Photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer

Photoacoustic-computed tomography is a novel imaging technique that combines high absorption contrast and deep tissue penetration capability, enabling comprehensive three-dimensional imaging of biological targets. However, the increasing demand for higher resolution and real-time imaging results in significant data volume, limiting data storage, transmission and processing efficiency of system. Therefore, there is an urgent need for an effective method to compress the raw data without compromising image quality. This paper presents a photoacoustic-computed tomography 3D data compression method and system based on Wavelet-Transformer. This method is based on the cooperative compression framework that integrates wavelet hard coding with deep learning-based soft decoding. It combines the multi-scale analysis capability of wavelet transforms with the global feature modeling advantage of Transformers, achieving high-quality data compression and reconstruction. Experimental results using k-wave simulation suggest that the proposed compression system has advantages under extreme compression conditions, achieving a raw data compression ratio of up to 1:40. Furthermore, three-dimensional data compression experiment using in vivo mouse demonstrated that the maximum peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM) values of reconstructed images reached 38.60 and 0.9583, effectively overcoming detail loss and artifacts introduced by raw data compression. All the results suggest that the proposed system can significantly reduce storage requirements and hardware cost, enhancing computational efficiency and image quality. These advantages support the development of photoacoustic-computed tomography toward higher efficiency, real-time performance and intelligent functionality.