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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 subnetworks. 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.