2015
From raw data to data-analysis for magnetic resonance spectroscopy - the missing link: jMRUI2XML
MOCIOIU, V, S ORTEGA-MARTORELL, I OLIER, Michal JABLONSKI, Jana STARČUKOVÁ et. al.Základní údaje
Originální název
From raw data to data-analysis for magnetic resonance spectroscopy - the missing link: jMRUI2XML
Autoři
MOCIOIU, V, S ORTEGA-MARTORELL, I OLIER, Michal JABLONSKI, Jana STARČUKOVÁ, P LISBOA, C ARUS a M JULIA-SAPE
Vydání
BMC Bioinformatics, London, BioMed Central, 2015, 1471-2105
Další údaje
Jazyk
angličtina
Typ výsledku
Článek v odborném periodiku
Utajení
není předmětem státního či obchodního tajemství
Odkazy
Impakt faktor
Impact factor: 2.435
UT WoS
000364364400001
Klíčová slova anglicky
Magnetic resonance spectroscopy; Pattern recognition; Signal processing; Software development
Změněno: 20. 10. 2017 17:10, Ing. Michal Jablonski, Ph.D.
Anotace
V originále
Background: Magnetic resonance spectroscopy provides metabolic information about living tissues in a non-invasive way. However, there are only few multi-centre clinical studies, mostly performed on a single scanner model or data format, as there is no flexible way of documenting and exchanging processed magnetic resonance spectroscopy data in digital format. This is because the DICOM standard for spectroscopy deals with unprocessed data. This paper proposes a plugin tool developed for jMRUI, namely jMRUI2XML, to tackle the latter limitation. jMRUI is a software tool for magnetic resonance spectroscopy data processing that is widely used in the magnetic resonance spectroscopy community and has evolved into a plugin platform allowing for implementation of novel features. Results: jMRUI2XML is a Java solution that facilitates common preprocessing of magnetic resonance spectroscopy data across multiple scanners. Its main characteristics are: 1) it automates magnetic resonance spectroscopy preprocessing, and 2) it can be a platform for outputting exchangeable magnetic resonance spectroscopy data. The plugin works with any kind of data that can be opened by jMRUI and outputs in extensible markup language format. Data processing templates can be generated and saved for later use. The output format opens the way for easy data sharing-due to the documentation of the preprocessing parameters and the intrinsic anonymization -for example for performing pattern recognition analysis on multicentre/multimanufacturer magnetic resonance spectroscopy data. Conclusions: jMRUI2XML provides a self-contained and self-descriptive format accounting for the most relevant information needed for exchanging magnetic resonance spectroscopy data in digital form, as well as for automating its processing. This allows for tracking the procedures the data has undergone, which makes the proposed tool especially useful when performing pattern recognition analysis. Moreover, this work constitutes a first proposal for a minimum amount of information that should accompany any magnetic resonance processed spectrum, towards the goal of achieving better transferability of magnetic resonance spectroscopy studies.