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Gaussian kernel based anatomically-aided diffuse optical tomography reconstruction

机译：基于高斯核的解剖辅助漫射光学层析成像重建

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摘要

Image reconstruction in diffuse optical tomography (DOT) is challenging because its inverse problem is nonlinear, ill-posed and ill-conditioned. Anatomical guidance from high spatial resolution imaging modalities can substantially improve the quality of reconstructed DOT images. In this paper, inspired by the kernel methods in machine learning, we propose the kernel method to introduce anatomical information into the DOT image reconstruction algorithm. In this kernel method, optical absorption coefficient at each finite element node is represented as a function of a set of features obtained from anatomical images such as computed tomography (CT). The kernel based image model is directly incorporated into the forward model of DOT, which exploits the sparseness of the image in the feature space. Compared with Laplacian approaches to include structural priors, the proposed method does not require the image segmentation of distinct regions. The proposed kernel method is validated with numerical simulations of 3D DOT reconstruction using synthetic CT data. We added 15% Gaussian noise onto both the numerical DOT measurements and the simulated CT image. We have also validated the proposed method by agar phantom experiment with anatomical guidance from a CT scan. We have studied the effects of voxel size and number of nearest neighborhood size in kernel method on the reconstructed DOT images. Our results indicate that the spatial resolution and the accuracy of the reconstructed DOT images have been improved substantially after applying the anatomical guidance with the proposed kernel method.
机译：漫射光学层析成像（DOT）中的图像重建具有挑战性，因为其逆问题是非线性的，不适定的和病态的。来自高空间分辨率成像模态的解剖学指导可以大大改善重建DOT图像的质量。在本文中，受机器学习中的核方法启发，我们提出了一种将解剖信息引入DOT图像重建算法的核方法。在这种核方法中，每个有限元节点的光吸收系数表示为一组特征的函数，这些特征是从解剖图像（例如计算机断层扫描（CT））获得的。基于内核的图像模型直接合并到DOT的正向模型中，该模型利用了特征空间中图像的稀疏性。与包含结构先验的拉普拉斯方法相比，该方法不需要对不同区域进行图像分割。利用合成CT数据对3D DOT重建进行数值模拟，验证了所提出的核方法。我们在数字DOT测量和模拟CT图像上都添加了15％的高斯噪声。我们还通过琼脂体模实验在CT扫描的解剖学指导下验证了所提出的方法。我们已经研究了核方法中体素大小和最近邻域大小的数量对重建的DOT图像的影响。我们的结果表明，在使用所提出的核方法进行解剖指导后，重构的DOT图像的空间分辨率和准确性已得到实质性改善。

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