标题： 通过多模态融合和端到端配准增强从CBCT生成的合成CT 显示英文标题

标题： Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration

摘要： 锥形束计算机断层扫描（CBCT）由于其快速的采集速度和较低的辐射剂量，被广泛用于术中成像。 然而，与传统的计算机断层扫描（CT）相比，CBCT图像通常存在伪影且视觉质量较低。 一种有前景的解决方案是合成CT（sCT）生成，其中将CBCT体积转换到CT领域。 在本工作中，我们通过联合利用术中CBCT和术前CT数据，采用多模态学习来增强sCT生成。 为克服模态之间的固有不对齐问题，我们在sCT流程中引入了一个端到端可学习的配准模块。 该模型在一个受控的合成数据集上进行评估，允许精确操控数据质量和对齐参数。 此外，我们在两个现实世界的临床数据集上验证了其鲁棒性和泛化能力。 实验结果表明，将配准集成到多模态sCT生成中提高了sCT的质量，在90个评估设置中的79个中优于基线多模态方法。 值得注意的是，在CBCT质量较低且术前CT中度错位的情况下，改进最为显著。 显示英文摘要

摘要： 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.