首页> 外文会议>Conference on image-guided procedures, robotic interventions, and modeling >Refinement and expansion of matched vessel graphs for intraoperative deformable registration of hepatic CT and ultrasound
【24h】

Refinement and expansion of matched vessel graphs for intraoperative deformable registration of hepatic CT and ultrasound

机译:肝CT和超声术中术中可变形登记的匹配血管图的改进和扩展

获取原文

摘要

Multimodal registration of intraoperative ultrasound and preoperative contrast enhanced computed tomography (CT) imaging is the basis for image guided percutaneous hepatic interventions. Currently, the surgeon manually performs a rigid registration using vessel structures and other anatomical landmarks for visual guidance. We have previously presented our approach for an automation of this intraoperative registration step based on the definition of bijective correspondences between the vessel structures using an automatic graph matching.1 This paper describes our method for refinement and expansion of the matched vessel graphs, resulting in a high number of bijective correspondences. Based on these landmarks, we could extend our method to a fully deformable registration. Our system was applied successfully on CT and ultrasound data of nine patients, which are studied in this paper. The number of corresponding vessel points could be raised from a mean of 9.6 points after the graph matching to 70.2 points using the presented refinement method. This allows for the computation of a smooth deformation field. Furthermore, we can show that our deformation calculation raises the registration accuracy for 3 of the 4 chosen target vessels in pre-/postoperative CT with a mean accuracy improvement of 44%.
机译:术中超声波和术前对比增强的计算机断层扫描(CT)成像的多峰注册是图像引导经皮肝干预的基础。目前,外科医生手动使用船舶结构和其他解剖标记进行刚性注册,用于视觉指导。我们之前已经基于使用自动曲线图匹配的血管结构之间的血管结构之间的防范对应关系的定义,提出了我们的自动化方法.1本文介绍了我们对匹配血管图形的改进和扩展的方法,导致了非常数量的基础对应关系。基于这些地标,我们可以将我们的方法扩展到完全可变形的注册。我们的系统已成功应用于九名患者的CT和超声数据,本文研究。在使用所提出的细化方法匹配到70.2点之后,可以从9.6点的平均值提出相应的血管点的数量。这允许计算平滑的变形字段。此外,我们可以表明,我们的变形计算在预/术后CT中提高了4个所选靶血管中的3个具有平均准确性的44%的注册精度。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号