用于电阻抗断层扫描中二值图像重建的高效梯度优化方法
Efficient Gradient-based Optimization for Reconstructing Binary Images in Applications to Electrical Impedance Tomography
AI总结：
提出一种结合样本几何、多尺度控制空间缩减与梯度优化的高效框架，用于EIT二值图像重建，在二维癌症检测逆问题中性能优越。
摘要：
本文开发并验证了一种新颖且高效的计算框架，用于重建二值型图像，该框架适用于各种生物医学应用中复杂度不同的模型。通过结合近期开发的利用具有定制几何形状的样本解和多尺度控制空间缩减的优化方法，并辅以基于梯度的技术，实现了计算速度和精度的双重效率。控制空间基于样本的几何形状及其各自贡献被有效缩减。整个三步计算过程由于调参参数数量较少而设计简单，便于在各种环境中实际实施。相当直接的梯度计算方法使该框架兼容任何优化软件，包括黑盒优化软件。该完整计算框架的性能在二维逆问题中进行了测试，即利用电阻抗断层扫描（EIT）进行癌症检测，数据来自合成生成的模型以及从医学图像中获取的、显示不同大小和形状癌区自然发展的数据。结果表明，新方法性能优越，在提高基于EIT的流程整体质量方面具有巨大潜力。
英文摘要：
A novel and highly efficient computational framework for reconstructing binary-type images suitable for models of various complexity seen in diverse biomedical applications is developed and validated. Efficiency in computational speed and accuracy is achieved by combining the advantages of recently developed optimization methods that use sample solutions with customized geometry and multiscale control space reduction, all paired with gradient-based techniques. The control space is effectively reduced based on the geometry of the samples and their individual contributions. The entire 3-step computational procedure has an easy-to-follow design due to a nominal number of tuning parameters making the approach simple for practical implementation in various settings. Fairly straightforward methods for computing gradients make the framework compatible with any optimization software, including black-box ones. The performance of the complete computational framework is tested in applications to 2D inverse problems of cancer detection by electrical impedance tomography (EIT) using data from models generated synthetically and obtained from medical images showing the natural development of cancerous regions of various sizes and shapes. The results demonstrate the superior performance of the new method and its high potential for improving the overall quality of the EIT-based procedures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
