用X光辅助稀疏中子CT重建，无需重训练扩散模型。
Cross-Modal Guidance for Fast Diffusion-Based Computed Tomography
- 引入跨模态引导，不重训练扩散模型即可提升重建质量。
- 在稀疏视角下，结合X光数据使中子CT重建质量显著提升。
- 适用于测量成本高、数据稀疏的成像场景，如中子断层扫描。
扩散模型已成为解决计算机断层扫描（CT）逆问题的强大先验。在某些应用中，如中子断层扫描，即使单次扫描也难以获取大量测量数据，导致数据稀疏，即使使用扩散模型也难以获得高质量重建。一种缓解策略是利用易获取的互补成像模态，但这类方法通常需要大规模数据重新训练扩散模型。本文提出一种无需重训练扩散先验的跨模态引导方法，实现高成本模态的加速成像。我们还研究了不完美侧模态对跨模态引导的影响。该方法在稀疏视角中子断层扫描上进行评估，结果表明，结合同一样本的X射线断层扫描数据可显著提升重建质量。
原文摘要 · Abstract (English)
Diffusion models have emerged as powerful priors for solving inverse problems in computed tomography (CT). In certain applications, such as neutron CT, it can be expensive to collect large amounts of measurements even for a single scan, leading to sparse data sets from which it is challenging to obtain high quality reconstructions even with diffusion models. One strategy to mitigate this challenge is to leverage a complementary, easily available imaging modality; however, such approaches typically require retraining the diffusion model with large datasets. In this work, we propose incorporating an additional modality without retraining the diffusion prior, enabling accelerated imaging of costly modalities. We further examine the impact of imperfect side modalities on cross-modal guidance. Our method is evaluated on sparse-view neutron computed tomography, where reconstruction quality is substantially improved by incorporating X-ray computed tomography of the same samples.
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