Deep dynamic image prior for three-dimensional time-sequence pulmonary electrical impedance tomography

Unsupervised learning methods, such as Deep Image Prior (DIP), have shown great potential in engineering imaging due to their training-data-free nature and high generalization capability. However, their reliance on numerous network parameter iterations results in high computational costs, limiting their practical application, particularly in complex three-dimensional (3D) or time-sequence tomographic imaging tasks. To overcome these challenges, we propose Deep Dynamic Image Prior (D 2 IP), a novel framework for three-dimensional time-sequence imaging. D 2 IP introduces three key strategies — Unsupervised Parameter Warm-Start (UPWS), Temporal Parameter Propagation (TPP), and a customized lightweight reconstruction backbone, Three-dimensional Fast Residual U-Net (3D-FastResUNet) — to accelerate convergence, enforce temporal coherence, and improve computational efficiency. Experimental results on both simulated and clinical pulmonary datasets demonstrate that D 2 IP enables fast and accurate 3D time-sequence Electrical Impedance Tomography (tsEIT) reconstruction. Compared to the state-of-the-art Regularized Shallow Image Prior (R-SIP) baseline, D 2 IP delivers superior image quality — with a 24.8% increase in average Mean Structural Similarity Index (MSSIM) and an 8.1% reduction in Relative Error (ERR) — alongside significantly reduced computational time (7.1× faster), demonstrating its promise for artificial intelligence (AI)-driven medical engineering applications, as exemplified by clinical dynamic pulmonary imaging.