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Abstract #3442

High-frequency Transform Guided Denoising Autoencoding Prior for Parallel MR Imaging

Yuanyuan Hu1, Zhuonan He2, Jinjie Zhou2, Minghui Zhang2, Qiegen Liu2, and Dong Liang1
1Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China, Shenzhen, China, 2the Department of Electronic Information Engineering, Nanchang University, Nanchang 330031, China, Nanchang, China

Ill-posed inverse problems embodied in parallel imaging remain an active research topic in several decades, with new approaches constantly emerging. Built on the observation that both dictionary learning and conventional sparse coding extract high-frequency component to model, we derived a novel strategy named HDAEP to explore the prior on high-frequency domain on the basis of denoising autoencoding. After the prior is learned from the trained network, the iteratively Gauss-Newton method is employed to jointly estimating the images and coil sensitivities. Experimental results show that the proposed method can achieve superior performances on parallel MRI reconstruction compared to state-of-the-arts.

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