End-to-End Hardware Modeling and Sensitivity Optimization of Photoacoustic Signal Readout Chains

TL;DR

This paper presents a comprehensive analytical model for optimizing the sensitivity of photoacoustic imaging detection chains, considering end-to-end coupling effects among transducer, cable, and receiver, validated with experimental data.

Contribution

It develops a complete end-to-end sensitivity model based on the KLM approach, including cable and receiver effects, and analyzes coupling effects for improved PAI system performance.

Findings

Model achieves less than 5% average error in validation.

Identifies low-frequency tailing due to model assumptions.

Provides insights for artifact suppression in PAI.

Abstract

The sensitivity of the acoustic detection subsystem in photoacoustic imaging (PAI) critically affects image quality. However, previous studies often focused only on front-end acoustic components or back-end electronic components, overlooking end-to-end coupling among the transducer, cable, and receiver. This work develops a complete analytical model for system-level sensitivity optimization based on the Krimholtz, Leedom, and Matthaei (KLM) model. The KLM model is rederived from first principles of linear piezoelectric constitutive equations, 1D wave equations and transmission line theory to clarify its physical basis and applicable conditions. By encapsulating the acoustic components into a controlled voltage source and extending the model to include lumped-parameter representations of cable and receiver, an end-to-end equivalent circuit is established. Analytical expressions for the system transfer functions are derived, revealing the coupling effects among key parameters such as transducer element area (EA), cable length (CL), and receiver impedance (RI). Experimental results validate the model with an average error below 5%. Additionally, a low-frequency tailing phenomenon arising from exceeding the 1D vibration assumption is identified and analyzed, illustrating the importance of understanding the model's applicable conditions and providing a potential pathway for artifact suppression. This work offers a comprehensive framework for optimizing detection sensitivity and improving image fidelity in PAI systems.