A constrained Gauss–Seidel method for correction of point spread function effect in MR spectroscopic imaging

Magnetic resonance spectroscopic imaging is limited by a low signal-to-noise ratio, so a compromise between spatial resolution and examination time is needed in clinical application. The reconstruction of truncated signal introduces a Point Spread Function that considerably affects the spatial resolution. In order to reduce spatial contamination, three methods, applied after Fourier transform image reconstruction, based on deconvolution or iterative techniques are tested to decrease Point Spread Function contamination. A Gauss-Seidel (GS) algorithm is used for iterative techniques with and without a non-negative constraint (GS+). Convergence and noise dependence studies of the GS algorithm have been done. The linear property of contamination was validated on a point sample phantom. A significant decrease of contamination without broadening the spatial resolution was obtained with GS+ method compared to a conventional apodization. This post-processing method can provide a contrast enhancement of clinical spectroscopic images without changes in acquisition time.