Vision Algorithms for Analysis of Tissue and Fluid Deformations from Medical Images

本研究的目的是开发用于分析医学图像中非刚性运动的视觉算法。我们将继续在蛇跟踪和图像速度场计算方面的算法开发，以评估组织变形和分析来自MRI和X射线图像的流体流动，并将技术扩展到3D。MRI的独特之处在于，它能够非侵入性和选择性地改变组织的磁化，并在变形组织(如心肌)内创建标记图案。所产生的图案在下面的组织上定义了一个时变的曲线坐标系，允许根据图像序列精确和定量地测量组织的运动和变形。使用动态规划(DP)B样条线和交互B样条可变形网格来跟踪这些可见标记线的运动。已经开发了WARP算法，该算法连续地映射平面上的两个组织区域，使得两个嵌入的曲线网格被引入配准。对于流场分析，建立了光流的标量函数公式。新的光流框架的新方面是它自动地在所产生的速度场上强制不可压缩。计划在各种实验条件下应用这些技术。

The aim of this research is to develop vision algorithms for analysis of non-rigid motion from medical imagery. We will continue our algorithm development in snake tracking and image velocity field computation for assessment of tissue deformations and analysis of fluid flow from MRI and X-ray imagery, and extend the techniques to 3D. MRI is unique in its ability to non-invasively and selectively alter tissue magnetization, and create tagged patterns within deforming tissue such as the heart muscle. The resulting pattern defines a time-varying curvilinear coordinate system on the underlying tissue, allowing for precise and quantitative measurement of tissue motion and deformation from a sequence of images. The motion of these visible tag lines is tracked with Dynamic Programming (DP) B-spline snakes, and interacting B-spline deformable grids. Warp algorithms have been developed which continuously map two tissue regions in the plane such that two embedded grids of curves are brought into registration. For fluid flow field analysis, a scalar function formulation for optical flow has been developed. The novel aspect of the new optical flow framework is that it automatically enforces incompressibility on the resulting velocity field. Applications of the techniques to various experimental conditions are planned.