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English(EN) DeVAR: Low-Dose CT Denoising via Visual Autoregressive Modeling

新的DeVAR框架使用视觉自回归模型进行CT去噪

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员推出了一种新颖的生成式框架DeVAR，用于低剂量CT（LDCT）去噪，该框架利用视觉自回归模型。该方法将正常剂量CT（NDCT）图像的生成条件化为全局上下文令牌，并逐步预测离散令牌图。为了增强细节保留，DeVAR包含了一个残差精炼器来捕捉细微的解剖结构，以及一个双表示混合训练策略，用于无缝集成连续和离散的潜在表示。在公共数据集上的实验表明，DeVAR在定性和定量评估中均优于现有的最先进的LDCT去噪方法。 AI

影响 这项研究通过减少辐射暴露，使CT扫描更清晰，从而可能提高医学影像的诊断准确性。

排序理由 该集群描述了一篇关于一种新的医学图像去噪方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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新的DeVAR框架使用视觉自回归模型进行CT去噪

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该集群描述了一篇关于一种新的医学图像去噪方法的最新研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Xuepeng Liu, Zetong Liu, Renyiming Li, Yan Li, Ruiyu Li, Ruili Li, Jiayi Ding, Eichi Takaya ·

    ProSAC-CT：用于低剂量CT去噪的渐进式光谱-解剖联合引导多阶段扩散模型

    arXiv:2607.01756v1 Announce Type: new Abstract: Low-dose computed tomography (LDCT) reduces radiation exposure but introduces stronger quantum noise, streak artifacts, and local texture degradation, which can obscure anatomical boundaries and weaken low-contrast structures. Diffu…

  2. arXiv cs.CV TIER_1 English(EN) · Xizhuo Zhang, Yannian Gu, Zhongzhen Huang, Shaoting Zhang, Xiaofan Zhang ·

    DeVAR：通过视觉自回归建模实现低剂量CT去噪

    arXiv:2606.28453v1 Announce Type: cross Abstract: Computed tomography (CT) plays a crucial role in medical diagnosis, but minimizing radiation exposure while maintaining image quality remains a critical challenge. Low-dose CT (LDCT) protocols reduce radiation risks but inevitably…

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