Authors:
M. Staniszewski
1
;
F. Binczyk
1
;
A. Skorupa
2
;
L. Boguszewicz
2
;
M. Sokol
2
;
J. Polanska
1
and
A. Polanski
1
Affiliations:
1
Silesian University of Technology, Poland
;
2
Maria Sklodowska-Curie Memorial Cancer Center and Institute of Oncology and Gliwice Branch, Poland
Keyword(s):
Nuclear Magnetic Resonance Spectroscopy, Singular Value Decomposition, Gaussian Mixture Model.
Related
Ontology
Subjects/Areas/Topics:
Applications and Services
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Computer Vision, Visualization and Computer Graphics
;
Electromagnetic Fields in Biology and Medicine
;
Informatics in Control, Automation and Robotics
;
Medical Image Detection, Acquisition, Analysis and Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Time and Frequency Response
;
Time-Frequency Analysis
Abstract:
Analysis of NMR spectra is a multi-stage computational process performed with the use of appropriately
chosen sequence of algorithms. Initial stages of this process, called pre-processing, including filtering, baseline
correction, phase correction and removal of unwanted components, are aimed at improving the quality
of NMR spectral signal by rejection of noise, removing unnecessary spectral components and irregularities.
After pre-processing the basic operations on NMR spectra are aimed at estimation of levels of certain
metabolites by analysis of appropriate structural properties of NMR spectral signals. In this paper authors
present design and implementation of two signals modelling methods. The first one is based on singular
value decomposition of the induction decay signal. The second is done with use of mixture model
constructed for frequency spectrum. Authors present all assumption that need to be satisfied and processing
steps that must be performed before final analysis. The
methods studied in the paper are implemented under
the black - box assumption; i.e., prior knowledge of parameters of metabolites in the spectra is not used. As
a second part of the project authors present a comparison of obtained result with popular modelling
techniques and software LCmodel and Tarquin, based on experimental phantom dataset. Comparisons
between different methods are based on the commonly used quality indexes, mean squared errors
corresponding to levels of detected metabolites and specificities and sensitivities of the process of detection
of metabolites. Using the presented comparisons we authors are able to characterize advantages and
drawbacks of the studied approaches.
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