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Gannet: A batch‐processing tool for the quantitative analysis of gamma‐aminobutyric acid–edited MR spectroscopy spectra
Author(s) -
Edden Richard A.E.,
Puts Nicolaas A.J.,
Harris Ashley D.,
Barker Peter B.,
Evans C. John
Publication year - 2014
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.24478
Subject(s) - computer science , raw data , domain (mathematical analysis) , artificial intelligence , mathematics , mathematical analysis , programming language
Purpose The purpose of this study is to describe the Gannet toolkit for the quantitative batch analysis of gamma‐aminobutyric acid (GABA) ‐edited MRS data. Materials and Methods Using MEGA‐PRESS editing and standard acquisition parameters, four MEGA‐PRESS spectra were acquired in three brain regions in 10 healthy volunteers. These 120 datasets were processed without user intervention with Gannet, a Matlab‐based tool that takes raw time‐domain data input, processes it to generate the frequency‐domain edited spectrum, and applies a simple modeling procedure to estimate GABA concentration relative to the creatine or, if provided, the unsuppressed water signal. A comparison of four modeling approaches is also presented. Results All data were successfully processed by Gannet. Coefficients of variation across subjects ranged from 11% for the occipital region to 17% for the dorsolateral prefrontal region. There was no clear difference in fitting performance between the simple Gaussian model used by Gannet and the other more complex models presented. Conclusion Gannet, the GABA Analysis Toolkit, can be used to process and quantify GABA‐edited MRS spectra without user intervention. J. Magn. Reson. Imaging 2014;40:1445–1452 . © 2013 Wiley Periodicals, Inc .