Imaging the Mechanical Properties of Soft Tissue/L'imagerie des caractéristiques mécaniques des tissus mous.

弹性成像是一种相对较新的技术，用于成像软组织内的弹性特性分布。弹性成像提供了可视化弹性对比度的能力，比乳腺癌的X射线对比度大10- 100倍，以及组织衰减等复杂特性，独特地能够诊断肝纤维化。 由于不存在用于成像内部弹性值的直接方法，弹性成像依赖于三步方法：首先，机械致动，第二，使用诸如数字、超声或磁共振成像的方法来捕获这种应变模式;最后，通过数学逆问题从测量的应变场重建弹性特性图像。 这项研究计划的重点是这个复杂的成像过程的基础工程系统。 其目标是开发实用的工具和资源，用于先进的弹性成像方法在体内的应用。 该研究计划支持的主要项目包括：开发各向异性材料特性成像方法;改进基于数字图像的弹性成像的图像重建能力;为外部研究小组提供从三维磁共振位移数据重建弹性成像图像的在线服务;改进弹性成像图像计算的数学和统计方法;和图形处理器单元计算快速图像处理。在每种情况下，科学方法包括确定每个目标应用的关键约束和目标，例如成本，加工时间，边界条件和复杂的机械行为等问题。这些约束和条件，然后用来驱动一个功能的原型系统的设计和实现。 通过这种方式，该计划旨在建立一个用于生成新型弹性成像应用的管道，进而可以通过适当的外部资助机制进一步开发。

Elastography is a relatively new technology for imaging the elastic property distribution within soft tissues. Elastography offers the ability to visualize elastic contrast, 10-100X greater than x-ray contrast for breast cancer, as well complex properties such as tissue attenuation, uniquely able to diagnose liver fibrosis. As no direct method for imaging internal elasticity values exists, elastography relies on a three step approach: first, mechanical actuation (e.g. vibration) is applied to the tissue, generating a strain field that is dependent upon the internal elastic structure; second, methods such as digital, ultrasound or magnetic resonance imaging are used to capture this strain pattern; finally, the elastic property image is reconstructed from the measured strain field by means of a mathematical inverse problem. This research program focuses on the engineering systems that underlie this complex imaging process. The goal is to develop practical tools and resources for the application of advanced elastographic imaging methods in-vivo. Key projects to be supported by this research program include: development of methods for imaging anisotropic material properties; improved image reconstruction capability for digital image based elastography; an on-line service available to external research groups for elastographic image reconstruction from 3D Magnetic Resonance displacement data; improved mathematical and statistical methods for elastographic image calculation; and Graphics Processor Unit computing for fast image processing. In each case, the scientific approach consists of identifying the key constraints and objectives of each target application, e.g issues such as cost, processing time, boundary conditions, and complex mechanical behavior. These constraints and conditions are then used to drive the design and implementation of a functioning prototype system. In this way, this program looks to establish a pipeline for generating novel elastography applications, which in turn may be further developed through appropriate outside funding mechanisms.