A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction
Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV)...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2016-01-01
|
| Series: | International Journal of Biomedical Imaging |
| Online Access: | http://dx.doi.org/10.1155/2016/7512471 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832551883042979840 |
|---|---|
| author | Hongyang Lu Jingbo Wei Qiegen Liu Yuhao Wang Xiaohua Deng |
| author_facet | Hongyang Lu Jingbo Wei Qiegen Liu Yuhao Wang Xiaohua Deng |
| author_sort | Hongyang Lu |
| collection | DOAJ |
| description | Reconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values. |
| format | Article |
| id | doaj-art-6e71330ae03c4fccaf7df632b9981498 |
| institution | Kabale University |
| issn | 1687-4188 1687-4196 |
| language | English |
| publishDate | 2016-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Biomedical Imaging |
| spelling | doaj-art-6e71330ae03c4fccaf7df632b99814982025-02-03T06:00:18ZengWileyInternational Journal of Biomedical Imaging1687-41881687-41962016-01-01201610.1155/2016/75124717512471A Dictionary Learning Method with Total Generalized Variation for MRI ReconstructionHongyang Lu0Jingbo Wei1Qiegen Liu2Yuhao Wang3Xiaohua Deng4Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Electronic Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Electronic Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Electronic Information Engineering, Nanchang University, Nanchang 330031, ChinaDepartment of Electronic Information Engineering, Nanchang University, Nanchang 330031, ChinaReconstructing images from their noisy and incomplete measurements is always a challenge especially for medical MR image with important details and features. This work proposes a novel dictionary learning model that integrates two sparse regularization methods: the total generalized variation (TGV) approach and adaptive dictionary learning (DL). In the proposed method, the TGV selectively regularizes different image regions at different levels to avoid oil painting artifacts largely. At the same time, the dictionary learning adaptively represents the image features sparsely and effectively recovers details of images. The proposed model is solved by variable splitting technique and the alternating direction method of multiplier. Extensive simulation experimental results demonstrate that the proposed method consistently recovers MR images efficiently and outperforms the current state-of-the-art approaches in terms of higher PSNR and lower HFEN values.http://dx.doi.org/10.1155/2016/7512471 |
| spellingShingle | Hongyang Lu Jingbo Wei Qiegen Liu Yuhao Wang Xiaohua Deng A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction International Journal of Biomedical Imaging |
| title | A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction |
| title_full | A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction |
| title_fullStr | A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction |
| title_full_unstemmed | A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction |
| title_short | A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction |
| title_sort | dictionary learning method with total generalized variation for mri reconstruction |
| url | http://dx.doi.org/10.1155/2016/7512471 |
| work_keys_str_mv | AT hongyanglu adictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT jingbowei adictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT qiegenliu adictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT yuhaowang adictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT xiaohuadeng adictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT hongyanglu dictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT jingbowei dictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT qiegenliu dictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT yuhaowang dictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction AT xiaohuadeng dictionarylearningmethodwithtotalgeneralizedvariationformrireconstruction |