CBCT-DRRs superior to CT-DRRs for target-tracking applications for pancreatic SBRT.

Objective.In current radiograph-based intra-fraction markerless target-tracking, digitally reconstructed radiographs (DRRs) from planning CTs (CT-DRRs) are often used to train deep learning models that extract information from the intra-fraction radiographs acquired during treatment. Traditional DRR algorithms were designed for patient alignment (i.e.bone matching) and may not replicate the radiographic image quality of intra-fraction radiographs at treatment. Hypothetically, generating DRRs from pre-treatment Cone-Beam CTs (CBCT-DRRs) with DRR algorithms incorporating physical modelling of on-board-imagers (OBIs) could improve the similarity between intra-fraction radiographs and DRRs by eliminating inter-fraction variation and reducing image-quality mismatches between radiographs and DRRs. In this study, we test the two hypotheses that intra-fraction radiographs are more similar to CBCT-DRRs than CT-DRRs, and that intra-fraction radiographs are more similar to DRRs from algorithms incorporating physical models of OBI components than DRRs from algorithms omitting these models.Approach.DRRs were generated from CBCT and CT image sets collected from 20 patients undergoing pancreas stereotactic body radiotherapy. CBCT-DRRs and CT-DRRs were generated replicating the treatment position of patients and the OBI geometry during intra-fraction radiograph acquisition. To investigate whether the modelling of physical OBI components influenced radiograph-DRR similarity, four DRR algorithms were applied for the generation of CBCT-DRRs and CT-DRRs, incorporating and omitting different combinations of OBI component models. The four DRR algorithms were: a traditional DRR algorithm, a DRR algorithm with source-spectrum modelling, a DRR algorithm with source-spectrum and detector modelling, and a DRR algorithm with source-spectrum, detector and patient material modelling. Similarity between radiographs and matched DRRs was quantified using Pearson's correlation and Czekanowski's index, calculated on a per-image basis. Distributions of correlations and indexes were compared to test each of the hypotheses. Distribution differences were determined to be statistically significant when Wilcoxon's signed rank test and the Kolmogorov-Smirnov two sample test returnedp≤ 0.05 for both tests.Main results.Intra-fraction radiographs were more similar to CBCT-DRRs than CT-DRRs for both metrics across all algorithms, with allp≤ 0.007. Source-spectrum modelling improved radiograph-DRR similarity for both metrics, with allp< 10-6. OBI detector modelling and patient material modelling did not influence radiograph-DRR similarity for either metric.Significance.Generating DRRs from pre-treatment CBCT-DRRs is feasible, and incorporating CBCT-DRRs into markerless target-tracking methods may promote improved target-tracking accuracies. Incorporating source-spectrum modelling into a treatment planning system's DRR algorithms may reinforce the safe treatment of cancer patients by aiding in patient alignment.

Radiological evaluation of an iodised hydrogel for prostate radiotherapy applications.

PURPOSE: Physical separation of healthy tissue and target volumes in prostate radiotherapy through the insertion of hydrogel can improve patient toxicity rates. An iodised hydrogel may provide anatomical separation of prostate and rectum while being easily visualised through radio-opacity. The aim of this study was to characterise SpaceOAR Vue™ in kilovoltage (kV) images and megavoltage (MV) radiotherapy treatment planning. METHODS: Two cassettes were 3D-printed, one filled with water and the other with SpaceOAR Vue™. Transmission dose through each cassette was measured in slab phantom geometry and compared for 6MV and 10MV photon energies. The SpaceOAR Vue™ slab phantom setup was simulated using computed tomography (CT) and a treatment plan created. The plan was calculated with the hydrogel segmented and material assignment set to water, and the resultant dose compared to corresponding measurement doses. The first 5 patients treated with SpaceOAR Vue™ were assessed with the volume and Hounsfield units (HU) of the hydrogel evaluated in CT and cone beam computed tomography (CBCT) imaging. RESULTS: Transmission through Water and SpaceOAR Vue™ agreed to within 0.5% for both photon energies. Furthermore, the segmentation of SpaceOAR Vue™ and material assignment to water, resulted in a plan dose that agreed to measurement to within 0.5%. Clinically, the SpaceOAR Vue™ volume and HU did not vary over patient treatment course, however was found to display differently on different kV imaging modalities. CONCLUSIONS: SpaceOAR Vue™ was found to be radio-opaque on kV images, but dosimetrically behaved similarly to water in MV treatment beams, making it suitable for clinical use.