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首页> 外文期刊>Medical Physics >Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography.
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Singular value decomposition based computationally efficient algorithm for rapid dynamic near-infrared diffuse optical tomography.

机译：基于奇异值分解的高效计算技术，用于快速动态近红外漫射光学层析成像。

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摘要

PURPOSE: A computationally efficient algorithm (linear iterative type) based on singular value decomposition (SVD) of the Jacobian has been developed that can be used in rapid dynamic near-infrared (NIR) diffuse optical tomography. METHODS: Numerical and experimental studies have been conducted to prove the computational efficacy of this SVD-based algorithm over conventional optical image reconstruction algorithms. RESULTS: These studies indicate that the performance of linear iterative algorithms in terms of contrast recovery (quantitation of optical images) is better compared to nonlinear iterative (conventional) algorithms, provided the initial guess is close to the actual solution. The nonlinear algorithms can provide better quality images compared to the linear iterative type algorithms. Moreover, the analytical and numerical equivalence of the SVD-based algorithm to linear iterative algorithms was also established as a part of this work. It is also demonstrated that the SVD-based image reconstruction typically requires O(NN2) operations per iteration, as contrasted with linear and nonlinear iterative methods that, respectively, require O(NN3) and O(NN6) operations, with "NN" being the number of unknown parameters in the optical image reconstruction procedure. CONCLUSIONS: This SVD-based computationally efficient algorithm can make the integration of image reconstruction procedure with the data acquisition feasible, in turn making the rapid dynamic NIR tomography viable in the clinic to continuously monitor hemodynamic changes in the tissue pathophysiology.
机译：目的：已开发出一种基于雅可比矩阵奇异值分解（SVD）的高效计算算法（线性迭代类型），该算法可用于快速动态近红外（NIR）漫射光学层析成像。方法：进行了数值和实验研究，以证明这种基于SVD的算法相对于常规光学图像重建算法的计算效率。结果：这些研究表明，线性迭代算法在对比度恢复（光学图像的量化）方面的性能要优于非线性迭代（常规）算法，前提是初始猜测与实际解决方案相近。与线性迭代类型算法相比，非线性算法可以提供质量更好的图像。此外，作为这项工作的一部分，还建立了基于SVD的算法与线性迭代算法的解析和数值等效性。还证明了基于SVD的图像重建通常每次迭代都需要O（NN2）操作，而线性和非线性迭代方法分别需要O（NN3）和O（NN6）操作，而“ NN”为光学图像重建程序中未知参数的数量。结论：这种基于SVD的计算有效算法可以使图像重建程序与数据采集的集成成为可能，从而使快速动态NIR层析成像在临床上可行，从而可以连续监测组织病理生理学中的血液动力学变化。

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