Respiratory motion prediction from CBCT image observations using UKF

Author

Sundarapandian, Manivannan ; Kalpathi, Ramakrishnan ; Siochi, R. Alfredo

Author_Institution

Siemens Technol. & Services Private Ltd., Bangalore, India

fYear

2015

fDate

18-22 May 2015

Firstpage

559

Lastpage

562

Abstract

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.

Keywords

Kalman filters; biological tissues; computerised tomography; image motion analysis; medical image processing; nonlinear filters; physiological models; pneumodynamics; position measurement; CBCT image; CBCT raw projection image; LCM model; UKF; breathing pattern prediction; diaphragm apex position measurement; local circular motion model; respiratory motion prediction accuracy; state estimation; state space model; unscented Kalman filter; Biological system modeling; Biomedical imaging; Computational modeling; Kalman filters; Mathematical model; Noise; Predictive models;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Vision Applications (MVA), 2015 14th IAPR International Conference on

Conference_Location

Tokyo

Type

conf

DOI

10.1109/MVA.2015.7153254

Filename

7153254