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Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements

Frédérick Dallaire, Fabien Picot, Jean-Philippe Tremblay, Guillaume Sheehy, Émile Lemoine, Rajeev Agarwal, Samuel Kadoury, Dominique Trudel, Frédéric Lesage, Kevin Petrecca and Frédéric Leblond

Article (2020)

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Terms of Use: Creative Commons Attribution .
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Cite this document: Dallaire, F., Picot, F., Tremblay, J.-P., Sheehy, G., Lemoine, É., Agarwal, R., ... Leblond, F. (2020). Quantitative spectral quality assessment technique validated using intraoperative in vivo Raman spectroscopy measurements. Journal of Biomedical Optics, 25(4). doi:10.1117/1.jbo.25.4.040501
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Significance: Ensuring spectral quality is prerequisite to Raman spectroscopy applied to surgery. This is because the inclusion of poor-quality spectra in the training phase of Raman-based pathology detection models can compromise prediction robustness and generalizability to new data. Currently, there exists no quantitative spectral quality assessment technique that can be used to either reject low-quality data points in existing Raman datasets based on spectral morphology or, perhaps more importantly, to optimize the in vivo data acquisition process to ensure minimal spectral quality standards are met. Aim: To develop a quantitative method evaluating Raman signal quality based on the variance associated with stochastic noise in important tissue bands, including C─C stretch, CH2  /  CH3 deformation, and the amide bands. Approach: A single-point hand-held Raman spectroscopy probe system was used to acquire 315 spectra from 44 brain cancer patients. All measurements were classified as either high or low quality based on visual assessment (qualitative) and using a quantitative quality factor (QF) metric. Receiver-operator-characteristic (ROC) analyses were performed to evaluate the performance of the quantitative metric to assess spectral quality and improve cancer detection accuracy. Results: The method can separate high- and low-quality spectra with a sensitivity of 89% and a specificity of 90% which is shown to increase cancer detection sensitivity and specificity by up to 20% and 12%, respectively. Conclusions: The QF threshold is effective in stratifying spectra in terms of spectral quality and the observed false negatives and false positives can be linked to limitations of qualitative spectral quality assessment.

Uncontrolled Keywords

Raman spectroscopy; Tissues; Signal to noise ratio; Cancer; In vivo imaging; Brain; Data acquisition; Luminescence; Tissue optics; Visualization;

Open Access document in PolyPublie
Subjects: 3500 Chimie analytique > 3505 Spectroscopie analytique
Department: Département de génie informatique et génie logiciel
Département de génie physique
Research Center: Non applicable
Funders: TransMedTech Institute, Natural Sciences, Engineering Research Council of Canada (NSERC), Collaborative Health Research Program (CIHR, NSERC), ODS Medical, Mitacs
Date Deposited: 22 May 2020 15:30
Last Modified: 23 May 2020 01:20
PolyPublie URL: https://publications.polymtl.ca/5230/
Document issued by the official publisher
Journal Title: Journal of Biomedical Optics (vol. 25, no. 4)
Publisher: SPIE
Official URL: https://doi.org/10.1117/1.jbo.25.4.040501


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