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A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction


Affiliations
1 Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
 

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.
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  • A Dictionary Learning Method with Total Generalized Variation for MRI Reconstruction

Abstract Views: 72  |  PDF Views: 2

Authors

Hongyang Lu
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
Jingbo Wei
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
Qiegen Liu
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
Yuhao Wang
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China
Xiaohua Deng
Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China

Abstract


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.