English(EN) A deep dictionary network-based foundation model for ultra-low-dose CT denoising

新型深度字典网络模型推动超低剂量CT图像去噪技术发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-16 04:00

研究人员开发了一种名为深度字典网络（DDN）的新型基础模型，用于超低剂量计算机断层扫描（ULDCT）图像的去噪。该模型旨在克服现有方法通常具有器官特异性且缺乏泛化能力的局限性。DDN架构旨在提高可解释性，其灵感来源于稀疏表示理论，并包含动态字典和阈值生成模块。该模型在超过一百万张常规剂量CT图像上进行了预训练，然后在ULDCT数据集上进行了微调，在不同解剖区域均展现出最先进的性能。 AI

影响这项研究有望实现更清晰的医学成像，同时减少辐射暴露，从而提高诊断准确性和患者安全性。

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arXiv cs.LG TIER_1 English(EN) · Baoshun Shi, Shuangyi Yang, Ke Jiang, Bin Zhu, Zhanli Hu, Huazhu Fu · 2026-09-16 04:00

一种基于深度字典网络的超低剂量CT去噪基础模型

arXiv:2609.16031v1 Announce Type: cross Abstract: Ultra-low-dose computed tomography (ULDCT) reduces radiation exposure but suffers from severe noise that degrades diagnostic image quality. Existing deep learning-based denoising methods are typically trained in an organ-specific …

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一种基于深度字典网络的超低剂量CT去噪基础模型

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