Photoacoustic imaging (PAI) uniquely combines optical contrast with the penetration depth of ultrasound, making it critical for clinical applications. However, the quality of 3D PAI is often degraded due to reconstruction artifacts caused by the sparse and angle-limited configuration of detector arrays. Existing iterative or deep learning-based methods are either time-consuming or require large training datasets, significantly limiting their practical application. Here, we propose Zero-Shot Artifact2Artifact (ZS-A2A), a zero-shot self-supervised artifact removal method based on a super-lightweight network, which leverages the fact that reconstruction artifacts are sensitive to irregularities caused by data loss. By introducing random perturbations to the acquired PA data, it spontaneously generates subset data, which in turn stimulates the network to learn the artifact patterns in the reconstruction results, thus enabling zero-shot artifact removal. This approach requires neither training data nor prior knowledge of the artifacts, and is capable of artifact removal for 3D PAI. For maximum amplitude projection (MAP) images or slice images in 3D PAI acquired with arbitrarily sparse or angle-limited detector arrays, ZS-A2A employs a self-incentive strategy to complete artifact removal and improves the Contrast-to-Noise Ratio (CNR). We validated ZS-A2A in both simulation study and $ in\ vivo $ animal experiments. Results demonstrate that ZS-A2A achieves state-of-the-art (SOTA) performance compared to existing zero-shot methods, and for the $ in\ vivo $ rat liver, ZS-A2A improves CNR from 17.48 to 43.46 in just 8 seconds. The project for ZS-A2A will be available in the following GitHub repository: https://github.com/JaegerCQ/ZS-A2A.

翻译：光声成像（PAI）独特地将光学对比度与超声波的穿透深度相结合，使其在临床应用中至关重要。然而，三维PAI的质量常因探测器阵列的稀疏和角度受限配置导致的重建伪影而下降。现有的基于迭代或深度学习的方法要么耗时，要么需要大量训练数据集，这极大地限制了其实际应用。本文提出零样本伪影到伪影（ZS-A2A），一种基于超轻量级网络的零样本自监督伪影去除方法，该方法利用了重建伪影对数据丢失引起的不规则性敏感这一事实。通过对采集的PA数据引入随机扰动，它自发生成子集数据，进而激励网络学习重建结果中的伪影模式，从而实现零样本伪影去除。该方法既不需要训练数据，也不需要关于伪影的先验知识，并且能够用于三维PAI的伪影去除。对于使用任意稀疏或角度受限探测器阵列获取的三维PAI的最大振幅投影（MAP）图像或切片图像，ZS-A2A采用自激励策略完成伪影去除并提高对比噪声比（CNR）。我们在仿真研究和$ in\ vivo $动物实验中验证了ZS-A2A。结果表明，与现有的零样本方法相比，ZS-A2A实现了最先进的性能，并且对于$ in\ vivo $大鼠肝脏，ZS-A2A在仅8秒内将CNR从17.48提高到43.46。ZS-A2A的项目将在以下GitHub仓库中提供：https://github.com/JaegerCQ/ZS-A2A。