In the low-field medical magnetic resonance imaging (MRI) system, the original digital MR signal is generated with high sampling rate and a large amount of noise. In this paper, we propose a wavelet transform-based preprocessing algorithm for this MR signal, in order to eliminate the noise, reduce the sampling rate and compress the memory of data. We select Daubechies filter as our decomposition filter and perform multi-level wavelet decomposition on the MR signal. The scaling function coefficients are obtained at the levels of decomposition, and taken as the low-frequency signal component. So that fast filtering and multistage decimation without spectrum-aliasing are realized. The experiment based on the permanent magnetism resonance imaging system proves the efficiency and practicability of this algorithm.

Date Published: 30 October 2009
PDF: 7 pages
Proc. SPIE 7497, MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques, 749720 (30 October 2009); doi: 10.1117/12.832538

Published in SPIE Proceedings Vol. 7497:
MIPPR 2009: Medical Imaging, Parallel Processing of Images, and Optimization Techniques
Faxiong Zhang; Faxiong Zhang, Editor(s)