Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data

Abstract

Background and purpose

Cone-Beam Computed Tomography (CBCT) based synthetic CT is being increasingly used for post-delivery dose computation or adaptive workflows in radiotherapy. However, the limited Field-of-View of the CBCT can cause inaccuracies when tissues fall outside the Field of View. This study evaluates the integration of surface-guided radiotherapy information to enhance synthetic CT generation for breast cases with missing tissues on CBCT.

Materials and Methods

A retrospective analysis was performed on 20 breast patients. CBCT volumes were acquired on a linac, with simultaneous surface scans from three cameras. A full Field of View CBCT was used to generate a reference synthetic CT. A smaller Field of View CBCT was reconstructed from the same raw data to create two synthetic CTs: a standard synthetic CT and a surface-guided synthetic CT, for which surface scan information was incorporated into the algorithm to guide reconstruction outside the Field of View. We compared the image similarity, external contour geometry, and dose distribution.

Results

The surface-guided synthetic CT showed superior agreement with the reference for nine tested metrics. Specifically, it showed an average Mean Squared Error reduction of 1963 HU${}^2$ ($p=0.002$), revealing hot spots in some cases.

Conclusion

Integrating surface scan information into deformable registration improves synthetic CT generation for CBCT with limited Field of View, yielding more accurate images and enhanced dose distribution precision for breast cases. Future work will explore other sites, and potential applications for adaptive radiotherapy.