TISSUE STRAIN AND ELASTICITY THROUGH SPECKLE MOTION

本研究的长期目标是开发一种方法来推断 并利用超声波成像组织硬度。 这种方法将带来新的 关于组织状况的定量信息，类似于 触诊在感觉到“硬”肿瘤或皮下肌肉时， 收缩。 它主要包括测量组织变形 通过外力引起的，变形在一个 柔性（即软）材料，刚性（硬）材料较小。 作为 这里提出，组织变形是通过跟踪空间， 超声压缩力引起的时间变化 斑点图案 从技术上讲，此应用程序用于开发基于模型的方法， 计算弹性分布（也称为弹性图）， 超声散斑位移数据。 首先，建议研究一个 直接弹性问题;这里的目的是提供一个分析 散斑相关成像的框架和计算机模型 和运动，假设弹性散射介质受到 变形 从理论上讲，这将有助于更好地 了解时空变化之间的复杂关系， 超声信号和潜在的组织运动。 从实际 这将有助于开发和测试组织运动估计器 基于散斑相关测量或基于光流。 组织运动估计是求解逆问题的第一步， 弹性问题，可以表述为：确定空间 能够最好地再现估计组织的弹性分布 在弹性方程和边界约束下的运动 条件 建议开发一种迭代程序， 最佳弹性图，即适于此的光流算法 问题;这张弹性图将与目前的弹性图进行比较。 由其他人研究，并使用基于相关性的一维组织 运动估计器 最后提出研究改进 通过使用2D和3D散斑估计弹性分布 议案

The long term objective of this research is to develop a method to infer and image tissue hardness using ultrasounds. This method would bring new quantitative informations about the condition of a tissue, similar to what palpation does qualitatively when sensing "hard" tumor or a muscle under contraction. It consists essentially in measuring the tissue deformation induced through an external force, the deformation being large in a compliant (i.e. soft) material, and smaller in a rigid (hard) one. As proposed here, tissue deformation is assessed by tracking the spatio- temporal changes induced by the compression force on the ultrasound speckle patterns. Technically, this application is for developing a model-based approach to compute the elasticity distribution (also called the elastogram) using ultrasound speckle displacement data. First it is proposed to study a direct elasticity problem; here the aim is to provide an analytical framework and a computer model of image formation for speckle correlation and motion, assuming an elastic scattering medium subjected to deformations. From a theoretical point of view, this will serve to better understand the complex relationship between the spatio-temporal changes in ultrasound signals and the underlying tissue motion. From a practical standpoint, this will serve to develop and test tissue motion estimators based either on speckle correlation measurements or on optical flow. Tissue motion estimation is the first step in solving the inverse elasticity problem which can be stated as: determining the spatial distribution of elasticity that could best reproduce the estimated tissue motion under constraints provided by the elasticity equations and boundary conditions. It is proposed to develop an iterative procedure to seek the optimal elastogram, i.e. an optical flow algorithm adapted to this problem; this elastogram will be compared to the one which is currently studied by others, and which uses correlation-based one-dimensional tissue motion estimator. It is finally proposed to study the improvement on the estimation of elasticity distribution through use of 2D and 3D speckle motion.