Super resolution OF 3D MRI images using a Gaussian scale mixture model constraint

In multi-slice magnetic resonance imaging (MRI) the resolution in the slice direction is usually reduced to allow faster acquisition times and to reduce the amount of noise in each 2-D slice. In this paper, a novel image super resolution (SR) algorithm is presented that is used to improve the resolution of the 3D MRI volumes in the slice direction. The proposed SR algorithm uses a complex wavelet-based de-blurring approach with a Gaussian scale mixture model sparseness constraint. The algorithm takes several multi-slice volumes of the same anatomical region captured at different angles and combines these low-resolution images together to form a single 3D volume with much higher resolution in the slice direction. Our results show that the 3D volumes reconstructed using this approach have higher quality than volumes produced by the best previously proposed approaches.

[1]  Hayit Greenspan,et al.  MRI Inter-slice Reconstruction Using Super-Resolution , 2001, MICCAI.

[2]  Mario Bertero,et al.  Introduction to Inverse Problems in Imaging , 1998 .

[3]  S Peled,et al.  Superresolution in MRI: Application to human white matter fiber tract visualization by diffusion tensor imaging , 2001, Magnetic resonance in medicine.

[4]  Russell M. Mersereau,et al.  A Super-Resolution Framework for 3-D High-Resolution and High-Contrast Imaging Using 2-D Multislice MRI , 2009, IEEE Transactions on Medical Imaging.

[5]  C. C. Li,et al.  Application of wavelet-based POCS super-resolution for cardiovascular MRI image enhancement , 2004, Third International Conference on Image and Graphics (ICIG'04).

[6]  Michael Unser,et al.  A Fast Thresholded Landweber Algorithm for Wavelet-Regularized Multidimensional Deconvolution , 2008, IEEE Transactions on Image Processing.

[7]  Klaus Scheffler.  Superresolution in MRI? , 2002, Magnetic resonance in medicine.

[8]  Rachid Deriche,et al.  The use of super‐resolution techniques to reduce slice thickness in functional MRI , 2004, Int. J. Imaging Syst. Technol..

[9]  I. Daubechies,et al.  An iterative thresholding algorithm for linear inverse problems with a sparsity constraint , 2003, math/0307152.

[10]  Nick G. Kingsbury,et al.  Image deconvolution using a Gaussian Scale Mixtures model to approximate the wavelet sparseness constraint , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[11]  Moon Gi Kang,et al.  Super-resolution image reconstruction: a technical overview , 2003, IEEE Signal Process. Mag..

[12]  Hayit Greenspan,et al.  Regularized super-resolution of brain MRI , 2009, 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  Levent Sendur,et al.  Bivariate shrinkage functions for wavelet-based denoising exploiting interscale dependency , 2002, IEEE Trans. Signal Process..

[14]  Hayit Greenspan,et al.  Super-resolution in MRI , 2002, Proceedings IEEE International Symposium on Biomedical Imaging.

[15]  D. Louis Collins,et al.  Design and construction of a realistic digital brain phantom , 1998, IEEE Transactions on Medical Imaging.