MRS quality assessment in a multicentre study on MRS‐based classification of brain tumours

M van Der Graaf, M Julià‐Sapé, FA Howe… - NMR in …, 2008 - Wiley Online Library
M van Der Graaf, M Julià‐Sapé, FA Howe, A Ziegler, C Majós, A Moreno‐Torres…
NMR in Biomedicine, 2008Wiley Online Library
This paper reports on quality assessment of MRS in the European Union‐funded multicentre
project INTERPRET (International Network for Pattern Recognition of Tumours Using
Magnetic Resonance; http://azizu. uab. es/INTERPRET), which has developed brain tumour
classification software using in vivo proton MR spectra. The quality assessment consisted of
both MR system quality assurance (SQA) and quality control (QC) of spectral data acquired
from patients and healthy volunteers. The system performance of the MR spectrometers at …
Abstract
This paper reports on quality assessment of MRS in the European Union‐funded multicentre project INTERPRET (International Network for Pattern Recognition of Tumours Using Magnetic Resonance; http://azizu.uab.es/INTERPRET), which has developed brain tumour classification software using in vivo proton MR spectra. The quality assessment consisted of both MR system quality assurance (SQA) and quality control (QC) of spectral data acquired from patients and healthy volunteers. The system performance of the MR spectrometers at all participating centres was checked bimonthly by a short measurement protocol using a specially designed INTERPRET phantom. In addition, a more extended SQA protocol was performed yearly and after each hardware or software upgrade. To compare the system performance for in vivo measurements, each centre acquired MR spectra from the brain of five healthy volunteers. All MR systems fulfilled generally accepted minimal system performance for brain MRS during the entire data acquisition period. The QC procedure of the MR spectra in the database comprised automatic determination of the signal‐to‐noise ratio (SNR) in a water‐suppressed spectrum and of the line width of the water resonance (water band width, WBW) in the corresponding non‐suppressed spectrum. Values of SNR > 10 and WBW < 8 Hz at 1.5 T were determined empirically as conservative threshold levels required for spectra to be of acceptable quality. These thresholds only hold for SNR and WBW values using the definitions and data processing described in this article. A final QC check consisted of visual inspection of each clinically validated water‐suppressed metabolite spectrum by two, or, in the case of disagreement, three, experienced MR spectroscopists, to detect artefacts such as large baseline distortions, exceptionally broadened metabolite peaks, insufficient removal of the water line, large phase errors, and signals originating from outside the voxel. In the end, 10% of 889 spectra with completed spectroscopic judgement were discarded. Copyright © 2007 John Wiley & Sons, Ltd.
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