Comparison of CBCT based synthetic CT methods suitable for proton dose calculations in adaptive proton therapy

Abstract

In-room imaging is a prerequisite for adaptive proton therapy. The use of onboard cone-beam computed tomography (CBCT) imaging, which is routinely acquired for patient position verification, can enable daily dose reconstructions and plan adaptation decisions. Image quality deficiencies though, hamper dose calculation accuracy and make corrections of CBCTs a necessity. This study compared three methods to correct CBCTs and create synthetic CTs that are suitable for proton dose calculations. CBCTs, planning CTs and repeated CTs (rCT) from 33 H&N cancer patients were used to compare a deep convolutional neural network (DCNN), deformable image registration (DIR) and an analytical image-based correction method (AIC) for synthetic CT (sCT) generation. Image quality of sCTs was evaluated by comparison with a same-day rCT, using mean absolute error (MAE), mean error (ME), Dice similarity coefficient (DSC), structural non-uniformity (SNU) and signal/contrast-to-noise ratios (SNR/CNR) as metrics. Dosimetric accuracy was investigated in an intracranial setting by performing gamma analysis and calculating range shifts. Neural network-based sCTs resulted in the lowest MAE and ME (37/2 HU) and the highest DSC (0.96). While DIR and AIC generated images with a MAE of 44/77 HU, a ME of −8/1 HU and a DSC of 0.94/0.90. Gamma and range shift analysis showed almost no dosimetric difference between DCNN and DIR based sCTs. The lower image quality of AIC based sCTs affected dosimetric accuracy and resulted in lower pass ratios and higher range shifts. Patient-specific differences highlighted the advantages and disadvantages of each method. For the set of patients, the DCNN created synthetic CTs with the highest image quality. Accurate proton dose calculations were achieved by both DCNN and DIR based sCTs. The AIC method resulted in lower image quality and dose calculation accuracy was reduced compared to the other methods.

Technical Note: First report on an in vivo range probing quality control procedure for scanned proton beam therapy in head and neck cancer patients

“…CBCTs suffer from the large uncertainty of the Hounsfield Units (HU), which makes them unsuitable for proton dose calculation. Promising results have been shown on performance of neural networks (NN) in generating synthetic CTs based on daily CBCTs, 28 rendering synthetic images suitable for dose calculation. Nevertheless, to some extent NN may be considered as a “black box”.…”

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

“…Furthermore, mean RREs and standard deviations based on rCTs and sCTs are consistent, with a difference in mean RREs of about -1% and standard deviations that lay mostly within the ±3% boundaries. 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 .…”

Online adaptive dose restoration in intensity modulated proton therapy of lung cancer to account for inter-fractional density changes

“…in-room CT on rails), systematic setup errors can be mostly mitigated, allowing DR to deal primarily with the range errors. However, on-board CBCT could also be considered, but this requires advanced image quantification features in order to use CBCT information for dose computation [44,45,46]. Dose differences between not adapted and restored plans were statistically significant (p < 0.05) in all dose regions, the target volume and the spinal cord.…”