Jeong Gangwon, Villa Umberto, Anastasio Mark A
Department of Bioengineering, University of Illinois at Urbana-Champaign, 1406 W Green St, Urbana, IL, 61801, USA.
Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, 201 E 24th St, Austin, TX, 78712, USA.
Photoacoustics. 2025 Mar 6;43:100700. doi: 10.1016/j.pacs.2025.100700. eCollection 2025 Jun.
In photoacoustic computed tomography (PACT) the accurate estimation of the initial pressure (IP) distribution generally requires knowledge of the object's heterogeneous speed-of-sound (SOS) distribution. Although hybrid imagers that combine ultrasound tomography with PACT have been proposed, in many current applications of PACT the SOS distribution remains unknown. Joint reconstruction (JR) of the IP and SOS distributions from PACT measurement data alone can address this issue. However, this joint estimation problem is ill-posed and corresponds to a non-convex optimization problem. While certain regularization strategies have been deployed, stabilizing the JR problem to yield accurate estimates of the IP and SOS distributions has remained an open challenge. To address this, the presented numerical studies explore the effectiveness of easy to implement canonical object constraints for stabilizing the JR problem. The considered constraints include support, bound, and total variation constraints, which are incorporated into an optimization-based method for JR. Computer-simulation studies that employ anatomically realistic numerical breast phantoms are conducted to evaluate the impact of these object constraints on JR accuracy. Additionally, the impact of certain data inconsistencies, such as caused by measurement noise and physics modeling mismatches, on the effectiveness of the object constraints is investigated. The results demonstrate, for the first time, that the incorporation of canonical object constraints in an optimization-based image reconstruction method holds significant potential for mitigating the ill-posed nature of the PACT JR problem.
在光声计算机断层扫描（PACT）中，初始压力（IP）分布的准确估计通常需要了解物体的非均匀声速（SOS）分布。尽管已经提出了将超声断层扫描与PACT相结合的混合成像仪，但在当前许多PACT应用中，SOS分布仍然未知。仅从PACT测量数据中对IP和SOS分布进行联合重建（JR）可以解决这个问题。然而，这个联合估计问题是不适定的，并且对应于一个非凸优化问题。虽然已经部署了某些正则化策略，但稳定JR问题以获得IP和SOS分布的准确估计仍然是一个未解决的挑战。为了解决这个问题，本文提出的数值研究探讨了易于实现的规范物体约束对稳定JR问题的有效性。所考虑的约束包括支撑、边界和总变差约束，这些约束被纳入基于优化的JR方法中。进行了使用解剖学逼真的数值乳房模型的计算机模拟研究，以评估这些物体约束对JR准确性的影响。此外，还研究了某些数据不一致性，如由测量噪声和物理建模不匹配引起的，对物体约束有效性的影响。结果首次表明，在基于优化的图像重建方法中纳入规范物体约束对于减轻PACT JR问题的不适定性具有巨大潜力。
