用术前CT增强术中CBCT生成更逼真的合成CT。
Enhancing Synthetic CT from CBCT via Multimodal Fusion: A Study on the Impact of CBCT Quality and Alignment
- 融合术前CT与术中CBCT，提升合成CT质量。
- 低质量但对齐良好的CBCT，增强效果最显著。
- 成果在真实临床数据中可复现，适合放疗导航应用。
锥形束计算机断层扫描（CBCT）因辐射剂量低、采集速度快，广泛用于术中实时成像。然而，尽管分辨率高，其图像常存在显著伪影，视觉质量低于常规CT。为缓解此问题，近期研究提出合成CT（sCT）生成技术，将CBCT体积转换至CT域。本文通过多模态学习，将术中CBCT与术前CT融合，进一步提升sCT生成效果。我们在两个真实数据集上验证方法，并利用一个通用合成数据集，分析了CBCT-CT对齐程度和CBCT质量对sCT质量的影响。结果表明，多模态sCT始终优于单模态基线，尤其在对齐良好但质量较低的CBCT-CT场景下提升最显著。最后，这些发现可在真实临床数据中高度复现。
原文摘要 · Abstract (English)
Cone-Beam Computed Tomography (CBCT) is widely used for real-time intraoperative imaging due to its low radiation dose and high acquisition speed. However, despite its high resolution, CBCT suffers from significant artifacts and thereby lower visual quality, compared to conventional Computed Tomography (CT). A recent approach to mitigate these artifacts is synthetic CT (sCT) generation, translating CBCT volumes into the CT domain. In this work, we enhance sCT generation through multimodal learning, integrating intraoperative CBCT with preoperative CT. Beyond validation on two real-world datasets, we use a versatile synthetic dataset, to analyze how CBCT-CT alignment and CBCT quality affect sCT quality. The results demonstrate that multimodal sCT consistently outperform unimodal baselines, with the most significant gains observed in well-aligned, low-quality CBCT-CT cases. Finally, we demonstrate that these findings are highly reproducible in real-world clinical datasets.
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