Predicting carbon spectrum in heteronuclear single quantum coherence spectroscopy for online feedback during surgery
Author(s)
Advisor
Çiçek, A. ErcümentDate
2020-09Publisher
Bilkent University
Language
English
Type
ThesisItem Usage Stats
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Abstract
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 and
prohibits real time analysis. Thus, obtaining 2D spectrum fast has major implications in medicine. 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. Learning is possible with small sample sizes
and without the need for performing the HSQC analysis, we can predict the 13C
dimension by just performing 1H HRMAS NMR experiment. We show on a rat
model of central nervous system tissues (80 samples, 5 tissues) that our methods
achieve 0.971 and 0.957 mean R2 values, respectively. Our tests on 15 human
brain tumor samples show that we can predict 104 groups of 39 metabolites with
97% accuracy. Finally, we show that we can predict the presence of a drug resistant tumor biomarker (creatine) despite obstructed signal in 1H dimension. In practice, this information can provide valuable feedback to the surgeon to further
resect the cavity to avoid potential recurrence.