Hankel Singular Value Decomposition as a method of preprocessing the Magnetic Resonance Spectroscopy

TL;DR

This paper introduces a preprocessing method for magnetic resonance spectroscopy signals using Hankel Singular Value Decomposition (HSVD) to model signals as harmonic sums, aiming to improve analysis accuracy.

Contribution

The paper develops a novel HSVD-based preprocessing approach for MRS signals, tested on real phantom data, enhancing signal modeling and noise reduction.

Findings

Effective noise suppression in MRS signals

Accurate harmonic parameter estimation

Improved preprocessing for spectral analysis

Abstract

The signal resulting from magnetic resonance spectroscopy is occupied by noises and irregularities so in the further analysis preprocessing techniques have to be introduced. The main idea of the paper is to develop a model of a signal as a sum of harmonics and to find its parameters. Such an approach is based on singular value decomposition applied to the data arranged in the Hankel matrix (HSVD) and can be used in each step of preprocessing techniques. For that purpose a method has was tested on real phantom data.