Learning physics-constrained inverse operators-rather than post-processing physics-based reconstructions-is a broadly applicable strategy for problems with expensive forward models. We demonstrate this principle in three-dimensional photoacoustic computed tomography (3D PACT), where current systems demand dense transducer arrays and prolonged scans, restricting clinical translation. We introduce PANO (PACT imaging neural operator), an end-to-end physics-aware neural operator-a deep learning architecture that generalizes across input sampling densities without retraining-that directly learns the inverse mapping from raw sensor measurements to a 3D volumetric image. Unlike two-step methods that reconstruct then denoise, PANO performs direct inversion in a single pass, jointly embedding physics and data priors. It employs spherical discrete-continuous convolutions to respect hemispherical sensor geometry and Helmholtz equation constraints to ensure physical consistency. PANO reconstructs high-quality images from both simulated and real data across diverse sparse acquisition settings, achieves real-time inference and outperforms the widely-used UBP algorithm by approximately 33 percentage points in cosine similarity on simulated data and 14 percentage points on real phantom data. These results establish a pathway toward more accessible 3D PACT systems for preclinical research, and motivate future in-vivo validation for clinical translation.

翻译：学习物理约束的反演算子（而非对基于物理的重建结果进行后处理）是一种适用于前向模型计算成本高昂问题的通用策略。我们在三维光声计算机层析成像（3D PACT）中验证了这一原理，当前系统需要密集的换能器阵列和长时间扫描，限制了临床转化。我们提出PANO（PACT成像神经算子）——一种端到端的物理感知神经算子（一种无需重新训练即可泛化至不同输入采样密度的深度学习架构），可直接学习从原始传感器测量值到三维体图像的反演映射。与先重建后去噪的两步法不同，PANO通过单次前向传播实现直接反演，将物理先验与数据先验进行联合嵌入。该方法采用球面离散-连续卷积以遵循半球形传感器几何结构，并利用亥姆霍兹方程约束确保物理一致性。PANO在多种稀疏采集设置下，从仿真和真实数据中均能重建高质量图像，实现实时推理，在仿真数据上的余弦相似度比广泛使用的UBP算法提升约33个百分点，在真实体模数据上提升14个百分点。这些结果为临床前研究开发更易实现的三维PACT系统开辟了路径，并为未来开展临床转化所需的体内验证提供了研究动机。