# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 3 Results
### Photoacoustic Detection Experiment
#### 3.4.2 The Experimental Results and Analysis of the Removal of Artifacts  
In the artifact-removal experiments, the processing results of the original photoacoustic image were compared with the photoacoustic image processed using the YOLOv8-Pix2 Pix model across different numbers of photoacoustic sources. The reconstructed photoacoustic images were compared with their standard maps using PSNR and SSIM as quantitative indicators of image reconstruction quality.  
Figure 9 illustrates the comparison of the original PAI image with the algorithmically processed image and the validation map after fusion with ultrasound imaging from a single photoacoustic source to a multi-photoacoustic source scenario, with the photoacoustic sources labeled PAS1, PAS2, and PAS3 in Figure 9. The artifacts in the original PAI image increase with the number of photoacoustic sources, which is especially significant in Figure 9d. This increase in artifacts is mainly due to the boundary reflection signal (BRS) interactions between multiple photoacoustic sources as well as due to the fact that a single photoacoustic source cannot uniformly illuminate all the photoacoustic sources, which leads to more artifacts and degradation of the imaging quality. The improved algorithm showed significant results in removing these artifacts, as shown in the middle figure in Figure 9d, clearly removing these complex artifacts. By fusing the PAI image with the US image, it is verified that the improved algorithm is able to accurately identify and localize the photoacoustic source even in the presence of multiple BRS reflections and irradiations.  
Figure 8: The schematics of the experiment samples. (**a**) Phantom I. (**b**) Phantom II. (**c**) Phantom III. (**d**) Phantom IV.  
Table 5 demonstrates the evaluation metrics of the original photoacoustic image compared with the photoacoustic image after artifact removal. Clearly, the PSNR and SSIM of the mimic photoacoustic images processed using the YOLOv8-Pix2 Pix model are significantly improved compared with those before artifact removal. Even in the Phantom IV with complex artifacts and low evaluation indexes, the use of this model can still effectively recover the image information and improve the values of PSNR and SSIM.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & & **Phantom I** & **Phantom II** & **Phantom III** & **Phantom IV** \\ \hline \multirow{2}{*}{Artifact Image} & PSNR & 74.089 & 78.531 & 71.162 & 54.164 \\  & SSIM & 0.993 & 0.995 & 0.985 & 0.646 \\ \hline \multirow{2}{*}{Artifact-free Image} & PSNR & 79.988 & 78.933 & 74.514 & 73.616 \\  & SSIM & 0.998 & 0.997 & 0.996 & 0.992 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Evaluation metrics for de-artifacting in photoacoustic images with and without artifacts.  
Figure 9: Experimental imaging results using (**a**) 1, (**b**) 2, (**c**) 3, (**d**) 4 photoacoustic sources.  
According to the experimental results, the time required to process a single image in a photoacoustic detection experiment using YOLOv8-Pix2 Pix is as follows: the preprocessing time is 2.0 ms, inference time is 22.6 ms, loss computation time is 0.0 ms, and post-processing time is 2.2 ms.