Photoacoustic phase and intensity dual-modality imaging for characterization of plaque viscoelasticity and lipid composition: a feasibility study

Rupture of atherosclerotic plaque is the most important cause of sudden death in acute myocardial infarction. Plaque stability is closely related to mechanical characters and histological composition. A dual-modality technique, combining photoacoustic phase imaging (PPI) and intensity imaging (PAI), was developed providing viscoelasticity and lipid component of plaques simultaneously.

We aimed to investigate the feasibility of PPI to identify thin-cap fibrous plaques by detecting viscosity-elasticity ratio (VER), to further examine the correlation between VER and the major components of plaque, and ultimately to verify the accuracy of PPI-PAI in assessing plaque types.

PPI-PAI experiments were performed on the aortas of 18 Apolipoprotein E-knockout (ApoE) mice fed with high-fat/high-cholesterol diet. After each experiment, the plaques at the target sites were stained with Oil Red O, Masson, and F4/80 staining to verify the accuracy of the imaging and to further investigate the correlations between VER and lipid core area, fibrous cap thickness, and macrophage content. Finally, conventional statistical methods, such as linear analysis, were used to summarize the experimental data and draw general conclusions.

PPI enables quantitative VER detection of plaques, with thin-cap fibrous plaques exhibiting the highest VER ( $3.77\times {10}^{-5}\sim 4.68\times {10}^{-5}s$ ). There is a high positive correlation between VER and lipid cross-section area ( $R^2=0.81$ , $p<0.0001$ ) and macrophage content ( $R^2=0.75$ , $p<0.0001$ ) in lipid plaque lesions. Likewise, in fibrous plaques, a strong negative correlation ( $R^2=0.71$ , $p<0.0001$ ) is demonstrated between VER and fibrous cap thickness; and a high positive correlation ( $R^2=0.12$ , $p=0.02$ ) between VER and macrophage content. As lipid plaques and thick-cap fibrous plaques may have the same VER values, the combination of PPI and PAI can effectively identify plaque types.

PPI facilitates the characterization of thin-cap fibrous plaques by VER measurements, and further combination with PAI can improve the detection rate of distinct plaque types.