Singular value decomposition based regularization prior to spectral mixing improves crosstalk in dynamic imaging using spectral diffuse optical tomography.
Singular value decomposition based regularization prior to spectral mixing improves crosstalk in dynamic imaging using spectral diffuse optical tomography.
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DOI：
10.1364/boe.3.002036
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发表时间：
2012-09-01
影响因子：
3.4
通讯作者：
Dehghani H
中科院分区：
文献类型：
--
作者：
Zhan Y;Eggebrecht AT;Culver JP;Dehghani H
光谱约束漫射光学层析成像（DOT）方法依赖于将光谱先验信息直接纳入图像重建算法，从而将多个波长的底层光学特性关联起来。尽管该方法已被证明提供了一种稳定的解决方案，但使用传统的tikhonov型正则化技术可能导致参数之间的额外串扰，特别是在线性、单步动态成像应用中。这主要是由于光谱雅可比矩阵的次优正则化，它不仅平滑了图像数据空间，而且平滑了光谱映射空间。本文提出了一种新的基于奇异值分解（SVD）的正则化技术，在对雅可比矩阵进行正则化的同时保留了谱先验信息，从而大大减少了恢复参数之间的串扰。利用模拟数据，通过基于奇异值分解的方法重构氧合血红蛋白和脱氧血红蛋白浓度的变化图像，并与非光谱和常规光谱方法重构的图像进行比较。在二维、两波长的例子中，与传统的光谱重建算法相比，该方法可将恢复参数之间的串扰减少98%，与非频谱约束算法相比减少60%。使用受试者特定的人类头部多层模型，进行了皮质激活的无噪声动态模拟，以进一步证明串扰的这种改进。然而，随着数据中真实噪声的加入，非频谱和所提算法的表现相似，这表明在动态DOT中使用频谱约束重建算法可能会受到信号对比度和系统噪声特性的限制。
The spectrally constrained diffuse optical tomography (DOT) method relies on incorporating spectral prior information directly into the image reconstruction algorithm, thereby correlating the underlying optical properties across multiple wavelengths. Although this method has been shown to provide a solution that is stable, the use of conventional Tikhonov-type regularization techniques can lead to additional crosstalk between parameters, particularly in linear, single-step dynamic imaging applications. This is due mainly to the suboptimal regularization of the spectral Jacobian matrix, which smoothes not only the image-data space, but also the spectral mapping space. In this work a novel regularization technique based on the singular value decomposition (SVD) is presented that preserves the spectral prior information while regularizing the Jacobian matrix, leading to dramatically reduced crosstalk between the recovered parameters. Using simulated data, images of changes in oxygenated and deoxygenated hemoglobin concentrations are reconstructed via the SVD-based approach and compared with images reconstructed by using non-spectral and conventional spectral methods. In a 2D, two wavelength example, it is shown that the proposed approach provides a 98% reduction in crosstalk between recovered parameters as compared with conventional spectral reconstruction algorithms, and 60% as compared with non-spectrally constrained algorithms. Using a subject specific multilayered model of the human head, a noiseless dynamic simulation of cortical activation is performed to further demonstrate such improvement in crosstalk. However, with the addition of realistic noise in the data, both non-spectral and proposed algorithms perform similarly, indicating that the use of spectrally constrained reconstruction algorithms in dynamic DOT may be limited by the contrast of the signal as well as the noise characteristics of the system.