Physics-informed DeepCT: Sinogram Wavelet Decomposition Meets Masked Diffusion
Journal:
arXiv
Published Date:
Jan 17, 2025
Abstract
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.
