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Proceedings Paper

Super-resolution in cardiac MRI using a Bayesian approach
Author(s): Nelson Velasco Toledo; Andrea Rueda; Cristina Santa Marta; Eduardo Romero
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Paper Abstract

Acquisition of proper cardiac MR images is highly limited by continued heart motion and apnea periods. A typical acquisition results in volumes with inter-slice separations of up to 8 mm. This paper presents a super-resolution strategy that estimates a high-resolution image from a set of low-resolution image series acquired in different non-orthogonal orientations. The proposal is based on a Bayesian approach that implements a Maximum a Posteriori (MAP) estimator combined with a Wiener filter. A pre-processing stage was also included, to correct or eliminate differences in the image intensities and to transform the low-resolution images to a common spatial reference system. The MAP estimation includes an observation image model that represents the different contributions to the voxel intensities based on a 3D Gaussian function. A quantitative and qualitative assessment was performed using synthetic and real images, showing that the proposed approach produces a high-resolution image with significant improvements (about 3dB in PSNR) with respect to a simple trilinear interpolation. The Wiener filter shows little contribution to the final result, demonstrating that the MAP uniformity prior is able to filter out a large amount of the acquisition noise.

Paper Details

Date Published: 13 March 2013
PDF: 6 pages
Proc. SPIE 8669, Medical Imaging 2013: Image Processing, 866932 (13 March 2013); doi: 10.1117/12.2007074
Show Author Affiliations
Nelson Velasco Toledo, Univ. Militar Nueva Granada (Colombia)
Univ. Nacional de Colombia (Colombia)
Andrea Rueda, Univ. Nacional de Educación a Distancia (Spain)
Cristina Santa Marta, Univ. Nacional de Educación a Distancia (Spain)
Eduardo Romero, Univ. Nacional de Colombia (Colombia)

Published in SPIE Proceedings Vol. 8669:
Medical Imaging 2013: Image Processing
Sebastien Ourselin; David R. Haynor, Editor(s)

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