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

Area of Science:

• Medical Imaging
• Computational Imaging
• Biomedical Engineering

Background:

• Compressed sensing (CS) enables reduced data acquisition in imaging while preserving quality.
• Photoacoustic projection imaging (PAPI) traditionally uses general CS measurements.
• Practical constraints necessitate CS designs involving subsets of integrating line detectors (ILDs).

Purpose of the Study:

• To develop a cost-effective CS-PAPI system using structured measurement matrices.
• To adapt CS recovery theory for measurements involving subsets of ILDs.
• To achieve exact sparse recovery in PAPI under practical constraints.

Main Methods:

• Extended PAPI with a custom CS unit.
• Developed structured CS matrices suitable for subset-based ILD measurements.
• Employed a random search strategy to select optimal CS measurement matrices.
• Implemented a CS-PAPI system with a 4:3 compression factor using grouped ILDs.

Main Results:

• Demonstrated exact sparse recovery for the designed CS matrices.
• Validated the CS-PAPI system's performance through numerical experiments.
• Showcased the feasibility of integrating CS with proven sparse recovery into PAPI.

Conclusions:

• CS with sparse recovery capabilities is effectively integrated into PAPI.
• Numerical results confirm the viability of the proposed CS-PAPI system.
• Future research will focus on experimental validation and advanced data-driven methods.