# Deep Learning Realizes Photoacoustic Imaging Artifact Removal
## 2 Materials and Methods
### Deep Learning Algorithm for Photoacoustic Image Artifact Removal  
Deep learning is widely used in removing artifacts, but existing algorithms can only remove bar and ring artifacts in photoacoustic images and cannot effectively deal with various complex artifacts. The YOLOv8-Pix2 Pix model proposed in this paper (the model framework is shown in Figure 3) addresses the characteristics of photoacoustic images and can comprehensively handle different types of artifacts while effectively recovering image details. The model first utilizes the YOLOv8 algorithm (Figure 3a) to localize the photoacoustic image and accurately segment the photoacoustic source to remove artifacts outside the photoacoustic source. Then, the Pix2 Pix algorithm (Figure 3b) is employed to process the artifacts in the photoacoustic image caused by radiation artifacts and the mixing of the photoacoustic source, enabling comprehensive artifact removal in the photoacoustic image. Figure 2: YOLOv8 architecture. Pix2 Pix [35; 36] is a Conditional Generative Adversarial Network (cGAN)-based image-to-image translation model designed for image transformation tasks that require explicit one-to-one correspondence, and can effectively remove aliasing artifacts within optical sound sources. The model consists of two core components, the generator (\(G\)) and the discriminator (\(D\)). The generator's task is to convert the input image into an output image, which is continuously optimized by means of adversarial training in order to deceive the discriminator and generate a sufficiently realistic image. The discriminator is responsible for distinguishing between the generated image and the real image, thus prompting the generator to produce a more realistic image. Pix2 Pix GAN's objective function VPix2 Pix GAN is defined as  
\[argmin_{G}max_{D}V_{pix2 pixGAN}(G,D)=E_{x\sim P_{low}(x),y\sim P_{ pixall(y)}}[logD(x,y)]+E_{x\sim P_{low(x)}}[\log(1-D(x,G(x)))] \tag{1}\]  
where \(x\sim Plow(x)\) denotes the brain tumor Magnetic Resonance Imaging (MRI) image and \(y\sim Pfull(y)\) denotes the corresponding brain tumor photoacoustic image. The discriminator loss is defined as the cross-entropy, as shown in Equation (2), which is used to adjust the weights of the discriminators. Compared to the common L2 loss, the L1 loss enforces low-frequency correctness, as shown in Equation (3), and encourages the reduction of image blur. Therefore, the generator is trained by minimizing the difference between the de-artifacted image and the reference image, i.e., the mean absolute error (MAE). \[V_{L1}(G)=E_{x\sim P_{low(x),y\sim P_{full(y)}}}[\left\|y-G(x)\right\|_{1}] \tag{2}\]  
Then, our final objective function can be expressed as  
\[G^{*}=argmin_{G}max_{D}V_{pix2 pixGAN}(G,D)+\lambda V_{L1}(G) \tag{3}\]  
where \(\lambda=100\) is an adjustable parameter to control the balance between \(V_{pix2 pixGAN}(G,D)\) and \(V_{L1}(G)\). YOLOv8 is a target detection algorithm for automatic identification and removal of artifacts in photoacoustic images, with efficient real-time target detection and good adap  
Figure 3: YOLOv8-Pix2 Pix model flow for processing photoacoustic image artifacts. ability to complex scenes. The source of photoacoustic images usually has a unique shape and texture characteristics. YOLOv8 can quickly and accurately analyze the photoacoustic images, effectively locate and identify the source of photoacoustic images, and then remove other contents in the photoacoustic images, so as to improve the quality and clarity of the photoacoustic images. The high efficiency and accuracy of YOLOv8 make it an important tool for photoacoustic image processing, which provides reliable image support for medical diagnosis. reliable image support. The YOLOv8-Pix2 Pix model proposed in this paper is applied to the task of converting brain tumor MRI images to brain tumor photoacoustic images.