SCSA based MATLAB pre-processing toolbox for 1H MR spectroscopic water suppression and denoising

A novel method for Magnetic Resonance Spectroscopy lipid signal suppression using Semi-classical signal analysis and Bidirectional Long short-term memory

Magnetic resonance spectroscopy (MRS) is a non-invasive method that enables the analysis and quantification of brain metabolites, which provide useful information about the neuro-biological substrates of brain function. Lactate plays a pivotal role in the diagnosis of various brain diseases. However, accurate lactate quantification is generally difficult to achieve due to the presence of large lipid peaks resonating at a similar spectral position. To overcome this problem several techniques have been proposed. However, most of them suffer from lactate signal loss or poor lipid peak removal. In this paper, a novel method for lipid suppression for MRS signal is proposed. The method combines a semi-classical signal analysis method and a bidirectional long short term memory technique. The method is validated using simulated data that mimics real MRS data.

A Quantum Weak Signal Detection Method for Strengthening Target Signal Features under Strong White Gaussian Noise

As the noise power increases, the target signal features become less obvious, which leads to the failure of weak signal detection methods. To address this problem, a quantum weak signal detection method, Local Semi-Classical Signal Analysis-Singular Value Decomposition (LSCSA-SVD), for strengthening target signal features under strong white Gaussian noise is proposed. Firstly, the time domain weak signal is quantized by the Schrodinger operator and its discrete spectrum formula. Then, in the quantum domain, the later eigenvalues are used to reconstruct the time domain signal, which can protect and enhance the target signal features. Finally, the difference between signal and noise in the singular value vector is used to further extract the reconstruction signal features. In simulation, the LSCSA-SVD can accurately extract target signals from white Gaussian noise signals with a signal-to-noise ratio (SNR) of −30 dB, which is better than the comparison methods. In the experiment, the weak acceleration sensor signal and the weak signal of the test circuit are successfully extracted. The results show that the LSCSA-SVD can suppress strong noise and improve the SNR.