用扩散模型增强神经表示，提升CT金属伪影去除效果。
Diffusion Model Regularized Implicit Neural Representation for CT Metal Artifact Reduction
- 结合物理约束与预训练扩散模型先验知识。
- 在模拟与临床数据上均实现更好去伪影效果。
- 适合需要高保真度的医学影像处理场景。
CT图像在金属存在时常严重受伪影影响。现有监督方法因依赖有限的配对金属-无伪影数据，导致性能不稳定，限制了临床应用；现有无监督方法则面临两大挑战：一是未有效融入CT物理几何以保证数据保真度，二是传统正则化项无法充分捕捉丰富先验知识。为此，本文提出基于扩散模型正则化的隐式神经表示框架用于金属伪影消除（MAR）。隐式神经表示融合物理约束并保证数据保真，预训练扩散模型则提供先验知识以正则化解。在模拟与临床数据上的实验结果验证了该方法的有效性与泛化能力，凸显其在临床环境中的应用潜力。
原文摘要 · Abstract (English)
Computed tomography (CT) images are often severely corrupted by artifacts in the presence of metals. Existing supervised metal artifact reduction (MAR) approaches suffer from performance instability on known data due to their reliance on limited paired metal-clean data, which limits their clinical applicability. Moreover, existing unsupervised methods face two main challenges: 1) the CT physical geometry is not effectively incorporated into the MAR process to ensure data fidelity; 2) traditional heuristics regularization terms cannot fully capture the abundant prior knowledge available. To overcome these shortcomings, we propose diffusion model regularized implicit neural representation framework for MAR. The implicit neural representation integrates physical constraints and imposes data fidelity, while the pre-trained diffusion model provides prior knowledge to regularize the solution. Experimental results on both simulated and clinical data demonstrate the effectiveness and generalization ability of our method, highlighting its potential to be applied to clinical settings.
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