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A multiscale optimization framework for reconstructing binary images using multilevel PCA-based control space reduction
Biomedical Physics & Engineering Express Pub Date : 2021-01-29 , DOI: 10.1088/2057-1976/abd4be
Priscilla M Koolman 1 , Vladislav Bukshtynov 2
Affiliation  

An efficient computational approach for optimal reconstructing parameters of binary-type physical properties for models in biomedical applications is developed and validated. The methodology includes gradient-based multiscale optimization with multilevel control space reduction by using principal component analysis (PCA) coupled with dynamical control space upscaling. The reduced dimensional controls are used interchangeably at fine and coarse scales to accumulate the optimization progress and mitigate side effects at both scales. Flexibility is achieved through the proposed procedure for calibrating certain parameters to enhance the performance of the optimization algorithm. Reduced size of control spaces supplied with adjoint-based gradients obtained at both scales facilitate the application of this algorithm to models of higher complexity and also to a broad range of problems in biomedical sciences. This technique is shown to outperform regular gradient-based methods applied to fine scale only in terms of both qualities of binary images and computing time. Performance of the complete computational framework is tested in applications to 2D inverse problems of cancer detection by the electrical impedance tomography (EIT). The results demonstrate the efficient performance of the new method and its high potential for minimizing possibilities for false positive screening and improving the overall quality of the EIT-based procedures.

中文翻译:

使用基于多级 PCA 的控制空间缩减重构二值图像的多尺度优化框架

开发并验证了一种有效的计算方法,用于优化生物医学应用中模型的二元物理特性参数重建。该方法包括基于梯度的多尺度优化,通过使用主成分分析 (PCA) 和动态控制空间放大来减少多级控制空间。缩减维度控制可在精细和粗略尺度上互换使用,以累积优化进度并减轻两种尺度的副作用。灵活性是通过所提出的校准某些参数的过程来实现的,以提高优化算法的性能。提供在两个尺度上获得的基于伴随梯度的控制空间的减小尺寸有助于将该算法应用于更高复杂性的模型以及生物医学科学中的广泛问题。仅在二值图像的质量和计算时间方面,该技术才表现出优于应用于精细尺度的常规基于梯度的方法。完整计算框架的性能在通过电阻抗断层扫描 (EIT) 应用于癌症检测的二维逆问题中进行了测试。结果证明了新方法的高效性能及其在最大限度地减少假阳性筛查可能性和提高基于 EIT 程序的整体质量方面的巨大潜力。仅在二值图像的质量和计算时间方面,该技术才表现出优于应用于精细尺度的常规基于梯度的方法。完整计算框架的性能在通过电阻抗断层扫描 (EIT) 应用于癌症检测的二维逆问题中进行了测试。结果证明了新方法的高效性能及其在最大限度地减少假阳性筛查可能性和提高基于 EIT 程序的整体质量方面的巨大潜力。仅在二值图像的质量和计算时间方面,该技术才表现出优于应用于精细尺度的常规基于梯度的方法。完整计算框架的性能在通过电阻抗断层扫描 (EIT) 应用于癌症检测的二维逆问题中进行了测试。结果证明了新方法的高效性能及其在最大限度地减少假阳性筛查可能性和提高基于 EIT 程序的整体质量方面的巨大潜力。
更新日期:2021-01-29
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