Tissue elasticity is an important biomarker for early prediction of diseases, and elastography methods have been developed for several modalities. In practice, photoacoustic imaging (PAI) provides information about the optical properties of deep-seated tissue based on an ultrasonic readout. In this study, we demonstrate the use of PAI to measure tissue elasticity through high frequency acquisitions. The method was tested using tissue mimicking agar phantoms and transducers of different frequencies. The results show that elastic contrast can be recovered from PA signals only using high-frequency transducers, given significant differences of elasticity between the imaging target and background.
Photoacoutic imaging of biological tissues is characterized by depth dependent optical fluence loss and acoustic variations. Here, we aim to correct for these inaccuracies aided by extrinsic imaging priors obtained through concurrent high-frequency ultrasound (US) imaging of tissue samples. We segmented the skin line and characterized tissue components using deformable model-based segmentation from the ultrasound images. The prior information from co-registered US images and tissue temperature was used to accurately model light fluence and speed of sound respectively. Methods applied here show significant improvement in beamforming performance, enhanced visual image quality and a higher PSNR.
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