We set up a photoacoustic microscopy (PAM) system to validate the effectiveness of our proposed de-noising algorithm. The schematic is shown in FIGURE 6. In this experimental setup, we focus the laser into a fiber by an objective lens (OL), and then use two cascaded lens to focus the output light of the fiber onto the sample. The received PA signal from ultrasound transducer (UT) is first fed into a low-noise amplifier (AMP) to be amplified with low noise induction, then transferred to data acquisition card for digitization, which is connected to a computer for real-time storage and display. The X/Y two-dimensional step motor is used for raster scanning. The data sampling rate is set to 80 MHz, and the transducer’s central frequency is 10 MHz.
FIGURE 6 The schematic of experimental system. FG: function generator; OL: objective lens; MMF: multimode fiber; CL: collimating lens; US: ultrasound; WT: water tank; AMP: amplifier; DAQ: data acquisition card; X/Y Motor: two-dimensional step motor.
5.2 | Ex-vivo experimental results
We conduct imaging ofex-vivo colorectal tissues first. We tried sqtwolog threshold method, 4-order Butterworth low pass filter, and our proposed gaWD method, respectively. We first draw the maximum amplitude projection (MAP) image after applying each de-noising method, shown in FIGURE 7 . As shown inFIGURE 7 (a), the MAP image reconstructed from raw data is severely corrupted by both random noise and stripe noise. It could be found that sqtwolog threshold method filters out some random noise, but is unable to reduce the stripe noise, shown in FIGURE 7 (b). As for 4-order Butterworth low pass filtering result in FIGURE 7 (c), it removes the stripe noise at the background, and enhances the image contrast. However, it blurs the image and loses some details, e.g., some texture of tissues is disappeared. Compared to these two methods, our proposed method achieves both de-noising and enhancing the image contrast, meanwhile preserving the details, presented in FIGURE 7 (d). The corresponding SNR of all these images are calculated and shown in FIGURE 7 (e). The de-noised image of our proposed method has the highest SNR, which validates the effectiveness of the proposed algorithm.