# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 1 Introduction  
Photoacoustic imaging (PAI) has shown great potential in providing detailed views of human anatomy [1; 2; 3; 4; 5]. Photoacoustic imaging technology enables high-resolution imaging of human tissues by delivering pulsed laser beams. In this technique, light signals that are preferentially absorbed by the tissue cause the generation of acoustic waves, which are subsequently detected and imaged by a conventional ultrasound (US) transducer. Photoacoustic imaging is widely used for the detection and treatment of cancer, vascular monitoring, medication delivery, surgical navigation, and metal implant detection [6; 7; 8; 9; 10]. However, radioactivity artifacts are often present in photoacoustic images, which can pose diagnostic and therapeutic challenges for clinicians [11; 12; 13]. Radioactivity artifacts are primarily caused by strong reflection of acoustic waves, which is often not adequately accounted for by conventional beam imaging methods. Reflections [14; 15; 16] lead to signal localization errors and thus affect image accuracy. At the same time, inconsistencies in the acoustic environment, such as variations in sound speed, density, or attenuation, make it difficult to accurately model acoustic wave propagation. Although photoacoustic imaging technology has a promising future for clinical applications, the presence of artifacts limits its further development [17; 18; 19].  
There are various methods to remove artifacts from photoacoustic images in current studies. Traditional methods such as singular value decomposition [20] and Gaussian filters [21] can only remove some artifacts caused by strong acoustic reflection [22]. Photoacoustically guided focused ultrasound (PAFUSion) technology uses an innovative method to eliminate reflection artifacts by simulating the photoacoustic(PA) field, but it needs to meet the requirements of ultrasound and photoacoustic image matching, which may reduce the frame rate and bring errors in the case of tissue movement [23]. Alternatively,some studies have attempted to utilize neural networks [24; 25; 26] to estimate beamforming delay functions to reduce artifacts caused by sound velocity errors. Other studies have explored Wasserstein generative adversarial networks (WGAN-GP) to reduce finite-view and finite-bandwidth artifacts [27; 28], but these approaches have not yet adequately considered the effects of light excitation and propagation on imaging quality. There are also studies using Pixel-DL [29] technology to improve PAT image reconstruction quality and reduce computational costs, but this method is not suitable for heterogeneous media (such as in vivo imaging).  
In this paper, a new photoacoustic image processing model is proposed to cope with different types of artifacts in photoacoustic images by using the YOLOv8-pix2 pix algorithm. The YOLOv8 algorithm handles the precise segmentation of brain tumors [30; 31] and the removal of artifacts near the signal source. Meanwhile, the Pix2 Pix conversion approach primarily addresses artifacts caused by signal aliasing. After the modeling process, not only are the artifacts effectively removed, but also the quality of the images is significantly improved. Meanwhile, the application of the YOLOv8 algorithm in image segmentation provides reliable image data support for the detection and treatment of brain tumors.