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Pipeline for the removal of hardware related artifacts and background noise for Raman spectroscopy.

ABSTRACT: Raman spectroscopy is a real-time, non-contact, and non-destructive technique able to obtain information about the composition of materials, chemicals, and mixtures. It uses the energy transfer properties of molecules to detect the composition of matter. Raman spectroscopy is mainly used in the chemical field because background fluorescence and instrumental noise affect biological (in vitro and in vivo) measurements. In this method, we describe how hardware related artifacts and fluorescence background can be corrected without affecting signal of the measurement. First, we applied manual correction for cosmic ray spikes, followed by automated correction to reduce fluorescence and hardware related artifacts based on a partial 5th degree polynomial fitting and Tophat correction. Along with this manuscript we provide a MatLab? script for the automated correction of Raman spectra.•"Polynomial_Tophat_background_subtraction _methods.m" offers an automated method for the removal of hardware related artifacts and fluorescence signals in Raman spectra.•"Polynomial_Tophat_background_subtraction _methods.m" provides a modifiable MatLab file adjustable for multipurpose spectroscopy analysis.•We offer a standardized method for Raman spectra processing suitable for biological and chemical applications for modular confocal Raman spectroscopes.

SUBMITTER: Bertens CJF

PROVIDER: S-EPMC7200319 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Pipeline for the removal of hardware related artifacts and background noise for Raman spectroscopy.

Bertens Christian J F CJF Zhang Shuo S Erckens Roel J RJ van den Biggelaar Frank J H M FJHM Berendschot Tos T J M TTJM Webers Carroll A B CAB Nuijts Rudy M M A RMMA Gijs Marlies M

MethodsX 20200421

Raman spectroscopy is a real-time, non-contact, and non-destructive technique able to obtain information about the composition of materials, chemicals, and mixtures. It uses the energy transfer properties of molecules to detect the composition of matter. Raman spectroscopy is mainly used in the chemical field because background fluorescence and instrumental noise affect biological (<i>in vitro</i> and <i>in vivo</i>) measurements. In this method, we describe how hardware related artifacts and fl ...[more]

PMID: 32382520

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Project description:Background and purposeBlood oxygenation level-dependent MR imaging is increasingly used clinically to noninvasively assess cerebrovascular reactivity and/or language and motor function. However, many patients have metallic implants, which will induce susceptibility artifacts, rendering the functional information uninformative. Here, we calculate and interpret blood oxygenation level-dependent MR imaging artifact impact arising from surgically implanted hardware.Materials and methodsA retrospective analysis of all blood oxygenation level-dependent MRIs (n = 343; B0 = 3T; TE = 35 ms; gradient echo EPI) acquired clinically (year range = 2006-2014) at our hospital was performed. Blood oxygenation level-dependent MRIs were most commonly prescribed for patients with cerebrovascular disease (n = 80) or patients undergoing language or motor localization (n = 263). Artifact volume (cubic centimeters) and its impact on clinical interpretation were determined by a board-certified neuroradiologist.ResultsMean artifact volume associated with intracranial hardware was 4.3 ± 3.2 cm(3) (range = 1.1-9.4 cm(3)). The mean artifact volume from extracranial hardware in patients with cerebrovascular disease was 28.4 ± 14.0 cm(3) (range = 6.1-61.7 cm(3)), and in patients with noncerebrovascular disease undergoing visual or motor functional mapping, it was 39.9 (3)± 27.0 cm(3) (range = 6.9-77.1 cm(3)). The mean artifact volume for ventriculoperitoneal shunts was 95.7 ± 39.3 cm(3) (range = 64.0-139.6 cm(3)). Artifacts had no-to-mild effects on clinical interpretability in all patients with intracranial implants. Extracranial hardware artifacts had no-to-moderate impact on clinical interpretability, with the exception of 1 patient with 12 KLS-Martin maxDrive screws with severe artifacts precluding clinical interpretation. All examined ventriculoperitoneal shunts resulted in moderate-to-severe artifacts, limiting clinical interpretation.ConclusionsBlood oxygenation level-dependent MR imaging yields interpretable functional maps in most patients beyond a small (30-40 cm(3)) artifact surrounding the hardware. Exceptions were ventriculoperitoneal shunts, particularly those with programmable valves and siphon gauges, and large numbers of KLS-Martin maxDrive screws.

Project description:In the real world, human speech recognition nearly always involves listening in background noise. The impact of such noise on speech signals and on intelligibility performance increases with the separation of the listener from the speaker. The present behavioral experiment provides an overview of the effects of such acoustic disturbances on speech perception in conditions approaching ecologically valid contexts. We analysed the intelligibility loss in spoken word lists with increasing listener-to-speaker distance in a typical low-level natural background noise. The noise was combined with the simple spherical amplitude attenuation due to distance, basically changing the signal-to-noise ratio (SNR). Therefore, our study draws attention to some of the most basic environmental constraints that have pervaded spoken communication throughout human history. We evaluated the ability of native French participants to recognize French monosyllabic words (spoken at 65.3 dB(A), reference at 1 meter) at distances between 11 to 33 meters, which corresponded to the SNRs most revealing of the progressive effect of the selected natural noise (-8.8 dB to -18.4 dB). Our results showed that in such conditions, identity of vowels is mostly preserved, with the striking peculiarity of the absence of confusion in vowels. The results also confirmed the functional role of consonants during lexical identification. The extensive analysis of recognition scores, confusion patterns and associated acoustic cues revealed that sonorant, sibilant and burst properties were the most important parameters influencing phoneme recognition. . Altogether these analyses allowed us to extract a resistance scale from consonant recognition scores. We also identified specific perceptual consonant confusion groups depending of the place in the words (onset vs. coda). Finally our data suggested that listeners may access some acoustic cues of the CV transition, opening interesting perspectives for future studies.

Dataset Information

Pipeline for the removal of hardware related artifacts and background noise for Raman spectroscopy.

Publications

Pipeline for the removal of hardware related artifacts and background noise for Raman spectroscopy.

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