DVMT OF IMAGE REGISTRATION FOR NEUROSURGERY

本提案描述了与以下方面相关的核心技术： 刚性和非刚性配准应用。 我们将会用这些 用于图像融合目的的配准方法（即， 同一患者的多个诊断成像采集），以及 对于模板驱动的分割TDS）（即用于 将图谱数据集扭曲成新MR数据集的配置）。 在 我们的建议，这项技术的临床意义是 演示了手术计划和可视化以及 术中图像引导。 我们之前的相关研究 配准和可变形建模集中在手动 基于信息的刚性配准和非刚性配准 模板驱动分割（TDS）。 进一步改善和 针对我国现有刚性基础的工程特点， 注册方法，并实现非刚性注册， 手术应用和模板驱动分割。 我们的目标之一是加强对解剖和 医学图像中可用的功能信息， 图像引导治疗。 我们将为外科医生提供 信息，跨模式登记和（在 介入或术中MRI单元中的程序） 与患者的解剖结构相匹配。 这些信息可以使 外科医生更精确地识别和避开关键结构 并且更精确地定位病理组织。 在注册领域，我们将继续临床开发 相关的配准方法和弹性匹配算法。 这些 算法都用于图像融合，即多个图像的合并。 同一患者的诊断成像采集，并作为 模板驱动的分割算法， 病人大脑的结构

This proposal describes the core technology that is relevant to both rigid and non-rigid registration applications. We will use these registration methods for the purposes of image fusion (i.e. merging of multiple diagnostic imaging acquisitions of the same patient), as well as for template- driven segmentation TDS) (i.e. algorithms used to warp atlas data sets into the configuration of a new MR data set). In our proposal, the clinical significance this technology is demonstrated for surgical planning and visualization as well as for intraoperative image-guidance. Our prior related research on registration and deformable modeling has concentrated on manual information (MI)-based rigid registration and non-rigid registration for template-driven segmentation (TDS). Further improvement and development is necessary on engineering features of our existing rigid register methods, and to implement the non rigid registration for surgical applications and for template driven segmentation. One of our goals is to enhance the exploitation of the anatomic and functional information available in medical imagery for use in image-guided therapy. We will provide the surgeon with access to this information, registered across modalities and (in the case of procedures in the interventional or intraoperative MRI unit) registered to the anatomy of the patient. This information may enable the surgeon to more precisely identify and avoid critical structures and to more accurately locate pathological tissues. In the area of registration we will continue to develop clinically relevant registration methods and elastic matching algorithms. These algorithms are used both for image fusion, i.e. merging of multiple diagnostic imaging acquisitions of the same patient, and as part of template-driven segmentation algorithms that warp atlas data sets into the configuration of a patient's brain.