Comparison of the suitability of CBCT- and MR-based synthetic CTs for daily adaptive proton therapy in head and neck patients

Abstract

Cone-beam computed tomography (CBCT)- and magnetic resonance (MR)-images allow a daily observation of patient anatomy but are not directly suited for accurate proton dose calculations. This can be overcome by creating synthetic CTs (sCT) using deep convolutional neural networks. In this study, we compared sCTs based on CBCTs and MRs for head and neck (H&N) cancer patients in terms of image quality and proton dose calculation accuracy. A dataset of 27 H&N-patients, treated with proton therapy (PT), containing planning CTs (pCTs), repeat CTs, CBCTs and MRs were used to train two neural networks to convert either CBCTs or MRs into sCTs. Image quality was quantified by calculating mean absolute error (MAE), mean error (ME) and Dice similarity coefficient (DSC) for bones. The dose evaluation consisted of a systematic non-clinical analysis and a clinical recalculation of actually used proton treatment plans. Gamma analysis was performed for non-clinical and clinical treatment plans. For clinical treatment plans also dose to targets and organs at risk (OARs) and normal tissue complication probabilities (NTCP) were compared. CBCT-based sCTs resulted in higher image quality with an average MAE of 40 ± 4 HU and a DSC of 0.95, while for MR-based sCTs a MAE of 65 ± 4 HU and a DSC of 0.89 was observed. Also in clinical proton dose calculations, sCTCBCT achieved higher average gamma pass ratios (2%/2 mm criteria) than sCTMR (96.1% vs. 93.3%). Dose-volume histograms for selected OARs and NTCP-values showed a very small difference between sCTCBCT and sCTMR and a high agreement with the reference pCT. CBCT- and MR-based sCTs have the potential to enable accurate proton dose calculations valuable for daily adaptive PT. Significant image quality differences were observed but did not affect proton dose calculation accuracy in a similar manner. Especially the recalculation of clinical treatment plans showed high agreement with the pCT for both sCTCBCT and sCTMR.

Range probing as a quality control tool for CBCT‐based synthetic CTs: In vivo application for head and neck cancer patients

“…Thummerer et al 15 described in detail the generation of CBCT-based sCTs by means of the DCNN employed in this study. Image quality as well as dosimetric evaluations indicated the potential suitability of sCTs for proton dose calculations and thus their integrability within adaptive proton therapy workflows 15,16 . Our outcomes suggest that CT numbers in the sCT images are representative and that sCTs can be used for proton dose calculations in HNC patients, supporting the hypothesis of Thummerer et al…”

“…High dosimetric accuracy is a pre‐requisite of any sCT generation method to be suitable for clinical implementation in adaptive proton therapy workflows. The dosimetric accuracy of the sCT generation method used in this study was investigated previously, 15 , 16 recalculating clinical treatment plans on sCTs and same‐day rCTs. However, this procedure requires a reference CT image.…”

“…Previous studies have demonstrated the feasibility of deep learning‐based methods to generate sCTs with a high image quality. 13 , 14 , 15 , 16 , 17 , 18 , 27 However, the clinical implementation of sCTs has been hampered by the lack of QC tools that verify the images generated by the DCNN. The proposed QC tool provides a direct assessment of the CT number accuracy in the sCTs, by means of in vivo range measurements.…”

“…sCTs were generated using a DCNN, initially implemented for MR‐to‐CT conversion by Spadea et al ., 27 and later shown suitable also for CBCT‐based sCT generation. 15 , 16 CBCTs and CTs of 28 HNC patients treated with proton therapy at our institution were used for training and validation of the neural network (25 for training, 3 for validation). CBCTs and rCTs were acquired using the devices and parameters described above.…”

“…Furthermore, comparable sCT image quality and dosimetric accuracy were found with respect to other deep learning sCT generation strategies. 15 , 16 , 17 , 18 Preliminary dosimetric tests showed the potential for employing DCNN CBCT‐based sCTs in an adaptive proton therapy workflow. 15 However, DCNNs are trained on specific datasets which do not guarantee a predictable performance when they receive input images that fall outside the training dataset.…”