Prompting Lipschitz-constrained network for multiple-in-one sparse-view CT reconstruction

TL;DR

This paper introduces LipNet, a Lipschitz-constrained network with an explicit prompt module for multiple sparse-view CT reconstruction, enabling a single model to handle various sampling settings efficiently and with provable convergence.

Contribution

The paper proposes LipNet with explicit Lipschitz constraints and an integrated prompt module, along with PromptCT, a storage-efficient framework for multiple-in-one SVCT reconstruction.

Findings

LipNet satisfies boundary property and Lipschitz continuity.

PromptCT achieves higher-quality reconstructions than benchmarks.

The approach reduces storage costs for multiple view configurations.

Abstract

Despite significant advancements in deep learning-based sparse-view computed tomography (SVCT) reconstruction algorithms, these methods still encounter two primary limitations: (i) It is challenging to explicitly prove that the prior networks of deep unfolding algorithms satisfy Lipschitz constraints due to their empirically designed nature. (ii) The substantial storage costs of training a separate model for each setting in the case of multiple views hinder practical clinical applications. To address these issues, we elaborate an explicitly provable Lipschitz-constrained network, dubbed LipNet, and integrate an explicit prompt module to provide discriminative knowledge of different sparse sampling settings, enabling the treatment of multiple sparse view configurations within a single model. Furthermore, we develop a storage-saving deep unfolding framework for multiple-in-one SVCT reconstruction, termed PromptCT, which embeds LipNet as its prior network to ensure the convergence of its corresponding iterative algorithm. In simulated and real data experiments, PromptCT outperforms benchmark reconstruction algorithms in multiple-in-one SVCT reconstruction, achieving higher-quality reconstructions with lower storage costs. On the theoretical side, we explicitly demonstrate that LipNet satisfies boundary property, further proving its Lipschitz continuity and subsequently analyzing the convergence of the proposed iterative algorithms. The data and code are publicly available at https://github.com/shibaoshun/PromptCT.