Dual-path deep learning reconstruction framework for propagation-based X-ray phase-contrast computed tomography with sparse-view projections

Propagation-based X-ray phase-contrast computed tomography (PB-PCCT) can serve as an effective tool for studying organ function and pathologies. However, it usually suffers from a high radiation dose due to the long scan time. To alleviate this problem, we propose a deep learning reconstruction framework for PB-PCCT with sparse-view projections. The framework consists of dual-path deep neural networks, where the edge detection, edge guidance, and artifact removal models are incorporated into two sub-networks. It is worth noting that the framework has the ability to achieve excellent performance by exploiting the data-based knowledge of the sample material characteristics and the model-based knowledge of PB-PCCT. To evaluate the effectiveness and capability of the proposed framework, simulations and real experiments were performed. The results demonstrated that the proposed framework could significantly suppress streaking artifacts and produce high-contrast and high-resolution computed tomography images. (C) 2021 Optical Society of America