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Proceedings Paper

Sparse Bayesian framework applied to 3D super-resolution reconstruction in fetal brain MRI
Author(s): Laura C. Becerra; Nelson Velasco Toledo; Eduardo Romero Castro
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Paper Abstract

Fetal Magnetic Resonance (FMR) is an imaging technique that is becoming increasingly important as allows assessing brain development and thus make an early diagnostic of congenital abnormalities, spatial resolution is limited by the short acquisition time and the unpredictable fetus movements, in consequence the resulting images are characterized by non-parallel projection planes composed by anisotropic voxels. The sparse Bayesian representation is a flexible strategy which is able to model complex relationships. The Super-resolution is approached as a regression problem, the main advantage is the capability to learn data relations from observations. Quantitative performance evaluation was carried out using synthetic images, the proposed method demonstrates a better reconstruction quality compared with standard interpolation approach. The presented method is a promising approach to improve the information quality related with the 3-D fetal brain structure. It is important because allows assessing brain development and thus make an early diagnostic of congenital abnormalities.

Paper Details

Date Published: 28 January 2015
PDF: 6 pages
Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928708 (28 January 2015); doi: 10.1117/12.2073844
Show Author Affiliations
Laura C. Becerra, Univ. Nacional de Colombia (Colombia)
Nelson Velasco Toledo, Univ. Militar Nueva Granada (Colombia)
Univ. Nacional de Colombia (Colombia)
Eduardo Romero Castro, Univ. Nacional de Colombia (Colombia)


Published in SPIE Proceedings Vol. 9287:
10th International Symposium on Medical Information Processing and Analysis
Eduardo Romero; Natasha Lepore, Editor(s)

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