Semi-Classical Signal Analysis Method with Soft-Thresholding for MRS denoising

Abstract

A Semi-Classical Signal Analysis (SCSA) method with soft thresholding is proposed for MRSI denoising. The SCSA takes advantage of the pulseshaped MRS spectrum to decompose both real and imaginary parts, into localized basis given by squared eigenfunctions of the Schrödinger operator. An optimization-based soft-threshold is provided to find optimal semi-classical parameters, for both the real and imaginary parts of the MRS signal. The optimal SCSA parameters discard the eigenfunctions representing noise from the noisy spectrum, and conserve the eigenfunctions representing the useful information. The obtained in-vivo results show the efficiency of the SCSA with soft thresholding in removing noise and conserving metabolite signals.