Objectives: When iodinated contrast is administered during CT simulation, standard practice requires a separate non-contrast CT for dose calculation. The objective of this study is to validate our hypothesis that since iodine affects Hounsfield units (HUs) more than electron density (ED), the information from post-contrast dual-layer CT (DLCT) would be sufficient for accurate dose calculation for both photon and proton therapy. Methods and materials: 10 pediatric patients with abdominal tumors underwent DLCT scans before and after iodinated contrast administration for radiotherapy planning. Dose distributions with these DLCT-based methods were compared to those with conventional calibration-curve methods that map HU images to ED and stopping-power ratio (SPR) images. Results: For photon plans, conventional and DLCT approaches based on post-contrast scans underestimated the PTV D99 by 0.87 ± 0.70% (p = 0.18) and 0.36 ± 0.31% (p = 0.34), respectively, comparing to their non-contrast optimization plans. Renal iodine concentration was weakly associated with D99 deviation for both conventional (R2 = 0.10) and DLCT (R2 = 0.02) approaches. For proton plans, the clinical target volume D99 errors were 3.67 ± 2.43% (p = 0.0001) and 0.30 ± 0.25% (p = 0.40) for conventional and DLCT approaches, respectively. The proton beam range changed noticeably with the conventional approach. Renal iodine concentration was highly associated with D99 deviation for the conventional approach (R2 = 0.83) but not for DLCT (R2 = 0.007). Conclusion: Conventional CT with iodine contrast resulted in a large dosimetric error for proton therapy, compared to true non-contrast plans, but the error was less for photon therapy. These errors can be greatly reduced in the case of the proton plans if DLCT is used, raising the possibility of using only a single post-contrast CT for radiotherapy dose calculation, thus reducing the time and imaging dose required. Advances in knowledge: This study is the first to compare directly the differences in the calculated dose distributions between pre- and post-contrast CT images generated by single-energy CT and dual-energy CT methods for photon and proton therapy.

Purpose/Objective(s): To describe a log file–based patient‐specific quality assurance (QA) method and develop an in‐house tool for system performance tracking and dose reconstruction in pencil‐beam scanning proton therapy that can be used for pre‐treatment plan review.Materials/Methods: The software extracts beam‐specific information from the treatment delivery log file and automatically compares the monitor units (MU), lateral position, and size of each spot against the intended values in the treatment plan to identify any discrepancies in the beam delivery. The software has been used to analyze 992 patients, 2004 plans, 4865 fields, and more than 32 million proton spots from 2016 to 2021. The composite doses of 10 craniospinal irradiation (CSI) plans were reconstructed based on the delivered spots and compared with the original plans as an offline plan review method.Results: Over the course of 6 years, the proton delivery system has proved stable in delivering patient QA fields with proton energies of 69.4–221.3 MeV and an MU range of 0.003–1.473 MU per spot. The planned mean and standard deviation (SD) of the energy and spot MU were 114.4 ± 26.4 MeV and 0.010 ± 0.009 MU, respectively. The mean and SD of the differences in MU and position between the delivered and planned spots were 9.56 × 10⁻⁸ ± 2.0 × 10⁻⁴ MU and 0.029/−0.007 ± 0.049/0.044 mm on the X/Y‐axis for random differences and 0.005/0.125 ± 0.189/0.175 mm on the X/Y‐axis for systematic differences. The mean and SD of the difference between the commissioning and delivered spot sizes were 0.086/0.089 ± 0.131/0.166 mm on the X/Y‐axis.Conclusion: A tool has been developed to extract crucial information about the performance of the proton delivery and monitor system and provide a dose reconstruction based on delivered spots for quality improvement. Each patient's plan was verified before treatment to ensure accurate and safe delivery within the delivery tolerance of the machine.

Objective . This study aimed to develop a novel method for generating synthetic CT (sCT) from cone-beam CT (CBCT) of the abdomen/pelvis with bowel gas pockets to facilitate estimation of proton ranges. Approach . CBCT, the same-day repeat CT, and the planning CT (pCT) of 81 pediatric patients were used for training ( n = 60), validation ( n = 6), and testing ( n = 15) of the method. The proposed method hybridizes unsupervised deep learning (CycleGAN) and deformable image registration (DIR) of the pCT to CBCT. The CycleGAN and DIR are respectively applied to generate the geometry-weighted (high spatial-frequency) and intensity-weighted (low spatial-frequency) components of the sCT, thereby each process deals with only the component weighted toward its strength. The resultant sCT is further improved in bowel gas regions and other tissues by iteratively feeding back the sCT to adjust incorrect DIR and by increasing the contribution of the deformed pCT in regions of accurate DIR. Main results . The hybrid sCT was more accurate than deformed pCT and CycleGAN-only sCT as indicated by the smaller mean absolute error in CT numbers (28.7 ± 7.1 HU versus 38.8 ± 19.9 HU/53.2 ± 5.5 HU; P ≤ 0.012) and higher Dice similarity of the internal gas regions (0.722 ± 0.088 versus 0.180 ± 0.098/0.659 ± 0.129; P ≤ 0.002). Accordingly, the hybrid method resulted in more accurate proton range for the beams intersecting gas pockets (11 fields in 6 patients) than the individual methods (the 90th percentile error in 80% distal fall-off, 1.8 ± 0.6 mm versus 6.5 ± 7.8 mm/3.7 ± 1.5 mm; P ≤ 0.013). The gamma passing rates also showed a significant dosimetric advantage by the hybrid method (99.7 ± 0.8% versus 98.4 ± 3.1%/98.3 ± 1.8%; P ≤ 0.007). Significance . The hybrid method significantly improved the accuracy of sCT and showed promises in CBCT-based proton range verification and adaptive replanning of abdominal/pelvic proton therapy even when gas pockets are present in the beam path.

Background: As auto‐segmentation tools become integral to radiotherapy, more commercial products emerge. However, they may not always suit our needs. One notable example is the use of adult‐trained commercial software for the contouring of organs at risk (OARs) of pediatric patients. Purpose: This study aimed to compare three auto‐segmentation approaches in the context of pediatric craniospinal irradiation (CSI): commercial, out‐of‐the‐box, and in‐house. Methods: CT scans from 142 pediatric patients undergoing CSI were obtained from St. Jude Children's Research Hospital (training: 115; validation: 27). A test dataset comprising 16 CT scans was collected from the McGill University Health Centre. All images underwent manual delineation of 18 OARs. LimbusAI v1.7 served as the commercial product, while nnU‐Net was trained for benchmarking. Additionally, a two‐step in‐house approach was pursued where smaller 3D CT scans containing the OAR of interest were first recovered and then used as input to train organ‐specific models. Three variants of the U‐Net architecture were explored: a basic U‐Net, an attention U‐Net, and a 2.5D U‐Net. The dice similarity coefficient (DSC) assessed segmentation accuracy, and the DSC trend with age was investigated (Mann–Kendall test). A radiation oncologist determined the clinical acceptability of all contours using a five‐point Likert scale. Results: Differences in the contours between the validation and test datasets reflected the distinct institutional standards. The lungs and left kidney displayed an increasing age‐related trend of the DSC values with LimbusAI on the validation and test datasets. LimbusAI contours of the esophagus were often truncated distally and mistaken for the trachea for younger patients, resulting in a DSC score of less than 0.5 on both datasets. Additionally, the kidneys frequently exhibited false negatives, leading to mean DSC values that were up to 0.11 lower on the validation set and 0.07 on the test set compared to the other models. Overall, nnU‐Net achieved good performance for body organs but exhibited difficulty differentiating the laterality of head structures, resulting in a large variation of DSC values with the standard deviation reaching 0.35 for the lenses. All in‐house models generally had similar DSC values when compared against each other and nnU‐Net. Inference time on the test data was between 47–55 min on a Central Processing Unit (CPU) for the in‐house models, while it was 1h 21m with a V100 Graphics Processing Unit (GPU) for nnU‐Net. Conclusions: LimbusAI could not adapt well to pediatric anatomy for the esophagus and the kidneys. When commercial products do not suit the study population, the nnU‐Net is a viable option but requires adjustments. In resource‐constrained settings, the in‐house model provides an alternative. Implementing an automated segmentation tool requires careful monitoring and quality assurance regardless of the approach.