Strategies for optimizing the phase correction algorithms in Nuclear Magnetic Resonance spectroscopy.
Nuclear Magnetic Resonance (NMR) spectroscopy is a popular medical diagnostic technique. NMR is also the favourite tool of chemists/biochemists to elucidate the molecular structure of small or big molecules; it is also a widely used tool in material science, in food science etc. In the case of medical diagnosis it allows for determining a metabolic composition of analysed tissue which may support the identification of tumour cells. Precession signal, that is a crucial part of MR phenomenon, contains distortions that must be filtered out before signal analysis. One of such distortions is phase error. Five popular algorithms: Automics, Shanon’s entropy minimization, Ernst’s method, Dispa and eDispa are presented and discussed. A novel adaptive tuning algorithm for Automics method was developed and numerically optimal solutions to automatic tuning of the other four algorithms were proposed. To validate the performance of the proposed techniques, two experiments were performed - the first one was done with the use of in silico generated data. For all presented methods, the fine tuning strategies significantly increased the correction accuracy. The highest improvement was observed for Automics algorithm, independently of noise level, with relative phase error dropping by average from 10.25% to 2.40% for low noise level and from 12.45% to 2.66% for high noise level. The second validation experiment, done with the use of phantom data, confirmed the in silico results. The obtained accuracy of the estimation of metabolite concentration was at 99.5%.


CONCLUSIONS
The proposed strategies for optimizing the phase correction algorithms significantly improve the accuracy of Nuclear Magnetic Resonance spectroscopy signal analysis.
Franciszek
F
Binczyk
Rafal
R
Tarnawski
Joanna
J
Polanska
D000465:Algorithms; D001921:Brain; D003198:Computer Simulation; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D019047:Phantoms, Imaging; D013223:Statistics as Topic / Q000379:methods*