Dosimetric evaluation of cone‐beam CT‐based synthetic CTs in pediatric patients undergoing intensity‐modulated proton therapy

Abstract

To evaluate dosimetric changes detected using synthetic computed tomography (sCT) derived from online cone-beam CTs (CBCT) in pediatric patients treated using intensity-modulated proton therapy (IMPT). Methods: Ten pediatric patients undergoing IMPT and aligned daily using proton gantry-mounted CBCT were identified for retrospective analysis with treated anatomical sites fully encompassed in the CBCT field of view. Dates were identified when the patient received both a CBCT and a quality assurance CT (qCT) for routine dosimetric evaluation. sCTs were generated based on a deformable registration between the initial plan CT (pCT) and CBCT. The clinical IMPT plans were re-computed on the same day qCT and sCT, and dosimetric changes due to tissue change or response from the initial plan were computed using each image. Linear regression analysis was performed to determine the correlation between dosimetric changes detected using the qCT and the sCT. Gamma analysis was also used to compare the dose distributions computed on the qCT and sCT. Results:The correlation coefficients (p-values) between qCTs and sCTs for changes detected in target coverage, overall maximum dose, and organ at risk dose were 0.97 (< .001), 0.84 (.002) and 0.91 (< .001), respectively. Mean ± SD gamma pass rates of the sCT-based dose compared to the qCT-based dose at 3%/3 mm, 3%/2 mm, and 2%/2 mm criteria were 96.5%±4.5%, 93.2%±6.3%, and 91.3%±7.8%, respectively. Pass rates tended to be lower for targets near lung. Conclusion:While insufficient for re-planning, sCTs provide approximate dosimetry without administering additional imaging dose in pediatric patients undergoing IMPT. Dosimetric changes detected using sCTs are correlated with changes detected using clinically-standard qCTs; however, residual differences in dosimetry remain a limitation. Further improvements in sCT image quality may both improve online dosimetric evaluation and reduce imaging dose for pediatric patients by reducing the need for routine qCTs.

Generation and evaluation of anatomy‐preserving virtual CT for online adaptive proton therapy

“…To the best of our knowledge, all presently available studies on CBCT corrections either recalculated the pCT plan on the generated daily CTs and re‐CTs or created a new plan on re‐CT and further recalculated it on the generated daily CTs 6,8,32,33 . Such methods of recalculating plans on daily CTs may not have a substantial impact on the dose distribution if neighboring ROIs have HU values that are not considerably different.…”

“…To the best of our knowledge, all presently available studies on CBCT corrections either recalculated the pCT plan on the generated daily CTs and re-CTs or created a new plan on re-CT and further recalculated it on the generated daily CTs. 6,8,32,33 Such methods of recalculating plans on daily CTs may not have a substantial impact on the dose distribution if neighboring ROIs have HU values that are not considerably different. This has been shown by Kurz et al 8 that despite a satisfactory agreement in terms of proton range and GPR analysis, detailed visual inspection and contour analysis of OARs found that the vCT is insufficiently accurate for prostate cancer patients.…”

Clinical evaluation of synthetic computed tomography methods in adaptive proton therapy of lung cancer patients

“…There, higher gamma pass rates were seen which was expected due to the higher sensitivity of dose distributions in proton therapy compared to photon therapy. DIR has also been used in an in-house built sCT method tested on paediatric proton therapy patients [25] . Deep-learning has recently also attracted attention for sCT generation [13] , [26] , [27] , e.g.…”

Comparison and evaluation of different deep learning models of synthetic CT generation from CBCT for nasopharynx cancer adaptive proton therapy

“…28 Several evaluations have been conducted on the use of sCT in adaptive proton radiotherapy, with most of these studies utilizing deep convolutional neural network (DCNN) and cycleGAN to generate sCT. [29][30][31][32] In this study, we aim to assess the suitability of different deep learning models, including the newly proposed cGAN, Unet+cycleGAN, and the conventional Unet and cycleGAN models, for generating sCT in NPC. By analyzing their unique structures and characteristics,we can determine which model is more appropriate for sCT generation and holds potential for further clinical exploration in adaptive proton therapy.…”

“…Several evaluations have been conducted on the use of sCT in adaptive proton radiotherapy, with most of these studies utilizing deep convolutional neural network (DCNN) and cycleGAN to generate sCT 29–32 . In this study, we aim to assess the suitability of different deep learning models, including the newly proposed cGAN, Unet+cycleGAN, and the conventional Unet and cycleGAN models, for generating sCT in NPC.…”