As a non-invasive hybrid biomedical imaging technology, photoacoustic tomography combines high contrast of optical imaging and high penetration of acoustic imaging. However, the conventional standard reconstruction under sparse view could result in low-quality image in photoacoustic tomography. Here, a novel model-based sparse reconstruction method for photoacoustic tomography via diffusion model was proposed. A score-based diffusion model is designed for learning the prior information of the data distribution. The learned prior information is utilized as a constraint for the data consistency term of an optimization problem based on the least-square method in the model-based iterative reconstruction, aiming to achieve the optimal solution. Blood vessels simulation data and the animal in vivo experimental data were used to evaluate the performance of the proposed method. The results demonstrate that the proposed method achieves higher-quality sparse reconstruction compared with conventional reconstruction methods and U-Net. In particular, under the extreme sparse projection (e.g., 32 projections), the proposed method achieves an improvement of ∼ 260 % in structural similarity and ∼ 30 % in peak signal-to-noise ratio for in vivo data, compared with the conventional delay-and-sum method. This method has the potential to reduce the acquisition time and cost of photoacoustic tomography, which will further expand the application range.

中文翻译：

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结合扩散模型与基于模型的迭代的光声断层扫描稀疏视图重建

光声断层扫描作为一种非侵入性混合生物医学成像技术，结合了光学成像的高对比度和声学成像的高穿透性。然而，稀疏视图下的传统标准重建可能会导致光声断层扫描图像质量低下。这里，提出了一种基于扩散模型的基于模型的光声层析成像稀疏重建方法。基于分数的扩散模型旨在学习数据分布的先验信息。在基于模型的迭代重建中，利用学习到的先验信息作为基于最小二乘法的优化问题的数据一致性项的约束，以达到最优解。使用血管模拟数据和动物体内实验数据来评估所提出方法的性能。结果表明，与传统重建方法和U-Net相比，该方法实现了更高质量的稀疏重建。特别是，在极端稀疏投影（例如，32个投影）下，与传统的延迟相比，所提出的方法在体内数据的结构相似性方面实现了约260%的改进，在峰值信噪比方面实现了约30%的改进相加法。该方法有可能减少光声层析成像的采集时间和成本，这将进一步扩大应用范围。