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

Institut für Robotik und Kognitive Systeme

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.

Originalsprache	Englisch
Seiten	56-58
Seitenumfang	3
Publikationsstatus	Veröffentlicht - 2007
Veranstaltung	CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition - Berlin, Deutschland Dauer: 27.06.2007 → 30.06.2007 Konferenznummer: CARS 2007

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress	CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
Land/Gebiet	Deutschland
Ort	Berlin
Zeitraum	27.06.07 → 30.06.07

UN 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. Beitrag in CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
, Berlin, Berlin, Deutschland. https://www.rob.uni-luebeck.de/aigaion2/index.php/attachments/single/47

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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",

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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', Beitrag in CARS 2007 - Computer Assisted Radiology and Surgery 21st International Congress and Exhibition
, Berlin, Deutschland, 27.06.07 - 30.06.07 S. 56-58. <https://www.rob.uni-luebeck.de/aigaion2/index.php/attachments/single/47>

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

Tagung, Konferenz, Kongress

UN SDGs

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