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
Open Access Green as soon as Postprint is submitted to ZB.
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.
Impact Factor
Scopus SNIP
Web of Science
Times Cited
Scopus
Cited By
Altmetric
Publication type
Article: Journal article
Document type
Scientific Article
Thesis type
Editors
Keywords
3d Reconstruction ; Bias Field ; Fetal Mri ; Intensity Matching ; Super-resolution
Keywords plus
Language
english
Publication Year
2012
Prepublished in Year
HGF-reported in Year
2012
ISSN (print) / ISBN
1361-8415
e-ISSN
1361-8415
ISBN
Book Volume Title
Conference Title
Conference Date
Conference Location
Proceedings Title
Quellenangaben
Volume: 16,
Issue: 8,
Pages: 1550-1564
Article Number: ,
Supplement: ,
Series
Publisher
Elsevier
Publishing Place
Day of Oral Examination
0000-00-00
Advisor
Referee
Examiner
Topic
University
University place
Faculty
Publication date
0000-00-00
Application date
0000-00-00
Patent owner
Further owners
Application country
Patent priority
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
Grants
Copyright
Erfassungsdatum
2022-09-06