Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter

Lukas Ramrath; Alexander Schlaefer; Floris Ernst; Sonja Dieterich; Achim Schweikard

Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter

Lukas Ramrath, Alexander Schlaefer, Floris Ernst, Sonja Dieterich, Achim Schweikard

Institute of Robotics and Cognitive Systems

Stanford University

Abstract

In this work, an Extended Kalman Filter formulation for respiration motion tracking is introduced. Based on the assumption of multiple sinusoidal components contributing to respiratory motion, a state-space model is developed. Performance of the filter is tested on data sets of patients subject to radiotherapy. Comparison to an nLMS predictor shows that the Kalman filter is less sensitive to systematic errors during target prediction.

Original language	English
Pages	56-58
Number of pages	3
Publication status	Published - 2007
Event	CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition - Berlin, Germany Duration: 27.06.2007 → 30.06.2007 Conference number: CARS 2007

Conference

Conference	CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
Country/Territory	Germany
City	Berlin
Period	27.06.07 → 30.06.07

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This output contributes to the following UN Sustainable Development Goals (SDGs)

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Ramrath, L., Schlaefer, A., Ernst, F., Dieterich, S., & Schweikard, A. (2007). Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter. 56-58. Paper presented at CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
, Berlin, Berlin, Germany. https://www.rob.uni-luebeck.de/aigaion2/index.php/attachments/single/47

@conference{421f6a04c877410faf5bd1922a5a7a2f,

title = "Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter",

abstract = "In this work, an Extended Kalman Filter formulation for respiration motion tracking is introduced. Based on the assumption of multiple sinusoidal components contributing to respiratory motion, a state-space model is developed. Performance of the filter is tested on data sets of patients subject to radiotherapy. Comparison to an nLMS predictor shows that the Kalman filter is less sensitive to systematic errors during target prediction.",

author = "Lukas Ramrath and Alexander Schlaefer and Floris Ernst and Sonja Dieterich and Achim Schweikard",

year = "2007",

language = "English",

pages = "56--58",

note = "CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition<br/> ; Conference date: 27-06-2007 Through 30-06-2007",

}

Ramrath, L, Schlaefer, A, Ernst, F, Dieterich, S & Schweikard, A 2007, 'Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter', Paper presented at CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
, Berlin, Germany, 27.06.07 - 30.06.07 pp. 56-58. <https://www.rob.uni-luebeck.de/aigaion2/index.php/attachments/single/47>

Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter. / Ramrath, Lukas; Schlaefer, Alexander; Ernst, Floris et al.
2007. 56-58 Paper presented at CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
, Berlin, Berlin, Germany.

Research output: Contribution to conference › Conference Papers › peer-review

TY - CONF

T1 - Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter

AU - Ramrath, Lukas

AU - Schlaefer, Alexander

AU - Ernst, Floris

AU - Dieterich, Sonja

AU - Schweikard, Achim

N1 - Conference code: CARS 2007

PY - 2007

Y1 - 2007

N2 - In this work, an Extended Kalman Filter formulation for respiration motion tracking is introduced. Based on the assumption of multiple sinusoidal components contributing to respiratory motion, a state-space model is developed. Performance of the filter is tested on data sets of patients subject to radiotherapy. Comparison to an nLMS predictor shows that the Kalman filter is less sensitive to systematic errors during target prediction.

AB - In this work, an Extended Kalman Filter formulation for respiration motion tracking is introduced. Based on the assumption of multiple sinusoidal components contributing to respiratory motion, a state-space model is developed. Performance of the filter is tested on data sets of patients subject to radiotherapy. Comparison to an nLMS predictor shows that the Kalman filter is less sensitive to systematic errors during target prediction.

UR - https://www.researchgate.net/publication/230817990_Prediction_of_respiratory_motion_with_a_multi-frequency_based_Extended_Kalman_Filter

M3 - Conference Papers

SP - 56

EP - 58

T2 - CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition<br/>

Y2 - 27 June 2007 through 30 June 2007

ER -

Prediction of respiratory motion with a multi-frequency based Extended Kalman Filter

Abstract

Conference

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