A Kalman filtering framework for virtual sensor-enhanced photoacoustic imaging

Photoacoustic imaging (PAI) combines the high contrast of optical absorption with the spatial resolution of ultrasound detection; however, its performance is often constrained by incomplete angular sampling and measurement noise. In this work, we introduce a model-based Kalman filtering framework for estimating virtual sensor measurements at intermediate angular positions of a circular detection array.Instead of adding new detector elements, the method generates statistically consistent virtual measurements from the existing array, effectively enriching the angular information available to the reconstruction algorithm without altering the physical hardware. The Kalman formulation exploits the directional propagation of acoustic waves and the temporal coherence of photoacoustic signals to produce noise-aware, minimum-variance estimates of the pressure field. Using extensive k-Wave simulations that incorporate finite-aperture detectors, acoustic attenuation, and heterogeneous media,we demonstrate that the proposed virtual sensing strategy substantially improves structural preservation and yields higher quantitative image quality compared with interpolation-based methods. These results establish Kalman-domain virtual sensing as a practical and physically grounded approach for augmenting PAI acquisition systems and enhancing reconstruction quality without modifying the detector hardware.