Lineshape estimation for magnetic resonance spectroscopy (MRS) signals: self-deconvolution revisited

Magnetic resonance spectroscopy (MRS) is an effective diagnostic technique for monitoring biochemical changes in an organism. The lineshape of MRS signals can deviate from the theoretical Lorentzian lineshape due to inhomogeneities of the magnetic field applied to patients and to tissue heterogeneity. We call this deviation a distortion and study the self-deconvolution method for automatic estimation of the unknown lineshape distortion. The method is embedded within a time-domain metabolite quantitation algorithm for short-echo-time MRS signals. Monte Carlo simulations are used to analyze whether estimation of the unknown lineshape can improve the overall quantitation result. We use a signal with eight metabolic components inspired by typical MRS signals from healthy human brain and allocate special attention to the step of denoising and spike removal in the self-deconvolution technique. To this end, we compare several modeling techniques, based on complex damped exponentials, splines and wavelets. Our results show that self-deconvolution performs well, provided that some unavoidable hyper-parameters of the denoising methods are well chosen. Comparison of the first and last iterations shows an improvement when considering iterations instead of a single step of self-deconvolution.