Locating Three-Dimensional Position of Deep-Seated SERS Phantom Lesions in Thick Tissues Using Tomographic Transmission Raman Spectroscopy

ACS Appl Mater Interfaces. 2023 Sep 27;15(38):44665-44675. doi: 10.1021/acsami.3c07792. Epub 2023 Sep 13.

Abstract

Locating distinct objects within a thick scattering medium remains a long-standing challenge in the fields of materials science, health, and engineering. Transmission Raman spectroscopy (TRS) with the use of surface-enhanced Raman scattering (SERS) nanoparticles has proven to be an effective approach to detect deep-seated lesions inside thick biological tissues. However, it has not yet been proven to spatially locate deep lesions in three dimensions using optical modalities. Herein, we present the concept of tomographic TRS and report its successful use for accurately locating SERS nanoparticles in elongated rod-like thick tissues. Our work starts with theoretical simulations of Raman photon propagation in tissues. We discovered a linear relationship between the Raman spectral peak ratio and propagation distance of Raman photons in tissues, allowing us to predict the location of lesions tagged by SERS NPs. Based on this, we propose a two-step tomographic TRS strategy, which includes axial scanning and ring scanning. We demonstrate the robustness of our approach using ex vivo thick tissue (4.5 cm in thickness) and locate an embedded SERS phantom lesion, with a ring scanning step of 10-30°. We successfully locate multiple SERS phantom lesions in the ex vivo porcine muscle stack with high accuracy (absolute error of <2 mm). Our method is rapid, efficient, and of low cost compared to current tomographic medical imaging techniques. This work advances Raman techniques for three-dimensional positioning and offers new insights toward practical diagnosis applications.

Keywords: depth prediction; quantitative depth; spatially offset Raman spectroscopy; surface-enhanced Raman scattering; tomography.

MeSH terms

  • Animals
  • Engineering
  • Muscles
  • Nanoparticles*
  • Phantoms, Imaging
  • Spectrum Analysis, Raman*
  • Swine