Unfolded Low-rank + Sparse Reconstruction for MRI

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F22%3APU144477" target="_blank" >RIV/00216305:26220/22:PU144477 - isvavai.cz</a>
Alternative codes found
RIV/68081731:_____/22:00568850
Result on the web
—
DOI - Digital Object Identifier
—

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
—
Czech description
—

Project
<a href="/en/project/EF19_073%2F0016948" target="_blank" >EF19_073/0016948: Quality internal grants at BUT</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

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
—
e-ISSN
—
Number of pages
5
Pages from-to
271-275
Publisher name
Brno University of Technology, Faculty of Electrical Engineering
Place of publication
Brno
Event location
Brno
Event date
Apr 26, 2022
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—

Similar results(10)