Significance: Compressed sensing (CS) uses special measurement designs combined with powerful mathematical algorithms to reduce the amount of data to be collected while maintaining image quality. This is relevant to almost any imaging modality, and in this paper we focus on CS in photoacoustic projection imaging (PAPI) with integrating line detectors (ILDs). Aim: Our previous research involved rather general CS measurements, where each ILD can contribute to any measurement. In the real world, however, the design of CS measurements is subject to practical constraints. In this research, we aim at a CS-PAPI system where each measurement involves only a subset of ILDs, and which can be implemented in a cost-effective manner. Approach: We extend the existing PAPI with a self-developed CS unit. The system provides structured CS matrices for which the existing recovery theory cannot be applied directly. A random search strategy is applied to select the CS measurement matrix within this class for which we obtain exact sparse recovery. Results: We implement a CS PAPI system for a compression factor of $4:3$, where specific measurements are made on separate groups of 16 ILDs. We algorithmically design optimal CS measurements that have proven sparse CS capabilities. Numerical experiments are used to support our results. Conclusions: CS with proven sparse recovery capabilities can be integrated into PAPI, and numerical results support this setup. Future work will focus on applying it to experimental data and utilizing data-driven approaches to enhance the compression factor and generalize the signal class.

翻译：意义：压缩感知（CS）通过结合特殊测量设计与强大的数学算法，在保持图像质量的同时减少需采集的数据量。该方法适用于几乎所有成像模态，本文重点研究采用集成线探测器（ILD）的光声投影成像（PAPI）中的CS技术。目的：我们之前的研究涉及较为通用的CS测量，其中每个ILD可参与任意测量。然而在实际应用中，CS测量的设计需受限于实际约束。本研究旨在构建一种CS-PAPI系统，其中每次测量仅涉及部分ILD，且能以经济高效的方式实现。方法：我们在现有PAPI系统基础上集成自主研发的CS单元。该系统生成的CS矩阵具有特定结构，现有恢复理论无法直接适用。我们采用随机搜索策略，在此类矩阵中选择能够实现精确稀疏恢复的CS测量矩阵。结果：我们实现了一个压缩比为$4:3$的CS PAPI系统，对每组16个ILD进行特定测量。通过算法设计了具有可证稀疏恢复能力的最优CS测量方案，并通过数值实验验证了该方案的有效性。结论：具有可证稀疏恢复能力的CS可集成至PAPI系统，数值结果支持该配置。未来工作将聚焦于实验数据应用，并利用数据驱动方法提升压缩比及拓展信号类别。