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Abstract #4686

Optimized pediatric brain tumor diagnosis by using in vivo magnetic resonance spectroscopy and machine learning

Dadi Zhao1,2, James T. Grist1,2, Yu Sun1,2, Vijay Sawlani1,3, and Andrew C. Peet1,2
1Institute of Cancer and Genomic Sciences, University of Birmingham, Birmingham, United Kingdom, 2Birmingham Children's Hospital NHS Foundation Trust, Birmingham, United Kingdom, 3Queen Elizabeth Hospital Birmingham NHS Foundation Trust, Birmingham, United Kingdom

Previous studies have shown that magnetic resonance spectroscopy (MRS) can provide diagnostic classifiers for childhood brain tumors. Here we investigate the effect of noise suppression on classification and build it into a pipeline aimed at optimizing the diagnosis. Although people have proposed several algorithms to suppress noise in MRS, the clinical application is still largely restricted to apodisation. We propose a wavelet-based framework to suppress the noise of in vivo MRS, thereby the signal quality of in vivo MRS and the classification accuracy of tumor cases was improved. The framework could be used in the clinical diagnosis through MRS.

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