Predicting carbon spectrum in heteronuclear single quantum coherence spectroscopy for online feedback during surgery

buir.advisorÇiçek, A. Ercüment
dc.contributor.authorKarakaşlar, Emin Onur
dc.departmentDepartment of Computer Engineeringen_US
dc.descriptionCataloged from PDF version of article.en_US
dc.descriptionThesis (M.S.): Bilkent University, Department of Computer Engineering, İhsan Doğramacı Bilkent University, 2020.en_US
dc.descriptionIncludes bibliographical references (leaves 26-29).en_US
dc.description.abstract1H 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.en_US
dc.description.statementofresponsibilityby Emin Onur Karakaşlaren_US
dc.format.extentxii, 44 leaves, 42 unnumbered leaves : charts ; 30 cm.en_US
dc.publisherBilkent Universityen_US
dc.subjectHRMAS NMRen_US
dc.subjectHSQC NMRen_US
dc.titlePredicting carbon spectrum in heteronuclear single quantum coherence spectroscopy for online feedback during surgeryen_US
dc.title.alternativeHeteronükleer tek kuantum uyumluluk spektroskopisi'ndeki karbon spektrumunun ameliyat esnasında eşzamanlı geridönüş için tahmin edilmesien_US


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