A quantitative comparison of metabolite signals as detected by in vivo MRS with ex vivo (1)H HR-MAS for childhood brain tumours.

Martin Wilson, Nigel Davies, RG Grundy, Andrew Peet

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41 Citations (Scopus)


(1)H MRS provides a powerful method for investigating tumour metabolism by allowing the measurement of metabolites in vivo. Recently, the technique of (1)H high-resolution magic angle spinning (HR-MAS) has been shown to produce high-quality data, allowing the accurate measurement of many metabolites present in unprocessed biopsy tissue. The purpose of this study was to evaluate the agreement between the techniques of in vivo MRS and ex vivo HR-MAS for investigating childhood brain tumours. Short-TE (30 ms), single-voxel, in vivo MRS was performed on 16 paediatric patients with brain tumours at 1.5 T. A frozen biopsy sample was available for each patient. HR-MAS was performed on the biopsy samples, and metabolite quantities were determined from the MRS and HR-MAS data using the LCModeltrade mark and TARQUIN algorithms, respectively. Linear regression was performed on the metabolite quantities to asses the agreement between MRS and HR-MAS. Eight of the 12 metabolite quantities were found to correlate significantly (P <0.05). The four worst correlating metabolites were aspartate, scyllo-inositol, glycerophosphocholine and N-acetylaspartate, and, except for glycerophosphocholine, this error was reflected in their higher Cramer-Rao lower bounds (CRLBs), suggesting that low signal-to-noise was the greatest source of error for these metabolites. Glycerophosphocholine had a lower CRLB implying that interference with phosphocholine and choline was the most significant source of error. The generally good agreement observed between the two techniques suggests that both MRS and HR-MAS can be used to reliably estimate metabolite quantities in brain tumour tissue and that tumour heterogeneity and metabolite degradation do not have an important effect on the HR-MAS metabolite profile for the tumours investigated. HR-MAS can be used to improve the analysis and understanding of MRS data. Copyright (c) 2009 John Wiley & Sons, Ltd.
Original languageEnglish
Pages (from-to)213-9
Number of pages7
JournalNMR in biomedicine
Issue number2
Publication statusPublished - 1 Feb 2009


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