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论文ICLR 2026 Poster2026 年clinical prediction

DM4CT:计算机断层重建扩散模型基准

ICLR 2026 Poster accepted paper at ICLR 2026. Diffusion models have recently emerged as powerful priors for solving inverse problems. While Computed Tomography (CT) is theoretically a linear inverse problem, it poses many practical challenges. These include correlated noise, artifact structures, reliance on system geometry, and misaligned value ranges, which make the direct application of diffusion models more difficult than in domains like natural image generation. To systematically evaluate how diffusion models perform in this context and compare them with established reconstruction methods, we introduce DM4CT, a comprehensive benchmark for CT reconstruction. Code/project link: https://github.com/DM4CT/DM4CT

论文默认配图 - 医学影像计算

论文详情

英文标题
DM4CT: Benchmarking Diffusion Models for Computed Tomography Reconstruction
作者
Jiayang Shi, Daniel Pelt, Joost Batenburg
期刊/会议
ICLR 2026 Poster
发表年份
2026 年
研究方向
clinical prediction