Dual-Domain Cross-Prompt Learning for Efficient Sparse-View CT

Sparse-view computed tomography (CT) effectively reduces radiation exposure, yet it degrades image signal-to-noise ratio (SNR) and compromises the reliability of clinical diagnosis. Deep unrolling networks, which integrate the merits of optimization-based and data-driven paradigms, have achieved promising performance for sparse-view CT reconstruction. However, existing learned data consistency (DC) and prompt-based reconstruction methods only capture simplistic image priors and rely on elaborately designed regularizers as well as excessive unrolled iterations, leading to heavy computational overhead and over-smoothed results. In this work, we integrate prompt learning into unrolled gradient descent networks and propose a Dual-domain Cross-Prompt Learning (DCPL) framework to address these limitations. Specifically, we first design an implicit pixel-wise learnable step size to adapt to the spatial gradient heterogeneity of CT images. We then integrate learnable prompts separately into the data-fidelity and regularization terms during unrolled iterations, enabling the model to adaptively capture intrinsic CT anatomical and noise priors. Furthermore, a cross-prompt guiding mechanism is developed to enable inter-domain prompt interaction, which facilitates efficient prompt generation and enhances the convergence stability of the model. Extensive experiments on multiple clinical benchmarks under both in-domain and cross-domain settings demonstrate that our DCPL achieves consistent improvements in artifact suppression and fine structural preservation, even under extremely sparse-view sampling. Notably, our method delivers robust reconstruction quality with significantly fewer parameters, higher inference efficiency, and with only a few unrolled iterations.