Background: Superconducting strip photon detector (SSPD) have been applied to monitor singlet oxygen ( 1O2) luminescence produced during photodynamic therapy (PDT) due to its high detection efficiency, low dark count and fast detection speed. Objective: This study calibrate of the detection efficiency and dark counts of a new SSPD system for singlet oxygen luminescence detection. Materials and Method: The input photons that passing through the attenuator, polarization controller and optical fiber were carefully controlled so that the average photon arrival-time interval was much larger than the response time of SSPD detector. The electrical signals generated from SSPD detector were collected after passing through a low-noise amplifier and analyzed using data acquisition card. Bias current started at 10 μA and increased by 1 μA step until saturation. The electrical signal pulses were recorded. The detection efficiency was determined by the ratio of the electrical signal pulse count minus the dark count to the number of photons input to the detector. Dark counts were measured by adjusting the bias current with the light input channel completely closed and in a dark environment. The experimental temperature was controlled at 2.2 K. Result: The best detection efficiency was achieved when the bias current was 23 μA with the input photon power of -107.6 dBm. The detection efficiency was 88% and 90% for two tested detectors with the optimized polarization. The dark count rate was less than 100 cps. Conclusions: This study demonstrated that the optimization of attenuator, polarization controller and bias current can improve the detection efficiency of SSPD system for singlet oxygen detection.
Dermoscopy is a useful tool for observing the vascular profile of port-wine stain (PWS) birthmarks. However, due to the complicity of the vascular profile, there is a lack of consensus on the classification of dermoscopic features of PWS vessels. This study investigated the potentials of deep learning-assisted methods in the classification of dermoscopy image-based of PWS vascular profiles. The classified images were used as training samples, and the RegNet network with better classification effect was selected to establish the migration learning method. The results showed that the accuracy of the RegNet network on the validation set was 82.63%. The preliminary study suggests that deep learning assisted PWS vascular contour type classification is feasible.
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