DocumentCode :
1510648
Title :
Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI
Author :
Gholipour, Ali ; Estroff, Judy A. ; Warfield, Simon K.
Author_Institution :
Med. Sch., Dept. of Radiol., Harvard Univ., Boston, MA, USA
Volume :
29
Issue :
10
fYear :
2010
Firstpage :
1739
Lastpage :
1758
Abstract :
Fast magnetic resonance imaging slice acquisition techniques such as single shot fast spin echo are routinely used in the presence of uncontrollable motion. These techniques are widely used for fetal magnetic resonance imaging (MRI) and MRI of moving subjects and organs. Although high-quality slices are frequently acquired by these techniques, inter-slice motion leads to severe motion artifacts that are apparent in out-of-plane views. Slice sequential acquisitions do not enable 3-D volume representation. In this study, we have developed a novel technique based on a slice acquisition model, which enables the reconstruction of a volumetric image from multiple-scan slice acquisitions. The super-resolution volume reconstruction is formulated as an inverse problem of finding the underlying structure generating the acquired slices. We have developed a robust M-estimation solution which minimizes a robust error norm function between the model-generated slices and the acquired slices. The accuracy and robustness of this novel technique has been quantitatively assessed through simulations with digital brain phantom images as well as high-resolution newborn images. We also report here successful application of our new technique for the reconstruction of volumetric fetal brain MRI from clinically acquired data.
Keywords :
biomedical MRI; brain; image motion analysis; image reconstruction; image resolution; medical image processing; obstetrics; phantoms; MRI; digital brain phantom; fast magnetic resonance imaging; fetal brain; motion artifacts; robust M-estimation; robust error norm function; robust super-resolution volume reconstruction; single shot fast spin echo; slice acquisition model; Brain modeling; Fetus; Image reconstruction; Image resolution; Imaging phantoms; Inverse problems; Magnetic resonance imaging; Pediatrics; Permission; Robustness; Fetal magnetic resonance imaging (MRI); M-estimation; maximum likelihood; robust super-resolution; volume reconstruction; Algorithms; Anatomy, Cross-Sectional; Brain; Humans; Image Enhancement; Image Interpretation, Computer-Assisted; Imaging, Three-Dimensional; Magnetic Resonance Imaging; Pattern Recognition, Automated; Prenatal Diagnosis; Reproducibility of Results; Sensitivity and Specificity;
fLanguage :
English
Journal_Title :
Medical Imaging, IEEE Transactions on
Publisher :
ieee
ISSN :
0278-0062
Type :
jour
DOI :
10.1109/TMI.2010.2051680
Filename :
5482022
Link To Document :
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