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

弹性成像是一种较新的软组织弹性分布成像技术。弹性成像提供了可视化弹性对比的能力，在乳腺癌中比x线对比高10-100倍，以及复杂的特性，如组织衰减，能够独特地诊断肝纤维化。由于没有直接成像内部弹性值的方法，弹性成像依赖于三步方法：首先，对组织施加机械驱动（例如振动），产生依赖于内部弹性结构的应变场；其次，采用数字、超声或磁共振成像等方法来捕获这种应变模式；最后，利用数学反问题从实测应变场重构出弹性特性图像。这个研究项目的重点是在这个复杂成像过程的基础上的工程系统。目标是开发实用的工具和资源，用于在体内应用先进的弹性成像方法。本课题重点支持的项目包括：材料各向异性成像方法的开发；改进的基于数字图像弹性成像的图像重建能力提供给外部研究小组的在线服务，用于从3D磁共振位移数据中重建弹性图像；改进了弹性图像计算的数理统计方法；图形处理器单元计算用于快速图像处理。在每种情况下，科学的方法包括确定每个目标应用的关键约束和目标，例如成本、处理时间、边界条件和复杂的机械行为等问题。然后使用这些约束和条件来驱动功能原型系统的设计和实现。通过这种方式，该项目希望建立一个产生新型弹性成像应用的管道，而这反过来又可以通过适当的外部资助机制进一步发展。

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.