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• W4320921390 startingPage "110" @default.
• W4320921390 abstract "Decreasing magnetic resonance (MR) image acquisition times can potentially make MR examinations more accessible. Prior arts including the deep learning models have been devoted to solving the problem of long MRI imaging time. Recently, deep generative models have exhibited great potentials in algorithm robustness and usage flexibility. Nevertheless, none of existing schemes can be learned from or employed to the k-space measurement directly. Furthermore, how do the deep generative models work well in hybrid domain is also worth being investigated. In this work, by taking advantage of the deep energy-based models, we propose a k-space and image domain collaborative generative model to comprehensively estimate the MR data from under-sampled measurement. Equipped with parallel and sequential orders, experimental comparisons with the state-of-the-arts demonstrated that they involve less error in reconstruction accuracy and are more stable under different acceleration factors." @default.
• W4320921390 created "2023-02-16" @default.
• W4320921390 creator A5006707718 @default.
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• W4320921390 date "2023-06-01" @default.
• W4320921390 modified "2023-10-17" @default.
• W4320921390 title "K-space and image domain collaborative energy-based model for parallel MRI reconstruction" @default.
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• W4320921390 doi "https://doi.org/10.1016/j.mri.2023.02.004" @default.
• W4320921390 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36796460" @default.
• W4320921390 hasPublicationYear "2023" @default.
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