Transform learning MRI with global wavelet regularization

A. Korhan Tanc, Ender M. Eksioglu

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

3 Atıf (Scopus)

Özet

Sparse regularization of the reconstructed image in a transform domain has led to state of the art algorithms for magnetic resonance imaging (MRI) reconstruction. Recently, new methods have been proposed which perform sparse regularization on patches extracted from the image. These patch level regularization methods utilize synthesis dictionaries or analysis transforms learned from the patch sets. In this work we jointly enforce a global wavelet domain sparsity constraint together with a patch level, learned analysis sparsity prior. Simulations indicate that this joint regularization culminates in MRI reconstruction performance exceeding the performance of methods which apply either of these terms alone.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2015 23rd European Signal Processing Conference, EUSIPCO 2015
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar1855-1859
Sayfa sayısı5
ISBN (Elektronik)9780992862633
DOI'lar
Yayın durumuYayınlandı - 22 Ara 2015
Etkinlik23rd European Signal Processing Conference, EUSIPCO 2015 - Nice, France
Süre: 31 Ağu 20154 Eyl 2015

Yayın serisi

Adı2015 23rd European Signal Processing Conference, EUSIPCO 2015

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???event.eventtypes.event.conference???23rd European Signal Processing Conference, EUSIPCO 2015
Ülke/BölgeFrance
ŞehirNice
Periyot31/08/154/09/15

Bibliyografik not

Publisher Copyright:
© 2015 EURASIP.

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