Prediction Framework for Statistical Respiratory Motion Modeling

摘要：

Breathing motion complicates many image-guided interventions working on the thorax or upper abdomen. However, prior knowledge provided by a statistical breathing model, can reduce the uncertainties of organ location. In this paper, a prediction framework for statistical motion modeling is presented and different representations of the dynamic data for motion model building of the lungs are investigated. Evaluation carried out on 4D-CT data sets of 10 patients showed that a displacement vector-based representation can reduce most of the respiratory motion with a prediction error of about 2 mm, when assuming the diaphragm motion to be known.

作者: Tobias Klinder Cristian Lorenz Jorn Ostermann

作者单位: Philips Research North America - Briarcliff Manor, NY, USA Philips Research Europe - Hamburg, Germany Institut fur Informationsverarbeitung, Leibniz University of Hannover, Germany

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 327–334

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）