Public articles linked to the same research event.
arXiv The work proposes a deformation-aware CBCT updating framework that uses robotic ultrasound as a dynamic proxy to infer tissue motion: after hand-eye calibration initialization and LC2-based rigid refinement, a lightweight network, USCorUNet, estimates dense bidirectional deformation fields from adjacent ultrasound frames, which are spatially regularized and transferred to the CBCT reference slice, enabling real-time end-to-end CBCT slice updating without additional radiation exposure, validated on phantom and in vivo data for both deformation estimation and ultrasound-guided CBCT updating.
The work proposes a deformation-aware CBCT updating framework that uses robotic ultrasound as a dynamic proxy to infer tissue motion: after hand-eye calibration initialization and LC2-based rigid refinement, a lightweight network, USCorUNet, estimates dense bidirectional deformation fields from adjacent ultrasound frames, which are spatially regularized and transferred to the CBCT reference slice, enabling real-time end-to-end CBCT slice updating without additional radiation exposure, validated on phantom and in vivo data for both deformation estimation and ultrasound-guided CBCT updating.
The work proposes a deformation-aware CBCT updating framework that uses robotic ultrasound as a dynamic proxy to infer tissue motion: after hand-eye calibration initialization and LC2-based rigid refinement, a lightweight network, USCorUNet, estimates dense bidirectional deformation fields from adjacent ultrasound frames, which are spatially regularized and transferred to the CBCT reference slice, enabling real-time end-to-end CBCT slice updating without additional radiation exposure, validated on phantom and in vivo data for both deformation estimation and ultrasound-guided CBCT updating.
The work proposes a deformation-aware CBCT updating framework that uses robotic ultrasound as a dynamic proxy to infer tissue motion: after hand-eye calibration initialization and LC2-based rigid refinement, a lightweight network, USCorUNet, estimates dense bidirectional deformation fields from adjacent ultrasound frames, which are spatially regularized and transferred to the CBCT reference slice, enabling real-time end-to-end CBCT slice updating without additional radiation exposure, validated on phantom and in vivo data for both deformation estimation and ultrasound-guided CBCT updating.