A Statistical Framework for Model-Based Inverse Problems in Ultrasound Elastography
A Statistical Framework for Model-Based Inverse Problems in Ultrasound Elastography
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DOI：
10.1109/ieeeconf51394.2020.9443450
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发表时间：
2020-10
期刊：
影响因子：
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通讯作者：
N. Mohammadi;M. Doyley;M. Çetin
中科院分区：
文献类型：
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作者：
N. Mohammadi;M. Doyley;M. Çetin
基于模型的组织计算弹性成像可以看作是求解跨越位移图像的有限元逆问题。由于现有的准静态弹性学方法大多依赖于正演模型的确定性公式，导致约束优化问题，位移观测误差的影响没有得到很好的解决。为此，我们提出了一种新的统计技术，该技术可导致弹性成像的统一优化问题。我们的统计模型考虑到位移测量的不完善性质，并导致杨氏模量的观测模型，该模型涉及与信号相关的彩色噪声。为了解决由此产生的正则化优化问题，我们提出了一种利用近端分裂方法的不动点算法。初步的定性和定量结果证明了所提出方法的有效性和稳健性。
Model-based computational elasticity imaging of tissues can be posed as solving an inverse problem over finite elements spanning the displacement image. As most existing quasi-static elastography methods count on deterministic formulations of the forward model resulting in a constrained optimization problem, the impact of displacement observation errors has not been well addressed. To this end, we propose a new statistical technique that leads to a unified optimization problem for elasticity imaging. Our statistical model takes the imperfect nature of the displacement measurements into account, and leads to an observation model for the Young’s modulus that involves signal dependent colored noise. To solve the resulting regularized optimization problem, we propose a fixed-point algorithm that leverages proximal splitting methods. Preliminary qualitative and quantitative results demonstrate the effectiveness and robustness of the proposed methodology.