PuSH - Publikationsserver des Helmholtz Zentrums München

Kuklisova-Murgasova, M.* ; Cifor, A.* ; Napolitano, R.* ; Papageorghiou, A.* ; Quaghebeur, G.* ; Rutherford, M.A.* ; Hajnal, J.V.* ; Noble, J.A.* ; Schnabel, J.A.*

Registration of 3D fetal neurosonography and MRI.

Med. Image Anal. 17, 1137-1150 (2013)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image.
Impact Factor
Scopus SNIP
Scopus
Cited By
Altmetric
0.000
2.931
29
Tags
Anmerkungen
Besondere Publikation
Auf Hompepage verbergern

Zusatzinfos bearbeiten
Eigene Tags bearbeiten
Privat
Eigene Anmerkung bearbeiten
Privat
Auf Publikationslisten für
Homepage nicht anzeigen
Als besondere Publikation
markieren
Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Block Matching ; Fetal 3d Ultrasound ; Fetal Neurosonography ; Mr Ultrasound Registration
Sprache englisch
Veröffentlichungsjahr 2013
HGF-Berichtsjahr 2013
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Quellenangaben Band: 17, Heft: 8, Seiten: 1137-1150 Artikelnummer: , Supplement: ,
Verlag Elsevier
Begutachtungsstatus Peer reviewed
Institut(e) Institute for Machine Learning in Biomed Imaging (IML)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-507100-001
Scopus ID 84882770964
PubMed ID 23969169
Erfassungsdatum 2022-09-06