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Suppressing Multi-Channel Ultra-Low-Field MRI Measurement Noise Using Data Consistency and Image Sparsity |
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| Authors: | 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 |
| Institution: | 1. Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan.; 2. Department of Biomedical Engineering and Computational Science, Aalto University School of Science, Espoo, Finland.; 3. Department of Mathematics, National Taiwan University, Taipei, Taiwan.; 4. Elekta Oy, Helsinki, Finland.; University of Maryland - College Park, United States of America, |
| 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. |
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