Abdominal imaging is one of the important clinical applications of magnetic resonance imagining, but image degradation due to respiratory motion remains a major problem. Retrospective respiratory navigator gating technique is an effective approach to alleviate such degradation but is subject to long scan time and low signal-to-noise ratio (SNR) efficiency. In this study, a modified retrospective navigator gating technique with variable over-sampling ratio acquisition and weighted average reconstruction algorithm is presented. Experiments in phantom and the imaging results of seven volunteers demonstrated that the proposed method provided an enhanced SNR and reduced ghost-to-image ratio compared to the conventional method. The proposed method can also be used to reduce imaging time while maintaining comparable image quality.
Simultaneous recording of electroencephalogram (EEG)-functional magnetic resonance imaging (fMRI) plays an important role in scientific research and clinical field due to its high spatial and temporal resolution. However, the fusion results are seriously influenced by ballistocardiogram (BCG) artifacts under MRI environment. In this paper, we improve the off-line constrained independent components analysis using real-time technique (rt-cICA), which is applied to the simulated and real resting-state EEG data. The results show that for simulated data analysis, the value of error in signal amplitude (Er) obtained by rt-cICA method was obviously lower than the traditional methods such as average artifact subtraction (P<0.005). In real EEG data analysis, the improvement of normalized power spectrum (INPS) calculated by rt-cICA method was much higher than other methods (P<0.005). In conclusion, the novel method proposed by this paper lays the technical foundation for further research on the fusion model of EEG-fMRI.
Transcranial magnetic stimulation (TMS) combined with electroencephalography(EEG) has become an important tool in brain research. However, it is difficult to remove the large artifacts in EEG signals caused by the online TMS intervention. In this paper, we summed up various types of artifacts. After introducing a variety of online methods, the paper emphasized on offline approaches, such as subtraction, principal component analysis and independent component analysis, which can remove or minimize TMS-induced artifacts according to their different characteristics. Although these approaches can deal with most of the artifacts induced by TMS, the removal of large artifacts still needs to be improved. This paper systematically summarizes the effective methods for artifacts removal in TMS-EEG studies. It is a good reference for TMS-EEG researchers while choosing the suitable artifacts removal methods.
In order to solve imperfection of heart rate extraction by method of traditional ballistocardiogram (BCG), this paper proposes an improved method for detecting heart rate by BCG. First, weak cardiac activity signals are acquired in real time by embedded sensors. Local BCG beats are obtained by signal filtering and signal conversion. Second, the heart rate is estimated directly from the BCG beat without the use of a heartbeat template. Compared with other methods, the proposed method has strong advantages in heart rate data accuracy and anti-interference, and it also realizes non-contact online detection. Finally, by analyzing the data of more than 20,000 heart rates of 13 subjects, the average beat error was 0.86% and the coverage was 96.71%. It provides a new way to estimate heart rate for hospital clinical and home care.
The brain-computer interface (BCI) systems used in practical applications require as few electroencephalogram (EEG) acquisition channels as possible. However, when it is reduced to one channel, it is difficult to remove the electrooculogram (EOG) artifacts. Therefore, this paper proposed an EOG artifact removal algorithm based on wavelet transform and ensemble empirical mode decomposition. Firstly, the single channel EEG signal is subjected to wavelet transform, and the wavelet components which involve EOG artifact are decomposed by ensemble empirical mode decomposition. Then the predefined autocorrelation coefficient threshold is used to automatically select and remove the intrinsic modal functions which mainly composed of EOG components. And finally the ‘clean’ EEG signal is reconstructed. The comparative experiments on the simulation data and the real data show that the algorithm proposed in this paper solves the problem of automatic removal of EOG artifacts in single-channel EEG signals. It can effectively remove the EOG artifacts when causes less EEG distortion and has less algorithm complexity at the same time. It helps to promote the BCI technology out of the laboratory and toward commercial application.