Open Access Green as soon as Postprint is submitted to ZB.
Piecewise-diffeomorphic image registration: Application to the motion estimation between 3D CT lung images with sliding conditions.
Med. Image Anal. 17, 182-193 (2013)
In this paper, we propose a new strategy for modelling sliding conditions when registering 3D images in a piecewise-diffeomorphic framework. More specifically, our main contribution is the development of a mathematical formalism to perform Large Deformation Diffeomorphic Metric Mapping registration with sliding conditions. We also show how to adapt this formalism to the LogDemons diffeomorphic registration framework. We finally show how to apply this strategy to estimate the respiratory motion between 3D CT pulmonary images. Quantitative tests are performed on 2D and 3D synthetic images, as well as on real 3D lung images from the MICCAI EMPIRE10 challenge. Results show that our strategy estimates accurate mappings of entire 3D thoracic image volumes that exhibit a sliding motion, as opposed to conventional registration methods which are not capable of capturing discontinuous deformations at the thoracic cage boundary. They also show that although the deformations are not smooth across the location of sliding conditions, they are almost always invertible in the whole image domain. This would be helpful for radiotherapy planning and delivery.
Impact Factor
Scopus SNIP
Scopus
Cited By
Cited By
Altmetric
0.000
2.931
65
Annotations
Special Publikation
Hide on homepage
Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Diffeomorphic Registration ; Lddmm ; Logdemons ; Respiratory Motion ; Sliding Motion
Language
english
Publication Year
2013
HGF-reported in Year
2013
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
Journal
Medical Image Analysis
Quellenangaben
Volume: 17,
Issue: 2,
Pages: 182-193
Publisher
Elsevier
Reviewing status
Peer reviewed
Institute(s)
Institute for Machine Learning in Biomed Imaging (IML)
POF-Topic(s)
30205 - Bioengineering and Digital Health
Research field(s)
Enabling and Novel Technologies
PSP Element(s)
G-507100-001
Scopus ID
84883871559
Scopus ID
23177000
Erfassungsdatum
2022-09-06