论文标题
超声弹力图中基于模型的反问题的统计框架
A statistical framework for model-based inverse problems in ultrasound elastography
论文作者
论文摘要
基于模型的组织的计算弹性成像可以提出，因为在跨越位移图像的有限元素上解决了反问题。由于大多数现有的准静态弹性图方法都依靠正向模型的确定性公式，从而导致了受限的优化问题，因此位移观察错误的影响尚未得到很好的解决。为此，我们提出了一种新的统计技术，该技术导致弹性成像的统一优化问题。我们的统计模型考虑了位移测量值的不完善性质，并导致了Young模量的观察模型，该模型涉及信号依赖的彩色噪声。为了解决所得的正则优化问题，我们提出了一种利用近端分裂方法的定点算法。初步定性和定量结果证明了所提出方法的有效性和鲁棒性。
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.
