerical experiments to show the advantages and feasibility of the proposed technique in comparison to other methods.