Video-Rate Time-Reversal Photoacoustic Computed Tomography via Self-Supervised Multi-Frame Spatial Sparse Reconstruction

Time reversal (TR) serves as a fundamental reconstruction approach for photoacoustic computed tomography (PACT), featuring superior reconstruction accuracy rooted in the rigorous physical process. However, the full-wavefield propagation over the entire computational domain results in low computational throughput. Here, we propose spatial sparsification in the TR process and develop a self-supervised multi-frame super-resolution framework to reconstruct the sparse outputs, thereby achieving video-rate TR imaging without sacrificing image quality. The framework actively degrades images to provide training constraints and then extracts spatiotemporal correlations using 3D convolutions and cascaded structures. Spatiotemporal attention is employed to perform adaptive weighted fusion of multi-frame features. Animal and human finger joint imaging tests verify that our method yields superior image clarity under sparse reconstruction. Compared with conventional TR reconstruction, our method reduces computation time by 93% and achieves a frame rate of ∼24 Hz. When the raw images are used as input, the framework can break through the imaging resolution limit of the hardware and increase the Gray Level Variance by up to 7-fold. Moreover, the accurate image recovery capability of the proposed method enables high-fidelity quantitative analysis of physiological motion. This work overcomes the long-standing throughput bottleneck in TR imaging and is expected to accelerate the clinical translation of high-quality, video-rate PACT imaging.