Evaluation of the quality of fit of flexible bolus material created using 3D printing technology.

AIMS: To retrospectively evaluate the quality of fit of 3D printed bolus over four different treatment sites to determine whether certain sites favor a 3D printed approach and if the quality of fit changes over the course of treatment. MATERIALS AND METHODS: A retrospective analysis of the first 60 cases treated using 3D printed bolus in our radiotherapy center was undertaken. All boluses were printed using flexible thermoplastic polyurethane (TPU) material. We developed a system of rating the quality of fit using four quality categories. The analysis of 60 patients consisted of a review of a total 627 treatment fractions for head and neck (H&N), scalp, pelvis, and extremity treatment sites. RESULTS: Out of 627 fractions evaluated, 75.1% were rated either "good" or "excellent", 20.6% were rated as "acceptable" and 4.3% were rated "poor". H&N, scalp, and extremity treatment regions were found to favor a 3D printed approach. However, pelvis cases had a higher proportion of "acceptable" and "poor" ratings. Trend analysis showed no notable change in the quality of 3D printed bolus fit over the course of treatment, except for pelvis cases which tended to change categories more than other treatment sites. CONCLUSION: This evaluation demonstrates that 3D printed bolus, created using semi-flexible materials such as TPU, is an effective and practical bolus choice for radiotherapy. In particular, using a 3D printed approach for H&N, scalp, and extremities was found to have a highly conformal fit.

Efficacy evaluation of retrospectively applying the Varian normal breathing predictive filter for volume definition and artifact reduction in 4D CT lung patients.

Phase-based sorting of four-dimensional computed tomography (4D CT) datasets is prone to image artifacts due to patient's breathing irregularities that occur during the image acquisition. The purpose of this study is to investigate the effect of the Varian normal breathing predictive filter (NBPF) as a retrospective phase-sorting parameter in 4D CT. Ten 4D CT lung cancer datasets were obtained. The volumes of all tumors present, as well as the total lung volume, were calculated on the maximum intensity projection (MIP) images as well as each individual phase image. The NBPF was varied retrospectively within the available range, and changes in volume and image quality were recorded. The patients' breathing trace was analysed and the magnitude and location of any breathing irregularities were correlated to the behavior of the NBPF. The NBPF was found to have a considerable effect on the quality of the images in MIP and single-phase datasets. When used appropriately, the NBPF is shown to have the ability to account for and correct image artifacts. However, when turned off (0%) or set above a critical level (approximately 40%), it resulted in erroneous volume reconstructions with variations in tumor volume up to 26.6%. Those phases associated with peak inspiration were found to be more susceptible to changes in the NBPF. The NBPF settings selected prior to exporting the breathing trace for patients evaluated using 4D CT directly affect the accuracy of the targeting and volume estimation of lung tumors. Recommendations are made to address potential errors in patient anatomy introduced by breathing irregularities, specifically deep breath or cough irregularities, by implementing the proper settings and use of this tool.

Evaluation of a novel phantom for the quality assurance of a six-degree-of-freedom couch 3D-printed at multiple centres.

This study aimed to validate a bespoke 3D-printed phantom for use in quality assurance (QA) of a 6 degrees-of-freedom (6DoF) treatment couch. A novel phantom design comprising a main body with internal cube structures, was fabricated at five centres using Polylactic Acid (PLA) material, with an additional phantom produced incorporating a PLA-stone hybrid material. Correctional setup shifts were determined using image registration by 3D-3D matching of high HU cube structures between obtained cone-beam computer tomography (CBCT) images to reference CTs, containing cubes with fabricated rotational offsets of 3.5°, 1.5° and -2.5° in rotation, pitch, and roll, respectively. Average rotational setup shifts were obtained for each phantom. The reproducibility of 3D-printing was probed by comparing the internal cube size as well as Hounsfield Units between each of the uniquely produced phantoms. For the five PLA phantoms, the average rot, pitch and roll correctional differences from the fabricated offsets were -0.3 ± 0.2°, -0.2 ± 0.5° and 0.2 ± 0.3° respectively, and for the PLA hybrid these differences were -0.09 ± 0.14°, 0.30 ± 0.00° and 0.03 ± 0.10°. There was found to be no statistically significant difference in average cube size between the five PLA printed phantoms, with the significant difference (P < 0.05) in HU of one phantom compared to the others attributed to setup choice and material density. This work demonstrated the capability producing a novel 3D-printed 6DoF couch QA phantom design, at multiple centres, with each unique model capable of sub-degree couch correction.

Dosimetry assessment of patient-specific 3D printable materials for HDR surface brachytherapy.

PURPOSE: 3D printable material water equivalence was investigated within the range of Iridium-192 source energies. The aim is to compare the dose calculated by our treatment planning system (TPS) with the dose measured in the presence of printed materials. The purpose of this investigation is to assess the feasibility of using 3D materials for brachytherapy surface applicators. METHODS AND MATERIALS: Cheetah was examined both in a water tank and with the CIRS anthropomorphic phantom. Calibrated Gafchromic EBT3-V3 film was used and the measurements compared with TG-43 calculations on Oncentra®Brachy. A 3D-printed slab 5 mm thick was created to position the source and two films were irradiated at 5 mm and 15 mm of distance. A curved mould with 7 trajectories was created and coupled with CIRS phantom. A set of CT images of phantom and mould was acquired and imported on TPS, where a target was defined and a dose plan was created. Plan was delivered with two films positioned between two different slabs of phantom, at reciprocal distance of 2 cm, orientated perpendicularly to the source axis. RESULTS: All PDDs show a maximum difference of 4.7% (average 2.2%). At 5 mm and at 15 mm, the gamma pass rate is 100% with tolerance 2%/1 mm DTA. Results of films placed intra-slabs show a high pass rate (>99%) with tolerances of 2% dose and 1 mm DTA. CONCLUSION: 3D material investigated is water equivalent at Ir-192 energies and agreed with Oncentra®Brachy dose calculations which suggest that it is a suitable material for superficial brachytherapy.

Using a neural network to predict deviations in mean heart dose during the treatment of left-sided deep inspiration breath hold patients.

PURPOSE: We investigated if a neural network could be used to predict the change in mean heart dose when a patient's heart deviates from its planned position during radiotherapy treatment. METHODS: Predictions were made based on parameters available at the time of treatment planning. The dose prescription, deep inspiration breath-hold (DIBH) amplitude, heart volume, lung volume, V90% and mean heart dose were used to predict the increase in dose to the heart when a shift towards the treatment field was undertaken. The network was trained using 3 mm, 5 mm and 7 mm shifts in heart positions for 50 patients' giving 150 data points in total. The neural network architecture was also varied to find the most optimal network design. The final neural network was then tested using cross-validation to evaluate the model's ability to generalise to new data. RESULTS: The optimal neural network found was comprised of a single hidden layer of 30 neurons. Based on twenty train/test splits, 94% of all prediction errors were below 0.2 Gy, 97.3% were below 0.3 Gy and 100% were below 0.5 Gy. The average RMSE and maximum prediction error over all train/test splits were 0.13 Gy and 0.5 Gy respectively. CONCLUSIONS: Our approach using a neural network provides a clinically acceptable estimate of the increase in Mean Heart Dose (MHD), without the need for further imaging, contouring or evaluation. The trained neural network gives clinicians the information and tools required to evaluate what shift in heart position would be acceptable and which scenarios require immediate action before treatment continues.