Respiratory motion prediction from CBCT image observations using UKF

被引：0

作者：

Sundarapandian, Manivannan ^{[1
]}

Kalpathi, Ramakrishnan ^{[2
]}

Siochi, R. Alfredo ^{[3
]}

机构：

[1] Siemens Technol & Serv Private Ltd, Bangalore, Karnataka, India

[2] Indian Inst Sci, EE Dept, Bangalore, Karnataka, India

[3] Univ Iowa, Dept Radiat Oncol, Iowa City, IA 52242 USA

来源：

2015 14TH IAPR INTERNATIONAL CONFERENCE ON MACHINE VISION APPLICATIONS (MVA) | 2015年

关键词：

REAL-TIME PREDICTION; MODEL;

D O I：

暂无

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, we propose a prediction model for breathing pattern based on observations from CBCT raw projection images. From the raw CBCT projections the diaphragm apex position is measured, which in turn is used for the state estimation. We use a novel state space model followed by an Unscented Kalman Filter (UKF). Our method is compared with one of the successful models called Local Circular Motion (LCM). The initial results show that, our model outperforms the LCM model in terms of prediction accuracy.

引用

页码：559 / 562

页数：4

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