Design, implementation, and analysis of a compressed sensing photoacoustic projection imaging system

• Published in: Journal of biomedical optics Vol. 29; no. S1; p. S11529
• Main Authors: Haltmeier, Markus, Ye, Matthias, Felbermayer, Karoline, Hinterleitner, Florian, Burgholzer, Peter
• Format: Journal Article
• Language: English
• Published: United States Society of Photo-Optical Instrumentation Engineers 01.01.2024
• Subjects: Algorithms; Data Compression - methods; Equipment Design; Image Processing, Computer-Assisted - methods; Phantoms, Imaging; Photoacoustic Techniques - instrumentation; Photoacoustic Techniques - methods; Special Issue Honoring Lihong V. Wang, Pioneer in Biomedical Optics; optimal design; structured measurement matrices; compressed sensing; photoacoustic projection imaging
• ISSN: 1083-3668; 1560-2281; 1560-2281
• DOI: 10.1117/1.JBO.29.S1.S11529

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). 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. 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. 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. 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.