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Kuklisova-Murgasova, M.* ; Quaghebeur, G.* ; Rutherford, M.A.* ; Hajnal, J.V.* ; Schnabel, J.A.*

Reconstruction of fetal brain MRI with intensity matching and complete outlier removal.

Med. Image Anal. 16, 1550-1564 (2012)
DOI PMC
Creative Commons Lizenzvertrag
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
We propose a method for the reconstruction of volumetric fetal MRI from 2D slices, comprising super-resolution reconstruction of the volume interleaved with slice-to-volume registration to correct for the motion. The method incorporates novel intensity matching of acquired 2D slices and robust statistics which completely excludes identified misregistered or corrupted voxels and slices. The reconstruction method is applied to motion-corrupted data simulated from MRI of a preterm neonate, as well as 10 clinically acquired thick-slice fetal MRI scans and three scan-sequence optimized thin-slice fetal datasets. The proposed method produced high quality reconstruction results from all the datasets to which it was applied. Quantitative analysis performed on simulated and clinical data shows that both intensity matching and robust statistics result in statistically significant improvement of super-resolution reconstruction. The proposed novel EM-based robust statistics also improves the reconstruction when compared to previously proposed Huber robust statistics. The best results are obtained when thin-slice data and the correct approximation of the point spread function is used. This paper addresses the need for a comprehensive reconstruction algorithm of 3D fetal MRI, so far lacking in the scientific literature.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter 3d Reconstruction ; Bias Field ; Fetal Mri ; Intensity Matching ; Super-resolution
Sprache englisch
Veröffentlichungsjahr 2012
HGF-Berichtsjahr 2012
ISSN (print) / ISBN 1361-8415
e-ISSN 1361-8415
Zeitschrift Medical Image Analysis
Quellenangaben Band: 16, Heft: 8, Seiten: 1550-1564 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
DOI 10.1016/j.media.2012.07.004
Scopus ID 84870066176
PubMed ID 22939612
Erfassungsdatum 2022-09-06
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