LAUSR

Abstract Purpose Cone‐beam CT (CBCT)–based synthetic CT (sCT) dose calculation has the potential to make the adaptive radiotherapy (ART) pathway more efficient while removing subjectivity. This study assessed four sCT generation methods using 15 head‐and‐neck rescanned ART patients. Each patient's planning CT (pCT), rescan CT (rCT), and CBCT post‐rCT was acquired with the CBCT deformably registered to the rCT (dCBCT). Methods The four methods investigated were as follows: method 1—deformably registering the pCT to the dCBCT. Method 2—assigning six mass density values to the dCBCT. Method 3—iteratively removing artifacts and correcting the dCBCT Hounsfield units (HU). Method 4—using a cycle general adversarial network machine learning model (trained with 45 paired pCT and CBCT). Treatment plans were created on the rCT and recalculated on each sCT. Planning target volume (PTV) and organ‐at‐risk (OAR) structures were contoured by clinicians on the rCT (high‐dose PTV, low‐dose PTV, spinal canal, larynx, brainstem, and parotids) to allow the assessment of dose–volume histogram statistics at clinically relevant points. Results The HU mean absolute error (MAE) and minimum dose gamma index pass rate (2%/2 mm) were calculated, and the generation time was measured for 15 patients using the rCT as the comparator. For methods 1–4 the MAE, gamma index analysis, and generation time were as follows: 59.7 HU, 100.0%, and 143 s; 164.2 HU, 95.2%, and 232 s; 75.7 HU, 99.9%, and 153 s; and 79.4 HU, 99.8%, and 112 s, respectively. Dose differences for PTVs and OARs were all <0.3 Gy except for method 2 (<0.5 Gy). Conclusion All methods were considered clinically viable. The machine learning method was found to be most suitable for clinical implementation due to its high dosimetric accuracy and short generation time. Further investigation is required for larger anatomical changes between the CBCT and pCT and for other anatomical sites.