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XeMRI to CT lung image registration enhanced with personalized 4DCT-derived motion model

Abstract:

This paper presents a novel method for multi-modal lung image registration constrained by a motion model derived from lung 4DCT. The motion model is estimated based on the results of intra-patient image registration using Principal Component Analysis. The approach with a prior motion model is particularly important for regions where there is not enough information to reliably drive the registration process, as in the case of hyperpolarized Xenon MRI and proton density MRI to CT registration. ...

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Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1007/978-3-030-00946-5_26

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Institution:
University of Oxford
Division:
MSD
Department:
Oncology
Sub department:
Oncology
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Oncology
Role:
Author
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Institution:
University of Oxford
Division:
MSD
Department:
Biomedical Services
Role:
Author
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Institution:
University of Oxford
Division:
MPLS
Department:
Engineering Science
Role:
Author
ORCID:
0000-0001-8139-3480
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Contributors

Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
Role:
Editor
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Publisher:
Springer International Publishing Publisher's website
Journal:
Lecture Notes in Computer Science Journal website
Volume:
11040
Pages:
260-271
Series:
Lecture Notes in Computer Science
Host title:
IMAGE ANALYSIS FOR MOVING ORGAN, BREAST, AND THORACIC IMAGES
Publication date:
2018-09-12
DOI:
EISSN:
1611-3349
ISSN:
0302-9743
ISBN:
978-3-030-00945-8
Language:
English
Keywords:
Pubs id:
921910
Local pid:
pubs:921910
Deposit date:
2020-06-17

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