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English(EN) Dual-Domain Self-Supervised Artifact Removal Framework for Photoacoustic Computed Tomography

新框架可去除光声CT图像中的伪影

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种新颖的自监督框架，用于去除光声计算机断层成像（PACT）图像中的伪影。该方法利用了 Siamese 神经网络和复合损失函数，该函数考虑了跨域保真度和不确定性加权一致性。该框架有效地分离了双域特征以过滤掉伪影，在模拟、模型和大小鼠及人体实验数据中均显示出图像质量的显著提高。此外，该方法通过加速的逆算子提供了计算效率。 AI

影响 提高了医学成像应用中的图像质量，可能有助于术前规划和诊断。

排序理由 详细介绍新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

AI 生成摘要 · Google Gemini · 来自 1 个来源。 我们如何撰写摘要 →

新框架可去除光声CT图像中的伪影

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详细介绍新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CV TIER_1 English(EN) · Yucheng Zhou, Shuang Li, Yu Zhang, Yibing Wang, Chulhong Kim, Seongwook Choi, Changhui Li ·

    用于光声计算机断层成像的双域自监督伪影去除框架

    arXiv:2607.16304v1 Announce Type: new Abstract: Photoacoustic Computed Tomography (PACT) often faces severe challenges from reconstruction artifacts due to sparse detection conditions. In this work, based on the distinct differences in artifact patterns between back-projection-ba…

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