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Optimized quantitative magnetic resonance spectroscopy for clinical routine
Author(s) -
Scheidegger Olivier,
Wingeier Kevin,
Stefan Dan,
GraveronDemilly Danielle,
van Ormondt Dirk,
Wiest Roland,
Slotboom Johannes
Publication year - 2013
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.24455
Subject(s) - dicom , computer science , in vivo magnetic resonance spectroscopy , protocol (science) , magnetic resonance imaging , metabolite , pulse sequence , visualization , software , medical physics , nuclear medicine , medicine , artificial intelligence , radiology , pathology , programming language , alternative medicine
Several practical obstacles in data handling and evaluation complicate the use of quantitative localized magnetic resonance spectroscopy (qMRS) in clinical routine MR examinations. To overcome these obstacles, a clinically feasible MR pulse sequence protocol based on standard available MR pulse sequences for qMRS has been implemented along with newly added functionalities to the free software package jMRUI‐v5.0 to make qMRS attractive for clinical routine. This enables (a) easy and fast DICOM data transfer from the MR console and the qMRS‐computer, (b) visualization of combined MR spectroscopy and imaging, (c) creation and network transfer of spectroscopy reports in DICOM format, (d) integration of advanced water reference models for absolute quantification, and (e) setup of databases containing normal metabolite concentrations of healthy subjects. To demonstrate the work‐flow of qMRS using these implementations, databases for normal metabolite concentration in different regions of brain tissue were created using spectroscopic data acquired in 55 normal subjects (age range 6–61 years) using 1.5T and 3T MR systems, and illustrated in one clinical case of typical brain tumor (primitive neuroectodermal tumor). The MR pulse sequence protocol and newly implemented software functionalities facilitate the incorporation of qMRS and reference to normal value metabolite concentration data in daily clinical routine. Magn Reson Med, 2013. © 2012 Wiley Periodicals, Inc.