Robust Super-Resolution Volume Reconstruction From Slice Acquisitions: Application to Fetal Brain MRI

• Published in: IEEE transactions on medical imaging Vol. 29; no. 10; pp. 1739 - 1758
• Main Authors: Gholipour, A, Estroff, J A, Warfield, S K
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.10.2010; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Acquisitions & mergers; Algorithms; Anatomy, Cross-Sectional - methods; Brain - anatomy & histology; Brain modeling; Fetal magnetic resonance imaging (MRI); Fetus; Humans; Image Enhancement - methods; Image Interpretation, Computer-Assisted - methods; Image reconstruction; Image resolution; Imaging phantoms; Imaging, Three-Dimensional - methods; Inverse problems; M-estimation; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; maximum likelihood; Pattern Recognition, Automated - methods; Pediatrics; Permission; Prenatal Diagnosis - methods; Reproducibility of Results; robust super-resolution; Robustness; Sensitivity and Specificity; Studies; volume reconstruction
• ISSN: 0278-0062; 1558-254X; 1558-254X
• DOI: 10.1109/TMI.2010.2051680

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.