فهرست نشریات علمی دسترسی آزاد ایران

Background: Performance of quantification methods utilized for proton magnetic resonance spectroscopy (1H-MRS), implemented either in time-domain or in frequency-domain, is limited due to static field (B0) inhomogeneities and the overlapping nature of metabolites in actual low-SNR environments. Sparse representation methods for MRS quantification have suggested robust and high performance algorithms which have been previously implemented based on Gaussian and Lorentzian models, to be selected by some pursuit methods, e.g. parallel basis selection method based on the focal underdetermined system solver (FOCUSS) algorithm. FOCUSS algorithm performs well in correlated environments, however it is computationally more intense compared to the selection methods. Objectives: Here, we proposed a sparse quantification method for 1H-MRS in time-frequency domain, achieved by continues wavelet transform (CWT), and employing sparse features of the whole simulated metabolites spectra existing in frequency, as well as properties of profiles in time. Stability and accuracy of the proposed technique was confirmed by simulated and phantom data, resulting in correct quantification of the metabolites of interest in 1H-MRS signals of brain. Results: PRMSE of the quantification for 5 metabolites was shown in three types of signals: 1) Simulated signal with infinite signal-to-noise ratio (SNR), quantified by the simulated dictionary; 2) Simulated signal with SNR = 10 dB, quantified by the simulated dictionary; and 3) Signal acquired from 7 different types of solutions in the phantom, quantified by the phantom-based dictionary. Conclusions: Results show that the proposed method can quantify metabolites of 1H-MRS signal with low level of error (< 1% for simulated signal (SNR = infinite), < 3% for simulated signal (SNR = 10) and < 9% for phantom signal with the dictionary based on phantom). The proposed procedure finds the sparse representation of the signal after exploiting almost all information of the signal in the linear sparse combination of a number of dictionary profiles after being transformed onto the time-frequency domain. Using metabolites profile acquired from the phantom also added to the accuracy of metabolites estimation.