In this paper, the Fourier transform based minimum mean square error (FT-based MMSE) method is used to calculate the regional cerebral blood volume (rCBV) in magnetic resonance (MR) perfusion imaging, and the method is improved to handle the existing noise in the imaging process. In the experiments with signal-to-noise ratio (SNR) of 50 dB, the rCBV values were compared with the results using MMSE method. The effects of different SNRs on the estimation of rCBV were analyzed. The experimental results showed that MMSE was a simple way to filter the measurement noise, and could calculate rCBV accurately. Compared with other existing methods, the present method is not sensitive to environment, and furthermore, it is suitable to deal with the perfusion images acquired from the environment with larger SNR.
Detecting and imaging method of biological electrical characteristics based on magneto-acoustic coupling effect gives valuable information of tissue in early tumor diagnosis and bioelectrical current monitoring. Normal exciting and receiving method is to use single pulse. In this method the signal to noise ratio (SNR) is limited, so the imaging quality and imaging speed are low. In this study, we propose a processing method based on coded excitation to improve SNR and shorten the processing time. The processing method using 13 bit Barker coded excitation and 16 bit Golay code excitation are studied by simulation and experiments. The results show that SNR of magneto-acoustic signal is improved by 20.96 dB and 20.62 dB by using 13 bit Barker coded and 16 bit Golay coded excitation, respectively. It also indicates the processing time is short compare to single pulse mode. In the case of the SNR increasing, the overall acquiring and processing time under 13 bit Barker coded excitation and the 16 bit Golay coded excitation is shortened to 3.62% and 4.73%, respectively, compared to the single pulse excitation with waveform averaging method. In conclusion, the coded excitation will be significant for the improvement of magneto-acoustic signal SNR and imaging quality.
Spike recorded by multi-channel microelectrode array is very weak and susceptible to interference, whose noisy characteristic affects the accuracy of spike detection. Aiming at the independent white noise, correlation noise and colored noise in the process of spike detection, combining principal component analysis (PCA), wavelet analysis and adaptive time-frequency analysis, a new denoising method (PCWE) that combines PCA-wavelet (PCAW) and ensemble empirical mode decomposition is proposed. Firstly, the principal component was extracted and removed as correlation noise using PCA. Then the wavelet-threshold method was used to remove the independent white noise. Finally, EEMD was used to decompose the noise into the intrinsic modal function of each layer and remove the colored noise. The simulation results showed that PCWE can increase the signal-to-noise ratio by about 2.67 dB and decrease the standard deviation by about 0.4 μV, which apparently improved the accuracy of spike detection. The results of measured data showed that PCWE can increase the signal-to-noise ratio by about 1.33 dB and reduce the standard deviation by about 18.33 μV, which showed its good denoising performance. The results of this study suggests that PCWE can improve the reliability of spike signal and provide an accurate and effective spike denoising new method for the encoding and decoding of neural signal.