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sRAKI-RNN: accelerated MRI with scan-specific recurrent neural networks using densely connected blocks
Author(s): Seyed Amir Hossein Hosseini; Chi Zhang; Kâmil Uǧurbil; Steen Moeller; Mehmet Akçakaya
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Paper Abstract

This study aims to improve upon Self-consistent Robust Artificial-neural-networks for k-space Interpolation (sRAKI), which is a deep learning-based parallel imaging technique for accelerated MRI reconstruction. The proposed technique, called sRAKI-RNN, combines the calibration and reconstruction phases of sRAKI into a single step that jointly learns the self-consistency rule and performs iterative reconstruction using recurrent neural networks (RNN). Similar to sRAKI, sRAKI-RNN supports arbitrary undersampling patterns and is a databasefree technique that is trained on autocalibrating signal (ACS) data from the same scan. Densely connected blocks are used in each iteration of the RNN to improve the convergence during the learning phase. sRAKI-RNN was evaluated on targeted right coronary artery (RCA) MRI. The results indicate that sRAKI-RNN further improves the noise resilience of sRAKI in a shorter running time and also considerably outperforms its linear counterpart, SPIRiT, in suppressing reconstruction noise.

Paper Details

Date Published: 9 September 2019
PDF: 7 pages
Proc. SPIE 11138, Wavelets and Sparsity XVIII, 111381B (9 September 2019); doi: 10.1117/12.2527949
Show Author Affiliations
Seyed Amir Hossein Hosseini, Univ. of Minnesota, Twin Cities (United States)
Chi Zhang, Univ. of Minnesota, Twin Cities (United States)
Kâmil Uǧurbil, Univ. of Minnesota, Twin Cities (United States)
Steen Moeller, Univ. of Minnesota, Twin Cities (United States)
Mehmet Akçakaya, Univ. of Minnesota, Twin Cities (United States)


Published in SPIE Proceedings Vol. 11138:
Wavelets and Sparsity XVIII
Dimitri Van De Ville; Manos Papadakis; Yue M. Lu, Editor(s)

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