# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 3 Results
### Brain Tumor Dataset
#### 3.3.1 The Experimental Results and Analysis of the Removal of Artifacts  
In the experiment, the traditional Gaussian filter and deep learning models (including CycleGAN, YOLOv8, and Pix2 Pix and their combinations) were compared to remove artifacts in photoacoustic images. PSNR and SSIM are used as quantitative indicators of image reconstruction quality. Figure 4 demonstrates the effectiveness of ground truth images with various methods for removing artifacts in photoacoustic images. In the comparison experiments, the effectiveness of different methods in removing artifacts in photoacoustic images was observed and evaluated in detail. Through the comparison of the image effect graphs, it is found that the various methods present different characteristics in dealing with artifacts. It is noticed that the photoacoustic images directly reconstructed by the traditional TR method suffer from obvious blurring and information loss, and the artifacts are significantly present at the internal and external boundaries of the images. In addition, the artifact-removal effect of Gaussian filter is worse, which indicates that it is not suitable to be applied for photoacoustic image de-artifacting. The CycleGAN algorithm performs well in removing internal artifacts with improved clarity of the internal structure, but there are still challenges in removing artifacts at the external and brain boundaries. The YOLOv8 algorithm performs better in dealing with the external artifacts; however, the internal image is still subject to blurring and lack of information, and the artifacts are more noticeable. The Pix2 Pix algorithm improves the internal information with the Pix2 Pix algorithm, which has achieved some success, but the handling of external artifacts still needs to be improved. The method of combining the YOLOv8 algorithm with CycleGAN can more effectively remove internal and external artifacts, but there are limitations in artifact removal at the brain boundary. However, the method combining the YOLOv8 algorithm with Pix2 Pix shows much better results, by not only effectively removing internal and external artifacts, but also better preserving the detailed structure and texture of the image. In addition to the visual observation of the image effect, the evaluation metrics, including PSNR and SSIM, indicate that the artifact-removal performance of the YOLOv8-Pix2 Pix method is superior to other methods, achieving the highest values. Comprehensive experimental results show that the method combining the YOLOv8 algorithm and Pix2 Pix exhibits significant advantages in both the artifact-removal effect and image quality evaluation, providing a feasible deep learning solution for photoacoustic image processing. Table 3 shows the average numerical results of the reconstruction of each method on the test set. The TR method is the most primitive direct reconstruction method with a PSNR of 28.918 dB and an SSIM of 0.566. The Gaussian filter performs poorly, which may be due to the fact that its simple filtering method fails to remove the artifacts efficiently. The YOLOv8 algorithm may be more concerned with the removal of artifacts by focusing more on the noise elimination, but neglected the preservation of image details and structures, resulting in the lowest SSIM. Among the comparison algorithms, YOLOv8-Pix2 Pix shows  
the best effect with a PSNR of 31.459 dB and an SSIM of 0.650, which indicates that this method can significantly improve the reconstruction quality while maintaining the image details, and proves that it is an effective photoacoustic image artifact-removal method. Figure 5 shows the results of artifact removal for Pix2 Pix and YOLOv8-Pix2 Pix at different detectors. As expected, reducing the number of detectors used to sample the sound pressure leads to more severe artifacts as well as lower average PSNR and SSIM. Compared to Pix2 Pix, YOLOv8-Pix2 Pix has a higher average PSNR and SSIM at all sample sparsity levels tested.