Ms. Liudmila Serebrennikova Profile

Liudmila Serebrennikova

at Worcester Polytechnic Institute

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Proceedings Article | 9 March 2023 Presentation + Paper

Feasibility of spectra-based quantification algorithm for non-linear photoacoustic contrast agents

Liudmila Serebrennikova, Shang Gao, Ryo Murakami, Srikanth Boinapally, Martin Pomper, Sangeeta Ray, Haichong Zhang

Proceedings Volume 12379, 1237909 (2023) https://doi.org/10.1117/12.2650607

KEYWORDS: Error analysis, Spectroscopy, Contrast agents, Photoacoustic spectroscopy, Tumors, Tissues, Photoacoustic imaging, Image enhancement

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Photoacoustic (PA) imaging is a technique that visualizes the optical absorption characteristics with ultrasound-like spatial resolution. It has been demonstrated that molecular targeted contrast agents (CAs) have great potential in applying to the early detection of tumor. These CAs attach to the tissues of the subject of interest, i.e., the cancerous cells, and emit PA signals based on the absorbed light. Precise quantification of the CA expression is desired to identify the degree of the malignancies. The current quantification of the CA expression is based on the strength of the PA intensity with the assumption of its linearity between them. However, the estimation accuracy of such a method is limited because the PA intensity is affected by many factors including the light, sound, and tissue interactions and the use of CAs that do not present linearity. Here, we investigate a robust quantification method by using the spectroscopic relationship between the dye concentration and its corresponding PA variation. We introduce a spectroscopic decomposition algorithm considering multiple reference spectra to accommodate the non-linear behavior of sample concentrations. The in vitro validation of the concept was performed using the synthesized PA CA possessing the non-linear property. The concentration of the samples was successfully estimated with the algorithm. The introduced method improved the quantification accuracy by reducing the averaging estimation error from 15.89 𝜇M to 1.80 𝜇M, compared with the conventional estimation.

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