Abstract
Gold nanorods (GNRs) showed to be a suitable contrast agent in photoacoustics (PA), and are able to provide a tunable absorption contrast against background tissue, while a detectable PA signal can be generated from highly localized and targeted areas. A crucial issue for these imaging techniques is represented by the discrimination between exogenous and endogenous contrast and the assessment of the real PA signal magnitude. The application of image resolution/unmixing methods was implemented and optimized to recover the relative magnitude spectra and distribution maps of image constituents of the biological sample based on multivariate analysis (multivariate curve resolution—alternating least squares, MCR-ALS) in the presence of GNRs with tunable absorption properties. The proposed data analysis methodology is demonstrated on real PA images from experimental animal models and ex-vivo preparations.
Highlights
Nowadays, hyperspectral photoacoustic (PA) imaging of endogenous contrasts in biological systems shows good potential as it is exploitable for the study of tumor angiogenesis and melanoma [1,2]
A relevant number of contrast agents for photoacoustic imaging (PAI) has been developed [3,4,5,6,7] and amongst them, plasmonic nanoparticles have gained considerable interest over the past few decades due to their surface plasmon resonances, relative biological stability/biocompatibility, and easy functionalization. [8,9] gold nanorods (GNRs) represent an ideal contrast agent for PA since they can provide an enhanced optical absorption contrast against background tissue [10,11,12]. Their tunable longitudinal surface plasmon resonance (LSPR) properties allow for the preparation of tissue-specific theranostic platforms exploiting the contrast they provide in photoacoustic imaging and their photothermal properties, which can be exploited for laser ablation-based therapeutic techniques [13,14,15]
The multivariate analysis was permitted to compress 327,240 spectra into two spectra and two images that are still able to represent more than 95% of the total variance contained in the initial data set (Supplementary Video S6)
Summary
Hyperspectral photoacoustic (PA) imaging of endogenous contrasts in biological systems shows good potential as it is exploitable for the study of tumor angiogenesis and melanoma [1,2]. In the case of biological samples, contributions of many different molecules (i.e., oxy- or deoxy-hemoglobin), the variability of the biological components in vivo, and the limited PA signal amplitude are often very critical to the interpretation of the PA spectra and the multivariate resolution image. To resolve this issue, the use of image resolution/unmixing methods can aid in recovering the single component of the spectra and defining the image constituents of the Nanomaterials 2021, 11, 142.
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