Internal Motion Estimation by Internal-external Motion Modeling for Lung Cancer Radiotherapy View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Haibin Chen, Zichun Zhong, Yiwei Yang, Jiawei Chen, Linghong Zhou, Xin Zhen, Xuejun Gu

ABSTRACT

The aim of this study is to develop an internal-external correlation model for internal motion estimation for lung cancer radiotherapy. Deformation vector fields that characterize the internal-external motion are obtained by respectively registering the internal organ meshes and external surface meshes from the 4DCT images via a recently developed local topology preserved non-rigid point matching algorithm. A composite matrix is constructed by combing the estimated internal phasic DVFs with external phasic and directional DVFs. Principle component analysis is then applied to the composite matrix to extract principal motion characteristics, and generate model parameters to correlate the internal-external motion. The proposed model is evaluated on a 4D NURBS-based cardiac-torso (NCAT) synthetic phantom and 4DCT images from five lung cancer patients. For tumor tracking, the center of mass errors of the tracked tumor are 0.8(±0.5)mm/0.8(±0.4)mm for synthetic data, and 1.3(±1.0)mm/1.2(±1.2)mm for patient data in the intra-fraction/inter-fraction tracking, respectively. For lung tracking, the percent errors of the tracked contours are 0.06(±0.02)/0.07(±0.03) for synthetic data, and 0.06(±0.02)/0.06(±0.02) for patient data in the intra-fraction/inter-fraction tracking, respectively. The extensive validations have demonstrated the effectiveness and reliability of the proposed model in motion tracking for both the tumor and the lung in lung cancer radiotherapy. More... »

PAGES

3677

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-22023-3

DOI

http://dx.doi.org/10.1038/s41598-018-22023-3

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1101148923

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29487330


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curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22023-3'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22023-3'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-22023-3'


 

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