Abstract
High-resolution sinogram inpainting is essential for computed tomography reconstruction, as missing high-frequency projections can lead to visible artifacts and diagnostic errors. Diffusion models are well-suited for this task due to their robustness and detail-preserving capabilities, but their application to high-resolution inputs is limited by excessive memory and computational demands. To address this limitation, we propose HiSin, a novel diffusion based framework for efficient sinogram inpainting via resolution-guided progressive inference. It progressively extracts global structure at low resolution and defers high-resolution inference to small patches, enabling memory-efficient inpainting. It further incorporates frequency-aware patch skipping and structure-adaptive step allocation to reduce redundant computation. Experimental results show that HiSin reduces peak memory usage by up to 31.25% and inference time by up to 18.15%, and maintains inpainting accuracy across datasets, resolutions, and mask conditions.
Abstract (translated)
高分辨率的扇束数据（sinogram）修复对于计算机断层成像（CT）重建至关重要，因为缺少高频投影会导致可见伪影和诊断错误。扩散模型由于其鲁棒性和细节保留能力而非常适合此任务，但它们在处理高分辨率输入时会受到内存和计算需求过大的限制。为了解决这一问题，我们提出了一种新的基于扩散的框架——HiSin，用于通过分辨率引导的逐步推理来进行高效的sinogram修复。该方法逐步在低分辨率下提取全局结构，并将高分辨率推理推延到小块处理中进行，从而实现内存效率高的修复过程。此外，它还结合了频率感知的小块跳过和结构自适应步骤分配来减少冗余计算。 实验结果显示，HiSin可以将峰值内存使用量降低最多31.25%，并将推理时间缩短最多18.15%。同时，在不同数据集、分辨率以及掩膜条件下，修复准确性得以保持。
URL
https://arxiv.org/abs/2506.08809
