用随机掩码和小波分解提升稀疏投影CT重建的泛化能力
Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion
- 在投影数据上引入随机掩码，扩大训练样本空间
- 对小波高频分量采用随机策略，增强细节特征表达
- 两阶段迭代确保图像全局一致性和细节精度
扩散模型在稀疏视图计算机断层扫描（SVCT）重建中展现出巨大潜力。然而，当网络在有限样本空间上训练时，其泛化能力可能受限，导致在陌生数据上性能下降，表现为图像模糊和区域不一致。为此，我们提出基于投影图的小波随机分解与随机掩码扩散模型（SWARM）。通过在投影图中引入随机掩码，有效扩展了有限的训练样本空间，使模型能学习更广泛的数据分布，增强对数据不确定性的理解与泛化能力。同时，对投影图小波变换的高频分量应用随机训练策略，提升了多频带特征表达能力，改善了细节捕捉性能与鲁棒性。采用两阶段迭代重建方法，在保证图像全局一致性的同时精细化重构细节。实验结果表明，SWARM在多个数据集上的定量与定性指标均优于现有方法。
原文摘要 · Abstract (English)
Diffusion model shows remarkable potential on sparse-view computed tomography (SVCT) reconstruction. However, when a network is trained on a limited sample space, its generalization capability may be constrained, which degrades performance on unfamiliar data. For image generation tasks, this can lead to issues such as blurry details and inconsistencies between regions. To alleviate this problem, we propose a Sinogram-based Wavelet random decomposition And Random mask diffusion Model (SWARM) for SVCT reconstruction. Specifically, introducing a random mask strategy in the sinogram effectively expands the limited training sample space. This enables the model to learn a broader range of data distributions, enhancing its understanding and generalization of data uncertainty. In addition, applying a random training strategy to the high-frequency components of the sinogram wavelet enhances feature representation and improves the ability to capture details in different frequency bands, thereby improving performance and robustness. Two-stage iterative reconstruction method is adopted to ensure the global consistency of the reconstructed image while refining its details. Experimental results demonstrate that SWARM outperforms competing approaches in both quantitative and qualitative performance across various datasets.
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