通过联合注册提升CBCT生成CT图像质量
Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
- 融合术中CBCT与术前CT，端到端学习配准
- 在90组测试中79组优于基线方法
- 低质量CBCT与中度错位时效果最显著
锥形束计算机断层扫描（CBCT）因快速成像和低辐射剂量广泛用于术中影像。但其图像常含伪影且视觉质量低于常规CT。一种有前景的解决方案是生成合成CT（sCT），将CBCT数据转换至CT域。本文通过联合使用术中CBCT与术前CT数据，利用多模态学习增强sCT生成。为解决模态间固有的不匹配问题，我们在sCT流程中引入可学习的端到端注册模块。模型在可控的合成数据集上评估，可精确调节数据质量和对齐参数。进一步在两个真实临床数据集上验证其鲁棒性与泛化能力。实验表明，集成注册的多模态sCT生成方法显著提升图像质量，在90组评估设置中79组优于基线方法。尤其在CBCT质量低且术前CT存在中度错位时，提升最为明显。
原文摘要 · Abstract (English)
Cone-Beam Computed Tomography (CBCT) is widely used for intraoperative imaging due to its rapid acquisition and low radiation dose. However, CBCT images typically suffer from artifacts and lower visual quality compared to conventional Computed Tomography (CT). A promising solution is synthetic CT (sCT) generation, where CBCT volumes are translated into the CT domain. In this work, we enhance sCT generation through multimodal learning by jointly leveraging intraoperative CBCT and preoperative CT data. To overcome the inherent misalignment between modalities, we introduce an end-to-end learnable registration module within the sCT pipeline. This model is evaluated on a controlled synthetic dataset, allowing precise manipulation of data quality and alignment parameters. Further, we validate its robustness and generalizability on two real-world clinical datasets. Experimental results demonstrate that integrating registration in multimodal sCT generation improves sCT quality, outperforming baseline multimodal methods in 79 out of 90 evaluation settings. Notably, the improvement is most significant in cases where CBCT quality is low and the preoperative CT is moderately misaligned.
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