Unrolled L+S decomposition for compressed sensing in magnetic resonance imaging
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Date
2022-12-12
Authors
Mokrý, Ondřej
Vitouš, Jiří
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Mark
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International Society for Science and Engineering, o.s.
Abstract
Článek se zabývá metodou rekonstrukce dat pro dynamické zobrazování magnetickou rezonancí s využitím přístupu tzv. deep unrolling aplikovaného na low-rank + sparse model. Standardní algoritmus pro odhad tohoto modelu je rozšířen o trénovatelné prvky tvořící hlubokou neuronovou síť a na simulovaném datové množině je natrénováno několik variant rozvinutého algoritmu. Vyhodnocení oproti standardnímu algoritmu ukazuje zlepšení ve smyslu střední kvadratické chyby se stejnými výpočetními náklady.
A deep unrolled reconstruction method for dynamic magnetic resonance imaging is developed, based on the low-rank + sparse model. A standard solver of this model is enriched with trainable structures, forming a deep neural network, and several variants of the unrolled algorithm are trained on a simulated dataset. Evaluation against the standard solver for the model shows improvement in terms of mean squared error with the same computational cost.
A deep unrolled reconstruction method for dynamic magnetic resonance imaging is developed, based on the low-rank + sparse model. A standard solver of this model is enriched with trainable structures, forming a deep neural network, and several variants of the unrolled algorithm are trained on a simulated dataset. Evaluation against the standard solver for the model shows improvement in terms of mean squared error with the same computational cost.
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Elektrorevue. 2022, vol. 24, č. 3, s. 86-93. ISSN 1213-1539
http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/unrolled-l-s-decomposition-for-compressed-sensing-in-magnetic-resonance-imaging--rozbaleny-l-s-rozklad-pro-komprimovane-snimani-pri-zobrazovani-magnetickou-rezonanci-/
http://www.elektrorevue.cz/cz/clanky/zpracovani-signalu/0/unrolled-l-s-decomposition-for-compressed-sensing-in-magnetic-resonance-imaging--rozbaleny-l-s-rozklad-pro-komprimovane-snimani-pri-zobrazovani-magnetickou-rezonanci-/
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Peer-reviewed
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en
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(C) 2022 Elektrorevue