Sparse Bayesian framework applied to 3D super-resolution reconstruction in fetal brain MRI

Laura C. Becerra; Nelson Velasco Toledo; Eduardo Romero Castro

doi:10.1117/12.2073844

28 January 2015 Sparse Bayesian framework applied to 3D super-resolution reconstruction in fetal brain MRI

Laura C. Becerra, Nelson Velasco Toledo, Eduardo Romero Castro

Proceedings Volume 9287, 10th International Symposium on Medical Information Processing and Analysis; 928708 (2015) https://doi.org/10.1117/12.2073844
Event: Tenth International Symposium on Medical Information Processing and Analysis, 2014, Cartagena de Indias, Colombia

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.

Citation Download Citation

Laura C. Becerra, Nelson Velasco Toledo, and Eduardo Romero Castro "Sparse Bayesian framework applied to 3D super-resolution reconstruction in fetal brain MRI", Proc. SPIE 9287, 10th International Symposium on Medical Information Processing and Analysis, 928708 (28 January 2015); https://doi.org/10.1117/12.2073844

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