Scan-Specific Residual Convolutional Neural Networks for Fast MRI Using Residual RAKI

Chi Zhang, Seyed Amir Hossein Hosseini, Steen Moeller, Sebastian Weingartner, Kamil Ugurbil, Mehmet Akcakaya

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Parallel imaging is a widely-used acceleration technique for magnetic resonance imaging (MRI). Conventional linear reconstruction approaches in parallel imaging suffer from noise amplification. Recently, a non-linear method that utilizes subject-specific convolutional neural networks for k-space reconstruction, Robust Artificial-neural-networks for k-space Interpolation (RAKI) was proposed and shown to improve noise resilience over linear methods. However, the linear convolutions still provide a sufficient baseline image quality and interpretability. In this paper, we sought to utilize a residual network architecture to combine the benefits of both the linear and non-linear RAKI reconstructions. This hybrid method, called residual RAKI (rRAKI) offers significant improvement in image quality compared to linear method, and improves upon RAKI in highly-accelerated simultaneous multi-slice imaging. Furthermore, it establishes an interpretable view for the use of CNNs in parallel imaging, as the CNN component in the residual network removes the noise amplification arising from the linear part.

Original languageEnglish (US)
Title of host publicationConference Record - 53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
EditorsMichael B. Matthews
PublisherIEEE Computer Society
Pages1476-1480
Number of pages5
ISBN (Electronic)9781728143002
DOIs
StatePublished - Nov 2019
Event53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019 - Pacific Grove, United States
Duration: Nov 3 2019Nov 6 2019

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
Volume2019-November
ISSN (Print)1058-6393

Conference

Conference53rd Asilomar Conference on Circuits, Systems and Computers, ACSSC 2019
Country/TerritoryUnited States
CityPacific Grove
Period11/3/1911/6/19

Bibliographical note

Publisher Copyright:
© 2019 IEEE.

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