Bilkent University
Department of Computer Engineering


Predicting Carbon Spectrum in Heteronuclear Single Quantum Coherence Spectroscopy for Online Feedback During Surgery


Emin Onur Karakaşlar
MS Student
Computer Engineering Department
Bilkent University

1H High-Resolution Magic Angle Spinning (HRMAS) Nuclear Magnetic Resonance (NMR) is a reliable technology used for detecting metabolites in solid tissues. Fast response time enables guiding surgeons in real time, for detecting tumor cells that are left over in the excision cavity. However, severe overlap of spectral resonances in 1D signal often render distinguishing metabolites impossible. In that case, Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) NMR is applied which can distinguish metabolites by generating 2D spectra (1H-13C). Unfortunately, this analysis requires much longer time. In this study, we show that using multiple multivariate regression and statistical total correlation spectroscopy, we can learn the relation between the 1H and 13C dimensions. We show on a rat model of central nervous system tissues that our methods achieve 0.971 and 0.957 mean R square values, respectively. Our tests on 15 human brain tumor samples show that we can predict 104 peaks of 39 metabolites with 97.1% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension.


DATE: 22 April 2019, Monday, CS590 presentations begin at @ 15:40