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
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
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Times Cited
Scopus
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Publikationstyp
Artikel: Journalartikel
Dokumenttyp
Wissenschaftlicher Artikel
Typ der Hochschulschrift
Herausgeber
Schlagwörter
Block Matching ; Fetal 3d Ultrasound ; Fetal Neurosonography ; Mr Ultrasound Registration
Keywords plus
Sprache
englisch
Veröffentlichungsjahr
2013
Prepublished im Jahr
HGF-Berichtsjahr
2013
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
ISBN
Bandtitel
Konferenztitel
Konferzenzdatum
Konferenzort
Konferenzband
Quellenangaben
Band: 17,
Heft: 8,
Seiten: 1137-1150
Artikelnummer: ,
Supplement: ,
Reihe
Verlag
Elsevier
Verlagsort
Tag d. mündl. Prüfung
0000-00-00
Betreuer
Gutachter
Prüfer
Topic
Hochschule
Hochschulort
Fakultät
Veröffentlichungsdatum
0000-00-00
Anmeldedatum
0000-00-00
Anmelder/Inhaber
weitere Inhaber
Anmeldeland
Priorität
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
Förderungen
Copyright
Erfassungsdatum
2022-09-06