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English(EN) $K$-NeAS: Scalable Multi-Material CT Reconstruction Using Neural SDFs

$K$-NeAS 使用神经SDF推进多材料CT重建

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了 $K$-NeAS，一种用于可扩展多材料CT重建的新型架构。该系统利用神经符号距离函数（SDF）和高斯混合模型（GMM）来自动进行衰减边界确定，无需手动调整。$K$-NeAS 可以模拟任意数量的重叠组织，并已证明具有卓越的 3D 体积保真度，尤其是在腹部等复杂多组织区域，其性能优于现有的单材料基线。 AI

排序理由 该集群包含一篇详细介绍CT重建新方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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$K$-NeAS 使用神经SDF推进多材料CT重建

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该集群包含一篇详细介绍CT重建新方法的学术论文。[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) · Daksh K. Shah, Emmanouil Nikolakakis, Razvan V. Marinescu ·

    $K$-NeAS：使用神经SDF的可扩展多材料CT重建

    arXiv:2607.14415v1 Announce Type: new Abstract: Computed Tomography (CT) carries significant ionizing radiation risks, driving the need for sparse-view reconstruction. Implicit scene representations (ISRs) address this by recovering continuous volumetric attenuation fields direct…

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