Noise suppression of proton magnetic resonance spectroscopy improves paediatric brain tumour classification

Abstract

Proton magnetic resonance spectroscopy (¹H-MRS) is increasingly used for clinical brain tumour diagnosis, but suffers from limited spectral quality. This retrospective and comparative study aims at improving paediatric brain tumour classification by performing noise suppression on clinical ¹H-MRS. Eighty-three/forty-two children with either an ependymoma (ages 4.6 $\pm$ 5.3/9.3 $\pm$ 5.4), a medulloblastoma (ages 6.9 $\pm$ 3.5/6.5 $\pm$ 4.4), or a pilocytic astrocytoma (8.0 $\pm$ 3.6/6.3 $\pm$ 5.0), recruited from four centres across England, were scanned with 1.5T/3T short-echo-time point-resolved spectroscopy. The acquired raw ¹H-MRS was quantified by using Totally Automatic Robust Quantitation in NMR (TARQUIN), assessed by experienced spectroscopists, and processed with adaptive wavelet noise suppression (AWNS). Metabolite concentrations were extracted as features, selected based on multiclass receiver operating characteristics, and finally used for identifying brain tumour types with supervised machine learning. The minority class was oversampled through the synthetic minority oversampling technique for comparison purposes. Post-noise-suppression ¹H-MRS showed significantly elevated signal-to-noise ratios (P < .05, Wilcoxon signed-rank test), stable full width at half-maximum (P > .05, Wilcoxon signed-rank test), and significantly higher classification accuracy (P < .05, Wilcoxon signed-rank test). Specifically, the cross-validated overall and balanced classification accuracies can be improved from 81% to 88% overall and 76% to 86% balanced for the 1.5T cohort, whilst for the 3T cohort they can be improved from 62% to 76% overall and 46% to 56%, by applying Naïve Bayes on the oversampled ¹H-MRS. The study shows that fitting-based signal-to-noise ratios of clinical ¹H-MRS can be significantly improved by using AWNS with insignificantly altered line width, and the post-noise-suppression ¹H-MRS may have better diagnostic performance for paediatric brain tumours.

Keywords: machine learning; metabolite concentration; noise suppression; paediatric brain tumour; proton magnetic resonance spectroscopy; wavelet.