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Photoacoustic tomography (PAT) combines optical contrast with ultrasonic penetration, yet hardware limitations and workflow constraints in practical systems inevitably introduce multiple degradation pathways including channel under-sampling, angular under-sampling, bandwidth limitations, and additive noise that severely deteriorate reconstructed image quality. Existing learning-based solutions typically specialize in a single degradation and therefore demand multiple models in practice. We present PAT-Mamba, a novel all-in-one blind restoration framework that simultaneously addresses multiple prevalent degradation types without prior knowledge of the acquisition conditions. PAT-Mamba incorporates two complementary innovations: TransMamba blocks that replace self-attention with selective state space models to capture long-range dependencies at linear complexity, enabling efficient global context propagation for artifact suppression and structural detail recovery, and Adaptive Frequency Learning Blocks (AFLB) that analyze degradation-specific frequency cues, generate learnable frequency masks, and refine frequency subbands through cross-attention and dual-branch gating. Experiments on three representative datasets demonstrate that PAT-Mamba achieves competitive or improved performance compared to state-of-the-art baselines in the majority of degradation scenarios, attaining an average PSNR of 37.04 dB on the Embryo simulation dataset (+0.91 dB over the runner-up), 30.41 dB on the in vivo Mice dataset (+0.53 dB), and 30.50 dB on the clinical Artery dataset. These results demonstrate that PAT-Mamba eliminates the need to deploy multiple specialized networks for different degradation types, while its blind restoration capability removes the requirement for prior degradation diagnosis in clinical workflows, providing a practical and efficient restoration solution for real-world PAT systems. Code is available at https://github.com/FDUlixiaoyao/PAT-Mamba.
Chen et al. (Tue,) studied this question.