零样本 Artifact2Artifact：无需任何数据的光声成像自激励伪影去除方法
Zero-Shot Artifact2Artifact: Self-incentive artifact removal for photoacoustic imaging without any data
AI总结：
本文提出零样本自监督方法 ZS-A2A，通过向光声数据引入随机扰动生成子集以学习伪影模式，无需训练数据即可去除 3D PAI 重建伪影，并在实验中显著提升 CNR。
摘要：
光声成像（PAI）独特地结合了光学对比度与超声的穿透深度，使其在临床应用中至关重要。然而，由于探测器阵列的稀疏和角度受限配置所导致的重建伪影，3D PAI 的质量往往下降。现有的迭代方法或基于深度学习的方法要么耗时，要么需要大规模训练数据集，显著限制了它们的实际应用。在此，我们提出 Zero-Shot Artifact2Artifact（ZS-A2A），一种基于超轻量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由数据丢失引起的不规则性敏感这一事实。通过向采集到的 PA 数据引入随机扰动，它自发地生成子集数据，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，并且能够对 3D PAI 进行伪影去除。对于以任意稀疏或角度受限探测器阵列采集的 3D PAI 中的最大幅度投影（MAP）图像或切片图像，ZS-A2A 采用自激励策略完成伪影去除，并提高对比度噪声比（CNR）。我们在仿真研究和体内动物实验中验证了 ZS-A2A。结果表明，与现有零样本方法相比，ZS-A2A 达到了最先进（SOTA）的性能，并且对于体内大鼠肝脏，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。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
