Enhancing Synthetic CT from CBCT via Multimodal Fusion and End-To-End Registration
This work addresses the challenge of low-quality CBCT images for intraoperative imaging in clinical settings, representing an incremental improvement in multimodal sCT generation.
The paper tackled the problem of generating synthetic CT from CBCT by integrating an end-to-end learnable registration module to handle misalignment between modalities, resulting in improved sCT quality that outperformed baseline methods in 79 out of 90 evaluation settings, especially when CBCT quality was low and CT was moderately misaligned.
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.