Alternating Minimization for Computed Tomography with Unknown Geometry Parameters
Alternating Minimization for Computed Tomography with Unknown Geometry Parameters
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DOI：
10.1137/21s1441638
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发表时间：
2021-08
期刊：
影响因子：
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通讯作者：
Mai Phuong Pham Huynh;M. Santana;Ana Castillo
中科院分区：
文献类型：
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作者：
Mai Phuong Pham Huynh;M. Santana;Ana Castillo
由于COVID-19大流行，全球对便携式CT机的需求不断增加，以便在各种环境下诊断患者。这导致了对CT图像重建算法的需求，这些算法可以在多种几何参数受到干扰的情况下产生高质量的图像。在本文中，我们提出了一种交替下降算法来解决这个问题，其中一步最小化正则化线性最小二乘问题，另一步最小化有界非线性最小二乘问题。此外，我们调查了现有的加速收敛算法的方法，并通过使用MATLAB包（如IRtools和imfil）讨论了实现细节。最后，通过数值实验验证了算法的有效性。
Due to the COVID-19 pandemic, there is an increasing demand for portable CT machines worldwide in order to diagnose patients in a variety of settings [16]. This has lead to a need for CT image reconstruction algorithms that can produce high-quality images in the case when multiple types of geometry parameters have been perturbed. In this paper, we present an alternating descent algorithm to address this issue, where one step minimizes a regularized linear least squares problem, and the other minimizes a bounded non-linear least-square problem. Additionally, we survey existing methods to accelerate the convergence algorithm and discuss implementation details through the use of MATLAB packages such as IRtools and imfil. Finally, numerical experiments are conducted to show the effectiveness of our algorithm.