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In this work we demonstrate a photoacoustic system which can identify the deformation of blood vessels under external pressure. Using photoacoustic imaging method, the vessel internal diameter can be derived from the peak-to-peak time interval of the laser (532nm) induced the photoacoustic signals. Comparisons with the actual vessel inter diameter show that the relative deviation is less than 4%, which proves the validity of this method. Interestingly, we find that the axial diameter of the blood vessel and the blood volume increase monotonously with increasing transient pressure, and the laser-induced photoacoustic signal is mainly contributed by the blood inside the vessel. Our results suggest a new way for continuous monitoring of the deformation of blood vessels under pressure. [ABSTRACT FROM AUTHOR]

In endometrial cancer, patients in early stages have a 91% 5-year survival chance. By contrast, patients in advanced stages have only 20% survival chance. Therefore, early diagnosis of endometrial cancer is very important. Photoacoustic imaging is able to distinguish benign from malignant tumors. However, the images acquired through photoacoustic imaging contain inherent artifacts, caused by imperfect reconstruction algorithms. In this paper, to improve the said images, a filtered delay-multiply-and-sum (F-DMAS) algorithm, which was proven to have an increased dynamic range and better quality of B-mode images was employed in the reconstruction process. First, the images of two blood vessel phantom experiments, acquired through photoacoustic imaging, were reconstructed by employing the F-DMAS algorithm. The results show the lateral resolutions of the system improving from 2.22 mm (with traditional photoacoustic imaging reconstruction algorithms) to 1.47 mm. Next, images of a pig uterus, filled with intralipid-20% emulsion, were also reconstructed by employing the F-DMAS algorithm. The average signal-to-noise ratio increased from 11.14 dB (with traditional photoacoustic imaging reconstruction algorithms) to 64.90 dB. In order to improve the continuity of the 3D PA image, F-DMAS with an adaptive coefficient was discussed to find the best balance between the signal-to-noise ratio and continuity. In conclusion, this paper demonstrates that the use of F-DMAS algorithms in the reconstruction of the images acquired through photoacoustic imaging in clinical investigations would improve the detection rate of early endometrial cancer. [ABSTRACT FROM AUTHOR]

Based on deep learning, a Dust Ablation Trail Analysis (DATA) code package is developed to detect dust ablation trails in tokamaks, which is intended to analyze a large amount data of tokamak dusts. To validate and benchmark the DATA code package, 2440 plasma jet images are exploited for the training and test of the deep learning DATA code package, since plasma jets resemble the shape and size of dust ablation clouds in tokamaks. After being trained by 1920 plasma jet images, the DATA code package is able to locate 100% plasma jets, classify plasma jets with the accuracy of >99.9%, and output image skeleton information for classified plasma jets. The DATA code package trained by the plasma jet images is also used to analyze the dust ablation trails captured in the Experimental Advanced Superconducting (EAST) tokamak with the satisfactory performance, further verifying its applicability in the fusion dust ablation investigation. Based on its excellent performance presented here, it is demonstrated that our DATA code package is able to automatically identify and analyze dust ablation trails in tokamaks, which can be used for further detailed investigations, such as the three-dimensional reconstruction of dusts and their ablation trails. [ABSTRACT FROM AUTHOR]