Unfolded Low-rank + Sparse Reconstruction for MRI

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68081731%3A_____%2F22%3A00568850" target="_blank" >RIV/68081731:_____/22:00568850 - isvavai.cz</a>
Alternative codes found
RIV/00216305:26220/22:PU144477
Result on the web
<a href="https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf" target="_blank" >https://www.eeict.cz/eeict_download/archiv/sborniky/EEICT_2022_sbornik_2_v3.pdf</a>
DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Unfolded Low-rank + Sparse Reconstruction for MRI
Original language description
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.
Czech name
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Czech description
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Project
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Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2022
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection
Proceedings II of the 28th Conference STUDENT EEICT 2022. Selected Papers
ISBN
978-80-214-6030-0
ISSN
2788-1334
e-ISSN
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Number of pages
5
Pages from-to
271-275
Publisher name
Brno University of Technology, Faculty of Electrical Engineering and Communication
Place of publication
Brno
Event location
Brno
Event date
Apr 26, 2022
Type of event by nationality
CST - Celostátní akce
UT code for WoS article
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