Dynamic quantitative photoacoustic tomography under sparse sampling via residual-driven Kalman filter assisted reconstruction.

Mechanically scanned photoacoustic tomography (PAT) provides high detection sensitivity and flexible system configurations, but its application to dynamic quantitative imaging is limited by the trade-off between temporal resolution and spatial sampling density. We propose a coordinated acquisition-estimation-reconstruction (AER) framework for dynamic quantitative PAT under sparse-pattern sampling. Sparse-pattern cyclic (SPC) acquisition collects baseline-referenced differential signals, while residual-driven adaptive Kalman filtering (RDAKF) adapts location-specific state-transition matrices at sampled locations and uses neighborhood-weighted transition information to propagate states at unsampled locations. The recovered spatially complete differential PA waveforms are then used to reconstruct absorption-coefficient perturbations through adjoint-based, PDE-constrained optimization. The framework was evaluated in numerical simulations, dynamic phantom experiments, and ex vivo tissue experiments using a single-element transmission-mode PAT system. Across multiple sparse-pattern configurations in the dynamic phantom experiment, structural similarity index values of 0.81-0.84 and root-mean-square errors of 0.017-0.023 mm⁻¹ were obtained. In the single-location-per-pattern configuration, the mean sparse-pattern acquisition rate was approximately 0.84 Hz. The framework tracked gradual, abrupt, periodic, and motion-associated variations, supporting the feasibility of dynamic quantitative PAT under measurement-limited conditions.