D2IP: Deep Dynamic Image Prior for 3D Time-sequence Pulmonary Impedance Imaging

• URL: http://arxiv.org/abs/2507.14046v1
• Date: Fri, 18 Jul 2025 16:14:09 GMT
• Title: D2IP: Deep Dynamic Image Prior for 3D Time-sequence Pulmonary Impedance Imaging
• Authors: Hao Fang, Hao Yu, Sihao Teng, Tao Zhang, Siyi Yuan, Huaiwu He, Zhe Liu, Yunjie Yang,
• Abstract summary: Deep Dynamic Image Prior (D2IP) is a novel framework for 3D time-sequence imaging.<n>Compared to state-of-the-art baselines, D2IP delivers superior image quality, with a 24.8% increase in average MSSIM and an 8.1% reduction in ERR.
• Score: 10.854639838765703
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised learning methods, such as Deep Image Prior (DIP), have shown great potential in tomographic 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 3D or time-sequence tomographic imaging tasks. To overcome these challenges, we propose Deep Dynamic Image Prior (D2IP), a novel framework for 3D time-sequence imaging. D2IP introduces three key strategies - Unsupervised Parameter Warm-Start (UPWS), Temporal Parameter Propagation (TPP), and a customized lightweight reconstruction backbone, 3D-FastResUNet - to accelerate convergence, enforce temporal coherence, and improve computational efficiency. Experimental results on both simulated and clinical pulmonary datasets demonstrate that D2IP enables fast and accurate 3D time-sequence Electrical Impedance Tomography (tsEIT) reconstruction. Compared to state-of-the-art baselines, D2IP delivers superior image quality, with a 24.8% increase in average MSSIM and an 8.1% reduction in ERR, alongside significantly reduced computational time (7.1x faster), highlighting its promise for clinical dynamic pulmonary imaging.

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