Literature DB >> 28840445

MR image reconstruction via guided filter.

Heyan Huang1, Hang Yang2, Kang Wang3.   

Abstract

Magnetic resonance imaging (MRI) reconstruction from the smallest possible set of Fourier samples has been a difficult problem in medical imaging field. In our paper, we present a new approach based on a guided filter for efficient MRI recovery algorithm. The guided filter is an edge-preserving smoothing operator and has better behaviors near edges than the bilateral filter. Our reconstruction method is consist of two steps. First, we propose two cost functions which could be computed efficiently and thus obtain two different images. Second, the guided filter is used with these two obtained images for efficient edge-preserving filtering, and one image is used as the guidance image, the other one is used as a filtered image in the guided filter. In our reconstruction algorithm, we can obtain more details by introducing guided filter. We compare our reconstruction algorithm with some competitive MRI reconstruction techniques in terms of PSNR and visual quality. Simulation results are given to show the performance of our new method.

Keywords:  Compressive sensing; Guided filter; Image reconstruction; Magnetic resonance imaging

Mesh:

Year:  2017        PMID: 28840445     DOI: 10.1007/s11517-017-1709-8

Source DB:  PubMed          Journal:  Med Biol Eng Comput        ISSN: 0140-0118            Impact factor:   2.602


  13 in total

1.  Higher degree total variation (HDTV) regularization for image recovery.

Authors:  Yue Hu; Mathews Jacob
Journal:  IEEE Trans Image Process       Date:  2012-01-09       Impact factor: 10.856

2.  Undersampled MRI reconstruction with patch-based directional wavelets.

Authors:  Xiaobo Qu; Di Guo; Bende Ning; Yingkun Hou; Yulan Lin; Shuhui Cai; Zhong Chen
Journal:  Magn Reson Imaging       Date:  2012-04-13       Impact factor: 2.546

3.  Exploiting sparsity and low-rank structure for the recovery of multi-slice breast MRIs with reduced sampling error.

Authors:  X X Yin; B W-H Ng; K Ramamohanarao; A Baghai-Wadji; D Abbott
Journal:  Med Biol Eng Comput       Date:  2012-05-30       Impact factor: 2.602

4.  MR image reconstruction from highly undersampled k-space data by dictionary learning.

Authors:  Saiprasad Ravishankar; Yoram Bresler
Journal:  IEEE Trans Med Imaging       Date:  2010-11-01       Impact factor: 10.048

5.  Parallel MR image reconstruction using augmented Lagrangian methods.

Authors:  Sathish Ramani; Jeffrey A Fessler
Journal:  IEEE Trans Med Imaging       Date:  2010-11-18       Impact factor: 10.048

6.  Sparse MRI: The application of compressed sensing for rapid MR imaging.

Authors:  Michael Lustig; David Donoho; John M Pauly
Journal:  Magn Reson Med       Date:  2007-12       Impact factor: 4.668

7.  Adaptive dictionary learning in sparse gradient domain for image recovery.

Authors:  Qiegen Liu; Shanshan Wang; Leslie Ying; Xi Peng; Yanjie Zhu; Dong Liang
Journal:  IEEE Trans Image Process       Date:  2013-08-15       Impact factor: 10.856

8.  Gradient-based image recovery methods from incomplete Fourier measurements.

Authors:  Vishal M Patel; Ray Maleh; Anna C Gilbert; Rama Chellappa
Journal:  IEEE Trans Image Process       Date:  2011-06-16       Impact factor: 10.856

9.  Sensitivity encoding reconstruction with nonlocal total variation regularization.

Authors:  Dong Liang; Haifeng Wang; Yuchou Chang; Leslie Ying
Journal:  Magn Reson Med       Date:  2010-12-16       Impact factor: 4.668

10.  Highly undersampled MR image reconstruction using an improved dual-dictionary learning method with self-adaptive dictionaries.

Authors:  Jiansen Li; Ying Song; Zhen Zhu; Jun Zhao
Journal:  Med Biol Eng Comput       Date:  2016-08-18       Impact factor: 2.602

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