The purpose of this study was to develop a straightforward method of supplementing patient anatomy and estimating out-of-field absorbed dose for a cohort of pediatric radiotherapy patients with limited recorded anatomy. A cohort of nine children, aged 2-14 years, who received 3D conformal radiotherapy for low-grade localized brain tumors (LBTs), were randomly selected for this study. The extent of these patients' computed tomography simulation image sets were cranial only. To approximate their missing anatomy, we supplemented the LBT patients' image sets with computed tomography images of patients in a previous study with larger extents of matched sex, height, and mass and for whom contours of organs at risk for radiogenic cancer had already been delineated. Rigid fusion was performed between the LBT patients' data and that of the supplemental computational phantoms using commercial software and in-house codes. In-field dose was calculated with a clinically commissioned treatment planning system, and out-of-field dose was estimated with a previously developed analytical model that was re-fit with parameters based on new measurements for intracranial radiotherapy. Mean doses greater than 1 Gy were found in the red bone marrow, remainder, thyroid, and skin of the patients in this study. Mean organ doses between 150 mGy and 1 Gy were observed in the breast tissue of the girls and lungs of all patients. Distant organs, i.e. prostate, bladder, uterus, and colon, received mean organ doses less than 150 mGy. The mean organ doses of the younger, smaller LBT patients (0-4 years old) were a factor of 2.4 greater than those of the older, larger patients (8-12 years old). Our findings demonstrated the feasibility of a straightforward method of applying supplemental computational phantoms and dose-calculation models to estimate absorbed dose for a set of children of various ages who received radiotherapy and for whom anatomies were largely missing in their original computed tomography simulations.

The purpose of this study was to independently apply an analytical model for equivalent dose from neutrons produced in a passive-scattering proton therapy treatment unit, H. To accomplish this objective, we applied the previously-published model to treatment plans of two pediatric patients. Their model accounted for neutrons generated by mono-energetic proton beams stopping in a closed aperture. To implement their model to a clinical setting, we adjusted it to account for the area of a collimating aperture, energy modulation, air gap between the treatment unit and patient, and radiation weighting factor. We used the adjusted model to estimate H per prescribed proton absorbed dose, D , for the passive-scattering proton therapy beams of two children, a 9-year-old girl and 10-year-old boy, who each received intracranial boost fields as part of their treatment. In organs and tissues at risk for radiation-induced subsequent malignant neoplasms, T, we calculated the mass-averaged H, H , per D . Finally, we compared H /D values to those of previously-published Monte Carlo (MC) simulations of these patients' fields. H /D values of the adjusted model deviated from the MC result for each organ on average by 20.8  ±  10.0% and 44.2  ±  17.6% for the girl and boy, respectively. The adjusted model underestimated the MC result in all T of each patient, with the exception of the girl's bladder, for which the adjusted model overestimated H /D by 3.1%. The adjusted model provided a better estimate of H /D than the unadjusted model. That is, between the two models, the adjusted model reduced the deviation from the MC result by approximately 37.0% and 46.7% for the girl and boy, respectively. We found that the previously-published analytical model, combined with adjustment factors to enhance its clinical applicability, predicted H /D in out-of-field organs and tissues at risk for subsequent malignant neoplasms with acceptable accuracy. This independent application demonstrated that the analytical model may be useful broadly for clinicians and researchers to calculate equivalent dose from neutrons produced externally to the patient in passive-scattering proton therapy.

This study developed a computationally efficient and easy-to-implement analytical model to estimate the equivalent dose from secondary neutrons originating in the bodies (‘internal neutrons’) of children receiving intracranial proton radiotherapy. A two-term double-Gaussian mathematical model was fit to previously published internal neutron equivalent dose per therapeutic absorbed dose versus distance from the field edge calculated using Monte Carlo simulations. The model was trained using three intracranial proton fields of a 9-year-old girl. The resulting model was tested against two intracranial fields of a 10-year-old boy by comparing the mean doses in organs at risk of a radiogenic cancer estimated by the model versus those previously calculated by Monte Carlo. On average, the model reproduced the internal neutron organ doses in the 10-year-old boy within 13.5% of the Monte Carlo at 3–10 cm from the field edge and within a factor of 2 of the Monte Carlo at 10–20 cm from the field edge. Beyond 20 cm, the model poorly estimated H/DRx, however, the values were very small, at <0.03 mSv Gy−1.

Purpose The purpose of this study was to evaluate similarities and differences in quality assurance (QA) guidelines for a conventional diagnostic magnetic resonance (MR) system and a MR simulator (MR‐SIM) system used for radiotherapy. Methods In this study, we compared QA testing guidelines from the American College of Radiology (ACR) MR Quality Control (MR QC) Manual to the QA section of the American Association of Physicists in Medicine (AAPM) Task Group 284 report (TG‐284). Differences and similarities were identified in testing scope, frequency, and tolerances. QA testing results from an ACR accredited clinical diagnostic MR system following ACR MR QC instructions were then evaluated using TG‐284 tolerances. Results Five tests from the ACR MR QC Manual were not included in TG‐284. Five new tests were identified for MR‐SIM systems in TG‐284 and pertained exclusively to the external laser positioning system of MR‐SIM systems. “Low‐contrast object detectability” (LCD), “table motion smoothness and accuracy,” “transmitter gain,” and “geometric accuracy” tests differed between the two QA guides. Tighter tolerances were required in TG‐284 for “table motion smoothness and accuracy” and “low contrast object detectability.” “Transmitter gain” tolerance was dependent on initial baseline measurements, and TG‐284 required that geometric accuracy be tested over a larger field of view than the ACR testing method. All tests from the ACR MR QC Manual for a conventional MR system passed ACR tolerances. The T2‐weighted image acquired with ACR sequences failed the 40‐spoke requirement from TG‐284, transmitter gain was at the 5% tolerance of TG‐284, and geometric accuracy could not be evaluated because of required equipment differences. Table motion passed both TG‐284 and ACR required tolerances. Conclusion Our study evaluated QA guidelines for an MR‐SIM and demonstrated the additional QA requirements of a clinical diagnostic MR system to be used as an MR‐SIM in radiotherapy as recommended by TG‐284.