发表机构
College of Future Technology, Peking University; Pohang University of Science and Technology（北京大学未来技术学院; 浦项科技大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对光声计算机断层扫描因检测条件稀疏产生的重建伪影问题，提出用轻量级暹罗神经网络及复合损失函数的自监督伪影去除框架，能有效抑制伪影并提升计算效率。
AI 中文摘要
光声计算机断层扫描（PACT）由于检测条件稀疏，在重建伪影方面常面临严峻挑战。基于基于反投影和基于傅里叶的重建算法在伪影模式上的明显差异，提出一种自监督伪影去除框架，采用轻量级暹罗神经网络和集成跨域保真度与不确定性加权一致性的复合损失函数，有效解耦双域特征并过滤伪影。通过模拟、体模、体内大鼠和人体实验数据的综合验证表明，该方法能显著抑制图像伪影，且因空间域和频域逆算子的加速，实现了卓越的计算效率。
英文摘要
Photoacoustic Computed Tomography (PACT) often faces severe challenges from reconstruction artifacts due to sparse detection conditions. In this work, based on the distinct differences in artifact patterns between back-projection-based and Fourier-based reconstruction algorithms, we propose a self-supervised artifact removal framework that employs a lightweight Siamese Neural Network and a composite loss function integrating cross-domain fidelity and uncertainty-weighted consistency, effectively decoupling dual-domain features and filtering artifacts. Comprehensive validations using simulations, phantoms, in vivo rat and human experimental data demonstrate that the proposed method can significantly suppress image artifacts. Furthermore, enabled by the acceleration of the spatial-domain and frequency-domain inverse operator, this end-to-end approach also achieves exceptional computational efficiency.
Comments13 pages, 9 figures
