A Plug-and-Play Deep Denoiser Prior Model for Accelerated MRI Reconstruction

Hasan H. Karaoglu, Ender M. Eksioglu

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

Özet

Magnetic resonance imaging (MRI) reconstruction is one of the important inverse imaging problems. Unlike the classical MRI approaches which demand long scanning time and are prone to reconstruction artifacts, compressed sensing MRI (CS-MRI) generates the scans data relatively faster and produces less artifacts for medical diagnosis. Model-based CS-MRI algorithms require long reconstruction time to obtain an MR image. On the other hand, although training time of deep learning techniques for the task is rather long, their reconstruction time is much shorter compared to iterative model-based MRI algorithms. Moreover, recent works have shown that Gaussian denoisers including deep denoisers can be utilized to solve the inverse problems in a plug-and-play fashion. In this paper, we propose an iterative convolutional neural network based Gaussian denoiser as a solver for the CS-MRI problem. Our experiments show that the proposed method has better reconstruction ability when compared to some important model-based and deep learning based methods from the literature.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığı2022 45th International Conference on Telecommunications and Signal Processing, TSP 2022
EditörlerNorbert Herencsar
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
Sayfalar260-263
Sayfa sayısı4
ISBN (Elektronik)9781665469487
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik45th International Conference on Telecommunications and Signal Processing, TSP 2022 - Virtual, Online, Czech Republic
Süre: 13 Tem 202215 Tem 2022

Yayın serisi

Adı2022 45th International Conference on Telecommunications and Signal Processing, TSP 2022

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???event.eventtypes.event.conference???45th International Conference on Telecommunications and Signal Processing, TSP 2022
Ülke/BölgeCzech Republic
ŞehirVirtual, Online
Periyot13/07/2215/07/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Finansman

This work is supported by TUBITAK (The Scientific and Technological Research Council of Turkey) under project number 119E248.

FinansörlerFinansör numarası
Türkiye Bilimsel ve Teknolojik Araştırma Kurumu119E248

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