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English(EN) Neighbor2Inverse: Self-Supervised Denoising for Low-Dose Region-of-Interest Phase Contrast CT

新的自监督方法改进了低剂量CT在医学成像中的去噪效果

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了新的自监督学习方法，用于去噪低剂量CT扫描，这是减少医学成像辐射暴露的关键步骤。一种方法是渐进式$\mathcal{J}$-不变学习（Progressive $\mathcal{J}$-Invariant Learning），它使用分步机制和噪声注入来提高去噪效率和性能，在梅奥LDCT数据集上优于现有的自监督方法。另一种方法Neighbor2Inverse，将Neighbor2Neighbor原理应用于相衬CT，通过欠采样投影创建去噪网络，以在抑制噪声的同时保留结构细节，这两种方法都为专业和临床CT应用展示了潜力。 AI

影响 自监督去噪的进步可以通过在不牺牲图像质量的情况下降低辐射剂量，从而实现更安全的医学成像。

排序理由 两篇arXiv论文详细介绍了用于低剂量CT去噪的新型自监督学习方法。

在 arXiv cs.CV 阅读 →

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新的自监督方法改进了低剂量CT在医学成像中的去噪效果

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两篇arXiv论文详细介绍了用于低剂量CT去噪的新型自监督学习方法。
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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Yichao Liu, Zongru Shao, Yueyang Teng, Junwen Guo ·

    低剂量CT去噪的渐进式$\mathcal{J}$-不变自监督学习

    arXiv:2601.14180v3 Announce Type: replace Abstract: Self-supervised learning has been increasingly investigated for low-dose computed tomography (LDCT) image denoising, as it alleviates the dependence on paired normal-dose CT (NDCT) data, which are often difficult to collect. How…

  2. arXiv cs.CV TIER_1 English(EN) · Johannes B. Thalhammer, Lorenzo D'Amico, Lucy Costello, Sebastian Peterhansl, Daniel Frey, Tina Dorosti, Florian Schaff, Jannis Ahlers, Ronan Smith, Marcus Kitchen, Franz Pfeiffer, Martin Donnelley, Daniela Pfeiffer, Kaye S. Morgan ·

    Neighbor2Inverse: 低剂量感兴趣区域相衬CT的自监督去噪

    arXiv:2605.01075v1 Announce Type: new Abstract: Propagation-based X-ray phase-contrast imaging (PBI) enables high-contrast visualization of lung structures and holds strong medical potential. However, safe translation to the clinic will require a substantial radiation dose reduct…

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