Ge Jia, Mo Zongxin, Zhang Shuangyang, Zhang Xiaoming, Zhong Yutian, Liang Zhaoyong, Hu Chaobin, Chen Wufan, Qi Li
School of Biomedical Engineering, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China.
Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, 1023 Shatai Rd., Baiyun District, Guangzhou, Guangdong 510515, China.
Photoacoustics. 2024 Jun 4;38:100618. doi: 10.1016/j.pacs.2024.100618. eCollection 2024 Aug.
Photoacoustic tomography (PAT), as a novel medical imaging technology, provides structural, functional, and metabolism information of biological tissue . Sparse Sampling PAT, or SS-PAT, generates images with a smaller number of detectors, yet its image reconstruction is inherently ill-posed. Model-based methods are the state-of-the-art method for SS-PAT image reconstruction, but they require design of complex handcrafted prior. Owing to their ability to derive robust prior from labeled datasets, deep-learning-based methods have achieved great success in solving inverse problems, yet their interpretability is poor. Herein, we propose a novel SS-PAT image reconstruction method based on deep algorithm unrolling (DAU), which integrates the advantages of model-based and deep-learning-based methods. We firstly provide a thorough analysis of DAU for PAT reconstruction. Then, in order to incorporate the structural prior constraint, we propose a nested DAU framework based on plug-and-play Alternating Direction Method of Multipliers (PnP-ADMM) to deal with the sparse sampling problem. Experimental results on numerical simulation, animal imaging, and multispectral un-mixing demonstrate that the proposed DAU image reconstruction framework outperforms state-of-the-art model-based and deep-learning-based methods.
光声断层扫描（PAT）作为一种新型医学成像技术，可提供生物组织的结构、功能和代谢信息。稀疏采样PAT（SS-PAT）使用较少数量的探测器生成图像，但其图像重建本质上是不适定的。基于模型的方法是SS-PAT图像重建的最先进方法，但它们需要设计复杂的手工先验。基于深度学习的方法由于能够从标记数据集中导出鲁棒的先验，在解决逆问题方面取得了巨大成功，但其可解释性较差。在此，我们提出了一种基于深度算法展开（DAU）的新型SS-PAT图像重建方法，该方法整合了基于模型和基于深度学习的方法的优点。我们首先对用于PAT重建的DAU进行了深入分析。然后，为了纳入结构先验约束，我们提出了一种基于即插即用交替方向乘子法（PnP-ADMM）的嵌套DAU框架来处理稀疏采样问题。在数值模拟、动物成像和多光谱解混方面的实验结果表明，所提出的DAU图像重建框架优于基于模型和基于深度学习的最先进方法。
