A fast and reliable quality assurance framework for CBCT-based synthetic CTs for adaptive proton radiotherapy

Why clinicians should know about it

• Picked for Medical Physics (paper of the day, 20 October 2026): Combined image and dose MAE classifies sCT suitability

Abstract


Cone beam CT based synthetic CTs (sCTs) enable online adaptive proton therapy by preserving daily patient anatomy without additional acquisition of diagnostic-quality CTs. However, clinical adoption is limited by the lack of fast, reliable quality assurance (QA) methods. This study proposes and evaluates a practical QA workflow to assess sCT suitability for proton dose optimization. 
Approach:
A two-step sCT-QA workflow was developed in which a candidate sCT intended for reoptimization (sCTref) is compared with an independently generated secondary sCT (sCTtest). The workflow combines image based metrics and dose based metrics, with doses derived from recalculating a template proton plan on both sCTs. In this study, the framework was evaluated using a deformably propagated virtual CT (vCT) as sCTref and either a deep learning-based sCT (sCTDL) or an analytically corrected CBCT (CBCTcorr) as sCTtest. Same-day CTs were deformably registered to the CBCT and used as ground truth (CTGT) for evaluation. Thresholds were optimized using 5-fold cross-validation on 182 head and neck (H&N) CT-CBCT pairs. Dose calculation suitability was defined as a gamma pass rate (2%, 1mm) exceeding 95% between dose distributions computed on sCT and CTGT. The method was further tested on an external H&N dataset and on abdominal cases.
Main Results:
Optimal classification performance was achieved using a combination of image mean absolute error (MAE) and dose MAE. On the H&N test set, the workflow achieved balanced accuracies of 91.2% and 89.7% for the vCT-CBCTcorr and vCT-sCTDL comparisons, respectively, while maintaining a low false positive rate. When applied without modifications to the external H&N and abdominal datasets, balanced accuracy was 94.7% (HN) and 85.0% (abdominal) for vCT-CBCTcorr, and 37.9% (HN) and 61.1% (abdominal) for vCT-sCTDL.
Significance:
This study introduces a clinically feasible sCT QA workflow for CBCT based adaptive proton therapy and showed promising transferability across datasets.

Abstract as published, via PubMed.