Unfolded Low-rank + Sparse Reconstruction for MRI
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2022
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Mark
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Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií
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Abstract
We apply the methodology of deep unfolding on the problem of reconstruction of DCE-MRI data. The problem is formulated as a convex optimization problem, solvable via the primal–dual splitting algorithm. The unfolding allows for optimal hyperparameter selection for the model. We examine two approaches – with the parameters shared across the layers/iterations, and an adaptive version where the parameters can differ. The results demonstrate that the more complex model can better adapt to the data.
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Proceedings II of the 28st Conference STUDENT EEICT 2022: Selected papers. s. 271-275. ISBN 978-80-214-6030-0
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
https://conf.feec.vutbr.cz/eeict/index/pages/view/ke_stazeni
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en
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© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologií