A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data

A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data

Bibliographic Details
Main Author: Wang, Xin
Language:English
Published: University of Cincinnati / OhioLINK 2009
Subjects:
Biomedical Research
MR spectroscopy
quantitation
HSVD
<sup>31</sup>P
algorithm
4T
prior knowledge
in vivo
Online Access:http://rave.ohiolink.edu/etdc/view?acc_num=ucin1236271757
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spelling ndltd-OhioLink-oai-etd.ohiolink.edu-ucin12362717572021-08-03T06:13:08Z A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data Wang, Xin Biomedical Research MR spectroscopy quantitation HSVD <sup>31</sup>P algorithm 4T prior knowledge in vivo The rapid development of magnetic resonance spectroscopy (MRS) greatly facilitates non-invasive measurement of brain metabolites, which makes it a versatile diagnostic procedure for biomedical research. The validity and dependability of MRSdata relies on the accuracy and efficiency of data post-processing and quantification analysis. Throughout the years, various quantification methods have been proposed and implemented in both <sup>31</sup>P and <sup>1</sup>H spectrum analysis. The frequency variation of certain chemical compounds of interest and serious baseline distortions remain the primary challenges for post-processing in large volume <i>in vivo</i> <sup>31</sup>P MRS. This work aims to undertake these problems by developing a Hankel Singular Value Decomposition(HSVD) based adaptive prior knowledge algorithm that can intelligently guide itself to an optimal result. This algorithm uses so called interference signals to optimize prior knowledge iteratively for parameter optimization. The purpose of this approach is to improve the quantification quality of MRS signals from different brain locations as well as from different experimental environments. To achieve this goal, we developed an algorithm termed Iterative Reduction of Interference Signal - HSVD (IRIS-HSVD). The Monte Carlo evaluations of the algorithm were conducted with simulated data using relevant in vivo parameters. The performance of this algorithm was compared to those of other automatic methods including HSVD and HTLS-PK. Examples of <i>in vivo</i> <sup>31</sup>P data obtained from brains of healthy subjects on a 4T MRI scanner were also presented, which demonstrated the superiority of the new method as compared to AMARES, a widely used program in the NMR community. 2009-04-20 English text University of Cincinnati / OhioLINK http://rave.ohiolink.edu/etdc/view?acc_num=ucin1236271757 http://rave.ohiolink.edu/etdc/view?acc_num=ucin1236271757 unrestricted This thesis or dissertation is protected by copyright: all rights reserved. It may not be copied or redistributed beyond the terms of applicable copyright laws.
collection NDLTD
language English
sources NDLTD
topic Biomedical Research
MR spectroscopy
quantitation
HSVD
<sup>31</sup>P
algorithm
4T
prior knowledge
in vivo
spellingShingle Biomedical Research
MR spectroscopy
quantitation
HSVD
<sup>31</sup>P
algorithm
4T
prior knowledge
in vivo
Wang, Xin
A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
author Wang, Xin
author_facet Wang, Xin
author_sort Wang, Xin
title A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
title_short A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
title_full A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
title_fullStr A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
title_full_unstemmed A Novel Approach for Automatic Quantitation of <sup>31</sup>P Magnetic Resonance Spectroscopy Data
title_sort novel approach for automatic quantitation of <sup>31</sup>p magnetic resonance spectroscopy data
publisher University of Cincinnati / OhioLINK
publishDate 2009
url http://rave.ohiolink.edu/etdc/view?acc_num=ucin1236271757
work_keys_str_mv AT wangxin anovelapproachforautomaticquantitationofsup31suppmagneticresonancespectroscopydata
AT wangxin novelapproachforautomaticquantitationofsup31suppmagneticresonancespectroscopydata
_version_ 1719432934686982144

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