Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment

A trajectory-tracked, near-infrared autofluorescence imaging guided, biochemical signature-projected needle-type Raman spectroscopy (TNBN-RS) system integrated on a medical cart was developed for rapid wide-field breast tissue stratification. A wide-field (10 × 10 cm<sup>2</sup>) near-infrared autofluorescence (NIRAF) imaging subsystem was developed for gross stratification of breast tissue types based on higher NIRAF intensity associated with breast cancer, followed by projection of NIRAF-identified breast tumor margins onto the tissue of interest with a compact projector. Raman spectra were further acquired from the NIRAF projected regions for confirmed margin assessment using a needle-type Raman probe equipped with color camera-based probe trajectory tracking. The trajectory of the Raman probe and the accompanying RS biochemical signature-based margin assessment were instantly projected. A unique field of view (FOV) calibration method was proposed to calibrate the TNBN-RS FOVs, resulting in a projection accuracy of <2 mm. A graphical user interface (GUI) was developed in C# for system control, real-time processing and display of NIRAF images, Raman spectra, and projection of their results. The performance of the TNBN-RS system was validated on an <i>ex vivo</i> breast tissue, demonstrating its potential for rapid intraoperative breast tumor margin assessment.

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Publication Details

Journal
Optics Letters
Published
2024-10-24
DOI
https://doi.org/10.1364/ol.539475
Citations
1
Primary Topic
Spectroscopy Techniques in Biomedical and Chemical Research
Type
article
Field-Weighted Citation Impact
0.66

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article

Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment

Jürgen Popp, Shuyu Chen, Lihui Liu, Michael P. Schmitt et al.
1 citations
Optics Letters
Spectroscopy Techniques in Biomedical and Chemical Research
0.66
article

Autofluorescence imaging guided needle-type Raman spectroscopy system for breast tumor margin assessment

Jürgen Popp, Shuyu Chen, Lihui Liu, Michael P. Schmitt, Houpu Yang, Jianfeng Wang, Ruoyu Zhang, Fei Xie, Qian Chen, Shu Wang
article en
1 citations

Abstract

A trajectory-tracked, near-infrared autofluorescence imaging guided, biochemical signature-projected needle-type Raman spectroscopy (TNBN-RS) system integrated on a medical cart was developed for rapid wide-field breast tissue stratification. A wide-field (10 × 10 cm<sup>2</sup>) near-infrared autofluorescence (NIRAF) imaging subsystem was developed for gross stratification of breast tissue types based on higher NIRAF intensity associated with breast cancer, followed by projection of NIRAF-identified breast tumor margins onto the tissue of interest with a compact projector. Raman spectra were further acquired from the NIRAF projected regions for confirmed margin assessment using a needle-type Raman probe equipped with color camera-based probe trajectory tracking. The trajectory of the Raman probe and the accompanying RS biochemical signature-based margin assessment were instantly projected. A unique field of view (FOV) calibration method was proposed to calibrate the TNBN-RS FOVs, resulting in a projection accuracy of <2 mm. A graphical user interface (GUI) was developed in C# for system control, real-time processing and display of NIRAF images, Raman spectra, and projection of their results. The performance of the TNBN-RS system was validated on an <i>ex vivo</i> breast tissue, demonstrating its potential for rapid intraoperative breast tumor margin assessment.

Optics LettersVol. 49(23)
Peking University (CN), Leibniz Institute of Photonic Technology (DE), Peking University People's Hospital (CN), Friedrich Schiller University Jena (DE)
National Natural Science Foundation of China, Beijing Institute of Technology Research Fund Program for Young Scholars, Beijing Municipal Natural Science Foundation
Openalex Percentile: Top 19%
Spectroscopy Techniques in Biomedical and Chemical Research
0.66
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