Data-specific mask-guided image reconstruction for diffuse optical tomography.
Data-specific mask-guided image reconstruction for diffuse optical tomography.
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DOI：
10.1364/ao.401132
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发表时间：
2020-10
期刊：
影响因子：
1.9
通讯作者：
Sohail Sabir;Sanghoon Cho;D. Heo;Kee Hyun Kim;Seungryong Cho;R. Pua
中科院分区：
文献类型：
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作者：
Sohail Sabir;Sanghoon Cho;D. Heo;Kee Hyun Kim;Seungryong Cho;R. Pua
在漫反射光学层析成像(DOT)图像重建中，传统的方法通常采用带固定惩罚参数的正则化方法来处理不适定逆问题，从而均匀地平滑解。在这项研究中，我们提出了一种特定于数据的掩模制导方案，该方案将先验掩模约束融入到图像重建框架中。通过利用多测量向量公式，从DOT数据本身创建先前的掩码。相应地，我们提出了两种将先验掩码整合到重建过程中的方法。首先，通过利用空间变化的正则化作为软先验。第二，通过实施有限利益地区重建，作为硬优先事项。此外，后一种方法在离散和连续步骤之间迭代，以分别更新掩模和光学参数。与改进的Levenberg-MarQuardt方法和基于L1正则化的稀疏恢复方法相比，该方法具有更高的光学对比度精度、更高的空间分辨率和更低的噪声水平。
Conventional approaches in diffuse optical tomography (DOT) image reconstruction often address the ill-posed inverse problem via regularization with a constant penalty parameter, which uniformly smooths out the solution. In this study, we present a data-specific mask-guided scheme that incorporates a prior mask constraint into the image reconstruction framework. The prior mask was created from the DOT data itself by exploiting the multi-measurement vector formulation. We accordingly propose two methods to integrate the prior mask into the reconstruction process. First, as a soft prior by exploiting a spatially varying regularization. Second, as a hard prior by imposing a region-of-interest-limited reconstruction. Furthermore, the latter method iterates between discrete and continuous steps to update the mask and optical parameters, respectively. The proposed methods showed enhanced optical contrast accuracy, improved spatial resolution, and reduced noise level in DOT reconstructed images compared with the conventional approaches such as the modified Levenberg-Marquardt approach and the l1-regularization based sparse recovery approach.