论文标题

无黑森的射线出生反演,用于定量超声断层扫描

Hessian-free Ray-Born Inversion for Quantitative Ultrasound Tomography

论文作者

Javaherian, Ashkan

论文摘要

这项研究介绍了一种基于我们以前的工作中提出的基于Hessian的方法,介绍了一种用于定量超声断层扫描的频域,无HESSIAN的射线反转方法。两种方法都使用基于射线的绿色函数的基于射线的近似模型,并迭代地解决了频域中的反问题,从低频到高频。在先前的研究中,每个频率子问题都通过迭代的Hessian Matrix迭代来解决,这大大提高了计算成本。本研究通过特定的加权对对角线进行对角来解决这一局限性,从而实现每个子问题的单步反转。与基于Hessian的方法相比,这种修改将计算费用降低了大约一个数量级,从而使其效率与使用弯曲射线的ra型,飞行时间的方法一致。此外,通过将正则化直接纳入向前操作员并与空间分辨率平衡计算效率,无HESSIAN的方法实现了在初始模型中对噪声和不准确性不太敏感的强大图像重建。对于基于射线的近似,本研究引入了近距离的射线追踪系统。射线的雅各比式没有独立追踪辅助射线,而是通过同时追踪与链接的射线旁边的近距离射线来近似。这种方法可以提高计算效率,同时保持准确性。

This study introduces a frequency-domain, Hessian-free ray-Born inversion method for quantitative ultrasound tomography, building upon the Hessian-based approach presented in our previous work. Both methods model acoustic wave propagation using a ray-based approximation of the heterogeneous Green's function and iteratively solve the inverse problem in the frequency domain, progressing from low to high frequencies. In the previous study, each frequency subproblem is solved by iterative inversion of the Hessian matrix, which significantly increases computational costs. The present study addresses this limitation by diagonalizing the Hessian matrix through specific weighting, enabling a single-step inversion for each subproblem. This modification reduces computational expense by approximately an order of magnitude compared to the Hessian-based method, bringing its efficiency in line with radon-type, time-of-flight-based methods that use bent rays. Furthermore, by incorporating regularization directly into the forward operator and balancing computational efficiency with spatial resolution, the Hessian-free method achieves robust image reconstructions that are less sensitive to noise and inaccuracies in the initial model. For the ray-based approximation, this study introduces a paraxial ray-tracing system. Instead of independently tracing an auxiliary ray, the Jacobian of the ray is approximated by simultaneously tracing a paraxial ray alongside the linked ray. This approach improves computational efficiency while maintaining accuracy.

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