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A real-time photoacoustic tomography which is capable of imaging the changes in biological features of living subject is presented. A custom developed data acquisition board and linear array transducer is used in this photoacoustic system. A phantom test were carried out to evaluate performance of the system. The developed system showed a satisfactory performance and its usefulness were evaluated. The universal back projection algorithm is used for image reconstruction and the sensitivity is analyzed from the obtained photoacoustic images.

The quantized vibration of chemical bonds provides a way of detecting specific molecules in a complex tissue environment. Unlike pure optical methods, for which imaging depth is limited to a few hundred micrometers by significant optical scattering, photoacoustic detection of vibrational absorption breaks through the optical diffusion limit by taking advantage of diffused photons and weak acoustic scattering. Key features of this method include both high scalability of imaging depth from a few millimeters to a few centimeters and chemical bond selectivity as a novel contrast mechanism for photoacoustic imaging. Its biomedical applications spans detection of white matter loss and regeneration, assessment of breast tumor margins, and diagnosis of vulnerable atherosclerotic plaques. This review provides an overview of the recent advances made in vibration-based photoacoustic imaging and various biomedical applications enabled by this new technology.

The photoacoustic images are obtained from a custom developed linear array photoacoustic tomography system. The biological specimens are imitated by conducting phantom tests in order to retrieve a fully functional photoacoustic image. The acquired image undergoes the active region based contour filtering to remove the noise and accurately segment the object area for further processing. The universal back projection method is used as the image reconstruction algorithm. The active contour filtering is analyzed by evaluating the signal to noise ratio and comparing it with the other filtering methods.

Lanthanide-doped upconversion nanoparticles have received growing attention in the development of low-background, highly sensitive and selective sensors. Here, we report a water probe based on ligand-free NaYF₄:Yb/Er nanoparticles, utilizing their intrinsically nonlinear upconversion process. The water molecule sensing was realized by monitoring the upconversion emission quenching, which is mainly attributed to efficient energy transfer between upconversion nanoparticles and water molecules as well as water-absorption-induced excitation energy attenuation. The nonlinear upconversion process, together with power function relationship between upconversion emission intensity and excitation power density, offers a sensitive detection of water content down to 0.008 vol % (80 ppm) in an organic solvent. As an added benefit, we show that noncontact detection of water can be achieved just by using water attenuation effect. Moreover, these upconversion nanoparticle based recyclable probes should be particularly suitable for real-time and long-term water monitoring, due to their superior chemical and physical stability. These results could provide insights into the design of upconversion nanoparticle based sensors.

Here we developed a real-time photoacoustic tomography (PAT) imaging acquisition device based on the linear array transducer utilized on ultrasonic devices. Also, we produced a phantom including diverse contrast media and acquired PAT imaging as the light source wavelength was changing to see if the contrast media reacted. Indocyanine green showed the highest reaction around the 800-nm band, methylene blue demonstrated the same in the 750-nm band, and gold nanoparticle showed the same in the 700-nm band. However, in the case of superparamagnetic iron oxide, we observed not reaction within the wavelength bands used herein to obtain imaging. Moreover, we applied selective filtering to the acquired PAT imaging to remove noise from around and reinforce the object’s area. Consequentially, we could see the object area in the imaging was effectively detected and the image noise was removed.