Shape-optimized Model-based Reconstruction Algorithm for Radiacoustic Imaging

We present a shape-optimized model-based (SOMB) reconstruction framework that addresses the limited-view problem in radiacoustic imaging arising from restricted detector placement, which creates severely ill-posed inverse problems that manifest as characteristic artifacts in reconstructed images. We first reconstruct the approximate dose-region boundary using a parametric level set-based shape optimization. Pointwise model-based dose reconstruction is then performed only within this shape-constrained region. By restricting reconstruction from the full imaging domain to only the pixels within the identified region, the number of unknowns is significantly reduced, transforming a severely ill-posed inverse problem into a significantly better-constrained one. We validate this methodology through computational studies and experiments using clinical X-ray and proton radiation sources under limited angular coverage data acquisition settings. Results demonstrate substantial artifact reduction and improved accuracy compared to standard model-based reconstructions, with 3-25% improvement in correlation coefficients across different imaging settings-more pronounced improvements occurring under stronger limited-view conditions. This shape-optimized approach provides a viable pathway for accurate radiation therapy monitoring under clinically realistic detector configurations.