Investigating the use of canonical object constraints for the joint estimation of initial pressure and speed-of-sound distributions in photoacoustic computed tomography

In photoacoustic computed tomography (PACT), accurate estimation of the initial pressure (IP) distribution generally requires precise knowledge of the object's speed-of-sound (SOS) distribution, which is generally unknown. Joint reconstruction (JR) of the IP and SOS distributions from PACT measurement data offers a promising solution to this challenge by concurrently estimating both quantities. However, this joint estimation problem is ill-posed and is formulated as a non-convex optimization problem. This work investigates the effectiveness of using object constraints in improving the accuracy and stability of JR solutions in PACT. The focus is on canonical constraints — support, bound, and total variation constraints—which are grounded in prior knowledge about the properties of the imaged object. These constraints are aimed at constraining the solution space and potentially mitigating the non-uniqueness of the problem. Although these constraints are practical and straightforward to implement, their impact has not been thoroughly investigated in the context of JR for PACT. This study systematically evaluates their effectiveness through two-dimensional computer simulations using ana