English(EN) Artifact Reduction in Undersampled 3D Cone-Beam CTs using a Hybrid 2D-3D CNN Framework

混合CNN框架减少CT扫描伪影

作者 PulseAugur 编辑部 · [1 个来源] · 2026-09-01 04:00

研究人员开发了一种新颖的混合深度学习框架，用于减少欠采样3D锥束CT扫描中的伪影。该方法结合了用于从单个切片提取初始特征的2D U-Net和利用体积上下文预测无伪影图像的3D解码器。该方法旨在平衡计算效率与提高切片间一致性，以获得更好的诊断效用。 AI

影响这种混合深度学习方法提供了一种更有效的方法来提高医学成像质量，有可能减少患者的辐射暴露。

排序理由该集群包含一篇详细介绍新技术框架的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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

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arXiv cs.AI TIER_1 English(EN) · Johannes Thalhammer, Tina Dorosti, Sebastian Peterhansl, Daniela Pfeiffer, Franz Pfeiffer, Florian Schaff · 2026-09-01 04:00

使用混合二维-三维CNN框架减少欠采样三维锥束CT中的伪影

arXiv:2602.08727v2 Announce Type: replace-cross Abstract: Undersampled CT volumes minimize acquisition time and radiation exposure but introduce artifacts degrading image quality and diagnostic utility. Reducing these artifacts is critical for high-quality imaging. We propose a c…