Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity

Fa-Hsuan Lin; Panu T Vesanen; Yi-Cheng Hsu; Jaakko O Nieminen; Koos C J Zevenhoven; Juhani Dabek; Lauri T Parkkonen; Juha Simola; Antti I Ahonen; Risto J Ilmoniemi

doi:10.1371/journal.pone.0061652

Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity

PLoS One. 2013 Apr 23;8(4):e61652. doi: 10.1371/journal.pone.0061652. Print 2013.

Authors

Fa-Hsuan Lin¹, Panu T Vesanen, Yi-Cheng Hsu, Jaakko O Nieminen, Koos C J Zevenhoven, Juhani Dabek, Lauri T Parkkonen, Juha Simola, Antti I Ahonen, Risto J Ilmoniemi

Affiliation

¹ Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan. fhlin@ntu.edu.tw

Abstract

Ultra-low-field (ULF) MRI (B 0 = 10-100 µT) typically suffers from a low signal-to-noise ratio (SNR). While SNR can be improved by pre-polarization and signal detection using highly sensitive superconducting quantum interference device (SQUID) sensors, we propose to use the inter-dependency of the k-space data from highly parallel detection with up to tens of sensors readily available in the ULF MRI in order to suppress the noise. Furthermore, the prior information that an image can be sparsely represented can be integrated with this data consistency constraint to further improve the SNR. Simulations and experimental data using 47 SQUID sensors demonstrate the effectiveness of this data consistency constraint and sparsity prior in ULF-MRI reconstruction.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Hand / anatomy & histology
Humans
Image Processing, Computer-Assisted / standards*
Image Processing, Computer-Assisted / statistics & numerical data
Magnetic Resonance Imaging / instrumentation
Magnetic Resonance Imaging / methods
Magnetic Resonance Imaging / standards*
Occipital Lobe / anatomy & histology
Phantoms, Imaging / standards
Signal-To-Noise Ratio

Grants and funding

This work was supported by grants from National Science Council, Taiwan (NSC 98-2320-B-002-004-MY3, NSC 100-2325-B-002-046), Ministry of Economic Affairs, Taiwan (100-EC-17-A-19-S1-175), the Academy of Finland (127624 and the FiDiPro program) and the European Community's Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 200859. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.