HiSin: A Sinogram-Aware Framework for Efficient High-Resolution Inpainting
This work addresses computational bottlenecks in medical imaging for diagnostic applications, representing an incremental improvement in efficiency for a domain-specific task.
The paper tackles the problem of high-resolution sinogram inpainting for computed tomography, where missing projections cause artifacts, by proposing HiSin, a diffusion-based framework that reduces peak memory usage by up to 30.81% and inference time by up to 17.58% compared to state-of-the-art methods while maintaining accuracy.
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 that exploits spectral sparsity and structural heterogeneity of projection data. It progressively extracts global structure at low resolution and defers high-resolution inference to small patches, enabling memory-efficient inpainting. Considering the structural features of sinograms, we incorporate 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 30.81% and inference time by up to 17.58% than the state-of-the-art framework, and maintains inpainting accuracy across.