Research ExplorerPublicationsPopulation-based Correspondence Models for Respiratory Motio...

Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations

Standard

Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations. / Wilms, Matthias; Werner, Rene; Yamamoto, Tokihiro; Handels, Heinz; Ehrhardt, Jan.

MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART). 1. ed. 2015. p. 81-88.

Research output: SCORING: Contribution to book/anthology › SCORING: Contribution to collected editions/anthologies › Research › peer-review

Harvard

Wilms, M, Werner, R, Yamamoto, T, Handels, H & Ehrhardt, J 2015, Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations. in MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART). 1 edn, pp. 81-88.

APA

Wilms, M., Werner, R., Yamamoto, T., Handels, H., & Ehrhardt, J. (2015). Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations. In MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART) (1 ed., pp. 81-88)

Vancouver

Wilms M, Werner R, Yamamoto T, Handels H, Ehrhardt J. Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations. In MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART). 1 ed. 2015. p. 81-88

Bibtex

@inbook{e44900d8dffd4703a33a13b41c990be1,
title = "Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations",
abstract = "Many respiratory motion compensation approaches in radiationtherapy of thoracic and abdominal tumors are guided by externalbreathing signals. Patient-specific correspondence models based on planning4D data are used to relate signal measurements to internal motion.The motion estimation accuracy of these models during a treatment fractiondepends on the degree of inter-fraction motion variations. Here, weinvestigate whether motion estimation accuracy in the presence of interfractionmotion variations can be improved by (sub)population models,which incorporate patient-specific motion information and motion datafrom selected additional patients. A sparse manifold clustering approachis integrated into a regression-based correspondence modeling frameworkfor automated identification of subpopulations of patients with similarmotion characteristics. In an evaluation with repeated 4D CT scans of 13patients, subpopulation models, on average, outperform patient-specificcorrespondence models in the presence of inter-fraction motion variations.",
author = "Matthias Wilms and Rene Werner and Tokihiro Yamamoto and Heinz Handels and Jan Ehrhardt",
year = "2015",
month = oct,
language = "English",
pages = "81--88",
booktitle = "MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART)",
edition = "1",

}

RIS

TY - CHAP

T1 - Population-based Correspondence Models for Respiratory Motion Estimation in the Presence of Interfraction Motion Variations

AU - Wilms, Matthias

AU - Werner, Rene

AU - Yamamoto, Tokihiro

AU - Handels, Heinz

AU - Ehrhardt, Jan

PY - 2015/10

Y1 - 2015/10

N2 - Many respiratory motion compensation approaches in radiationtherapy of thoracic and abdominal tumors are guided by externalbreathing signals. Patient-specific correspondence models based on planning4D data are used to relate signal measurements to internal motion.The motion estimation accuracy of these models during a treatment fractiondepends on the degree of inter-fraction motion variations. Here, weinvestigate whether motion estimation accuracy in the presence of interfractionmotion variations can be improved by (sub)population models,which incorporate patient-specific motion information and motion datafrom selected additional patients. A sparse manifold clustering approachis integrated into a regression-based correspondence modeling frameworkfor automated identification of subpopulations of patients with similarmotion characteristics. In an evaluation with repeated 4D CT scans of 13patients, subpopulation models, on average, outperform patient-specificcorrespondence models in the presence of inter-fraction motion variations.

AB - Many respiratory motion compensation approaches in radiationtherapy of thoracic and abdominal tumors are guided by externalbreathing signals. Patient-specific correspondence models based on planning4D data are used to relate signal measurements to internal motion.The motion estimation accuracy of these models during a treatment fractiondepends on the degree of inter-fraction motion variations. Here, weinvestigate whether motion estimation accuracy in the presence of interfractionmotion variations can be improved by (sub)population models,which incorporate patient-specific motion information and motion datafrom selected additional patients. A sparse manifold clustering approachis integrated into a regression-based correspondence modeling frameworkfor automated identification of subpopulations of patients with similarmotion characteristics. In an evaluation with repeated 4D CT scans of 13patients, subpopulation models, on average, outperform patient-specificcorrespondence models in the presence of inter-fraction motion variations.

M3 - SCORING: Contribution to collected editions/anthologies

SP - 81

EP - 88

BT - MICCAI workshop on Imaging and Computer Assistance in Radiation Therapy Workshop (ICART)

ER -