Baseline Correction of the Human (1)H MRS(I) Spectrum Using T(2)* Selective Differential Operators in the Frequency Domain

The baseline distortion caused by water and fat signals is a crucial issue in the (1)H MRS(I) study of the human brain. This paper suggests an effective and reliable preprocessing technique to calibrate the baseline distortion caused by the water and fat signals exhibited in the MRS spectral signal. For the preprocessing, we designed a T(2)* (or linewidth within the spectral signal) selective filter for the MRS(I) data based on differential filtering within the frequency domain. The number and types for the differential filtering were determined by comparing the T(2)* selectivity profile of each differential operator with the T(2)* profile of the metabolites to be suppressed within the MRS(I) data. In the performance evaluation of the proposed differential filtering, the simulation data for MRS spectral signals were used. Furthermore, the spectral signal of the human (1)H MRSI data obtained by 2D free induction decay chemical shift imaging with a typical water suppression technique was also used in the performance evaluation. The absolute values of the average of the filtered dataset were quantitatively analyzed using the LCModel software. With the suggested T(2)* selective (not frequency selective) filtering technique, in the simulated MRS data, we removed the metabolites from the simulated MRS(I) spectral signal baseline distorted by the water and fat signal observed in the most frequency band. Moreover, in the obtained MRSI data, the quantitative analysis results for the metabolites of interest showed notable improvement in the uncertainty estimation accuracy, the CRLB (Cramer-Rao Lower Bound) levels.