本文应用数字图像测量系统．对砼试件在成形早期最初26小时的变形场进行了非接触的定量测量，得到了砼成形早期收缩应变随时间变化的曲线。整个测量过程简便直接，表明数字图像测量方法完全可用来对砼结构的位移场进行非接触的精密测量，在配备了相应的光学观察仪器后，也可用来测量一般的应变场，从而特为土建结构的定量测试提供新的有力手段。

应用图像识别与变分学原理，对数字散斑相关方法作了理论阐述，导出了测量物体表面变形场的一般过程，将力学中的变形测量问题转化为单纯的数值计算问题，避免了传统光测方法与干涉条纹有关的一系列困难。初步的实验结果表明，该方法在工程实际现场、高速冲击动态过程、细观力学变形过程以及变形测量的自动化等方面都有广泛的应用潜力，从而为光测力学拓展应用领域、实现自动化测量展现了新的前景。

通过空彤彤态函敷的引入，将数字圈像（嵌斑）档是搜索方法进一步推广。提出丁敷字图像相是分析法在这一方击中，把对桩秽厦其各十导敷分量独立进行的相先进代变为叶变形彤态画敷的系数进行相是遗代，从而既简化丁速代过程，更妾现丁整十空彤埽的直接剥量（计算)。典型的实验蛄秉验证丁进一方法的可行性。

This paper presents a virtual tagging framework for measuring, as well as visualising, myocardial deformation using magnetic resonance (MR) velocity imaging. Tagging grids are allocated artificially according to the deformation gradient with varying shapes and densities. The control points are then deformed such that the difference between the induced deformation velocity and that of actually measured MR data is minimum. A full three-dimensional implementation of the technique combined with the mass conservation constraint is provided. Numerical considerations of applying the proposed framework and different optimization strategies have been investigated with both simulated and phantom experiments. The accuracy of the technique in terms of following material deformation is compared with that of conventional tagging technique. Index Terms-Finite-element analysis, magnetic resonance (MR) tagging, mass conservation, myocardial deformation, myocardial velocity mapping, strain analysis.

Respiratory-induced cardiac deformation is a major problem for high-resolution cardiac imaging. This paper presents a new technique for predictive cardiac motion modeling and correction, which uses partial least squares regression to extract intrinsic relationships between three-dimensional (3-D) cardiac deformation due to respiration and multiple one-dimensional real-time measurable surface intensity traces at chest or abdomen. Despite the fact that these surface intensity traces can be strongly coupled with each other but poorly correlated with respiratory-induced cardiac deformation, we demonstrate how they can be used to accurately predict cardiac motion through the extraction of latent variables of both the input and output of the model. The proposed method allows cross-modality reconstruction of patient specific models for dense motion field prediction, which after initial modeling can be used for real-time prospective motion tracking or correction. Detailed numerical issues related to the technique are discussed and the effectiveness of the motion and deformation modeling is validated with 3-D magnetic resonance data sets acquired from ten asymptomatic subjects covering the entire respiratory range.