Abstract

This dissertation presents the first photoacoustic study of single cells using ultra-high frequencies (UHF, over 100 MHz). At these frequencies, unique features occur in the photoacoustic signal spectrum which depend on the cell size, morphology and structure. A finite element model (FEM) was developed to simulate the photoacoustic signals from ideal spherical droplets containing a perfluorocarbon liquid and optically absorbing nanoparticles. The model was applied to droplets in suspension and on a boundary to examine how the photoacoustic spectrum varies with droplet size and configuration, and compared to measurements using a 375 MHz transducer. Good agreement in the spectral features between the measured values and the FEM and analytical solution were observed. For the droplet on a boundary, additional spectral features were observed there were correctly predicted by the FEM, but not the analytical solution. The FEM could be applied to situations where the analytical model cannot be used, such as the asymmetric shape of red blood cells (RBCs). Measurements of single RBCs were then compared to the FEM. The frequency location of the spectral minima shifted to higher frequencies as the RBC rotated from a vertical to horizontal orientation. The spectral minima shifted to lower frequencies as the RBC swelled from the normal biconcave shape to a spherical morphology. Healthy RBCs were differentiated from spherocytes, echinocytes and swollen RBCs using changes in the photoacoustic spectrum (p<0.001). These results suggest that the photoacoustic spectrum can be used to classify RBCs according to their shape and pathology. Classification of cells using the photoacoustic spectral features was applied to measurements of blood cells and circulating tumor cells (CTCs) such as melanoma and acute myeloid leukemia (AML) cells. Measurements of 89 cells showed that variations in the spectrum and signal amplitude could be used to identify and differentiate melanoma and AML cells from RBCs, thus identifying foreign cells in the bloodstream. This dissertation investigates how UHF photoacoustics can be used to identify and classify cells and particles in a sample using their photoacoustic spectra, with the end goal of using these methods to identify cell pathology and detect CTCs clinically.

Highlights

  • Cancer can directly and indirectly affect the cells in blood. Hematological cancers such as leukemia, lymphoma and myeloma account for 9% of all cancers and originate in or near the blood [1], while primary tumors originating elsewhere in the body can release metastatic cells called circulating tumor cells (CTCs) that travel through the blood stream

  • The finite element model (FEM) solution was compared to the analytical solution using identical parameters

  • The FEM could be applied to various scenarios where the analytical solution cannot be used, such as the generation of photoacoustic waves from multiple sources, heterogeneous absorbance and from asymmetric shapes such as the bi-concave shape of red blood cells where the photoacoustic spectral features are known to depend on RBC morphology [174]

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Summary

Introduction

In 2012, an estimated 1.64 million new cases of cancer occurred in the United States, with 577,000 deaths [1]. The main absorbing structure in tissue is the blood and the main chromophore in the blood is haemoglobin This endogenous source of contrast has been exploited in-vivo for the detection of vascular tumours [138] photoacoustic tomography [65] with functional imaging [139], and imaging the vasculature [114, 140]. When irradiated with sufficient optical energy, the NPloaded micron-sized droplets emit photoacoustic signals These contrast agents have a unique signature signal compared to other photoacoustic sources in tissue, and can be used to differentiate and quantify the emulsions present in a region. PFC emulsions have been developed as photoacoustic agents through the incorporation of optically absorbing nanoparticles (NPs) [144, 145, 148, 185] or dyes [186] into the droplet. There is limited information about the relation between numbers and heterogeneity of CTCs and types of cancers, and the lack of a gold standard for quantifying CTCs remains an issue [10]

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