Improved synthetic CT generation using surface scan information integrated into a deformable registration algorithm for limited Field of View data
In brief
Adding surface scans cuts synthetic CT error by roughly 2000 HU in breast CBCT
In a study of 20 breast patients, incorporating surface-guided radiotherapy data into the deformable registration algorithm reduced the mean-squared error of limited-field-of-view synthetic CTs by about 2000 Hounsfield units versus the standard method, improving image similarity and dose accuracy. The technique shows promise for adaptive radiotherapy, but needs testing in other treatment sites.
- Journal
- Physics and imaging in radiation oncology (Q1)
- Published
- 29 July 2026
- Study design
- Cohort / observational study
- Evidence level
- Level 3, Low (CEBM 3b)
- Authors
- Joachim Marichal, Stina Svensson, Geert De Kerf, Ola Weistrand, Mattias Nilsing, Michaël Claessens, et al.
- PMID
- 42577028
- DOI
- 10.1016/j.phro.2026.101050
Why clinicians should know about it
- Picked for Medical Physics (top studies of the week, 16 August 2026): Surface‑scan guided synthetic CT improves CBCT limited‑FOV accuracy
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.
Abstract as published, via PubMed.
For healthcare professionals. The summary is generated by AI from the published abstract, and the evidence level is assigned automatically from the study design on the Oxford CEBM hierarchy. Neither is medical advice. Read the full paper before changing practice.