用双域自监督方法，高效去除光声成像的重建伪影。
Dual-Domain Self-Supervised Artifact Removal Framework for Photoacoustic Computed Tomography
- 基于反投影与傅里叶算法伪影差异，设计双域解耦网络。
- 在模拟、模型、大鼠及人实验中显著降低伪影，信噪比提升30%以上。
- 轻量级结构+端到端训练，计算效率高，适合实时成像应用。
光声计算机断层成像（PACT）在稀疏检测条件下常面临严重的重建伪影问题。本文基于反投影与傅里叶重建算法间伪影模式的显著差异，提出一种自监督伪影消除框架，采用轻量级孪生神经网络和融合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。通过模拟数据、模型实验、活体大鼠及人体实验的全面验证表明，该方法能显著抑制图像伪影。此外，得益于空间域与频率域逆算子的加速，该端到端方法还实现了卓越的计算效率。
原文摘要 · Abstract (English)
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.
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