Suppressing multi-channel ultra-low-field MRI measurement noise using data consistency and image sparsity.
Where this comes from
- Record sourced from PubMed, PMID 23626710.
- Also identified by DOI 10.1371/journal.pone.0061652 and PMC identifier 3633989.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
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.
Medical subject headings
- Image Processing, Computer-Assisted
- Magnetic Resonance Imaging