https://ogma.newcastle.edu.au/vital/access/ /manager/Index ${session.getAttribute("locale")} 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:1841 Wed 11 Apr 2018 17:21:51 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3152 Wed 11 Apr 2018 15:29:57 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3151 Wed 11 Apr 2018 14:33:52 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3149 Wed 11 Apr 2018 13:42:46 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:1528 Wed 11 Apr 2018 11:45:08 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3284 Wed 11 Apr 2018 10:12:01 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:3153 Wed 11 Apr 2018 09:45:19 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24855 Wed 02 Mar 2022 14:24:57 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35115 Tue 23 Jun 2020 13:04:43 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:38241 Tue 17 Aug 2021 11:02:53 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:42887 cut(kinetic) was set at 500, 200, and 10 keV, and differences between 1 and 3 mm voxel were calculated. A planning study with 10 patient stereotactic body radiotherapy (SBRT) spine plans and 10 patient H&N plans was calculated in both Acuros XB (AXB) v15.6.06 and Anisotropic Analytical Algorithm (AAA) v15.6.06. The patient contour was overridden to water as only the penumbral differences between the two different algorithms were under investigation. Results: The dosimetry audit results show that for the SBRT spine case, plans calculated in AXB are colder than what is measured in the spinal cord by 5%-10%. This was also observed for other audit cases where a C-shape target is wrapped around an OAR where the plans were colder by 3%-10%. Plans calculated with Monaco MC were colder than measurements by approximately 7% with the OAR surround by a C-shape target, but these differences were not noted in the SBRT spine case. Results from the clinical patient plans showed that the AXB was on average 7.4% colder than AAA when comparing the minimum dose in the spinal cord OAR. This average difference between AXB and AAA reduced to 4.5% when using the more clinically relevant metric of maximum dose in the spinal cord. For the H&N plans, AXB was cooler on average than AAA in the spinal cord OAR (1.1%), left parotid (1.7%), and right parotid (2.3%). The EGSnrc investigation also noted similar, but smaller differences. The beam penumbra modeled by E_cut(kinetic) = 500 keV was steeper than the beam penumbra modeled by E_cut(kinetic) = 10 keV as the full scatter is not accounted for, which resulted in less dose being calculated in a central OAR region where the penumbra contributes much of the dose. The dose difference when using 2.5 mm voxels of the center of the OAR between 500 and 10 keV was 3%, reducing to 1% between 200 and 10 keV. Conclusions: Lack of full penumbral modeling due to approximations in the algorithms in MC based or LBTE algorithms are a contributing factor as to why these algorithms under-predict the dose to OAR when the treatment volume is wrapped around the OAR. The penumbra modeling approximations also contribute to AXB plans predicting colder doses than AAA in areas that are in the vicinity of beam penumbra. This effect is magnified in regions where there are many beam penumbras, for example in the spinal cord for spine SBRT cases.]]> Tue 06 Sep 2022 13:57:20 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:54008 Thu 25 Jan 2024 13:53:46 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:48039 Error). For simulated field size errors, the RMS_Error was 0.47 cm² and field shape changes were detected for random errors with standard deviation ≥ 2.5 mm. For clinical lung SABR deliveries, field location errors of 1.6 mm (parallel MLC motion) and 4.9 mm (perpendicular) were measured (expressed as a full-width-half-maximum). The mean and standard deviation of the errors in field size and shape were 0.0 ± 0.3 cm² and 0.3 ± 0.1 (expressed as a translation-invariant normalized RMS). No correlation was observed between geometric errors during each treatment fraction and dosimetric errors in the reconstructed dose to the target volume for this cohort of patients. Conclusion: A system for real-time delivery verification has been developed for MLC tracking using time-resolved EPID imaging. The technique has been tested offline in phantom-based deliveries and clinical patient deliveries and was used to independently verify the geometric accuracy of the MLC during MLC tracking radiotherapy.]]> Thu 23 Mar 2023 10:25:03 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:29967 Thu 03 Feb 2022 12:22:05 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7400 Sat 24 Mar 2018 11:14:02 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7816 Sat 24 Mar 2018 10:46:05 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7062 Sat 24 Mar 2018 08:38:01 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7064 Sat 24 Mar 2018 08:37:58 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7068 Sat 24 Mar 2018 08:37:56 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:7127 10% of maximum, in-field signal). Using the continuous acquisition mode, the EPID response was not linear with dose. It was found that the continuous mode dose response corresponded approximately to dropping one image per acquisition session. Reproducibility of EPID response to low monitor units (MUs) was found to be poor but greatly improved with increasing MU. Open field profiles were found to be stable in the cross-plane direction but required several frames to become stable in the in-plane direction. However, both of these issues are clinically insignificant due to arc-IMRT deliveries requiring relatively large monitor units (>100 MU). Analysis of the five IMRT, arc, and arc-IMRT tests revealed that all examples compared to within 2% of maximum dose for more than 95% of in-field pixels. The continuous acquisition mode is suited to time-resolved dosimetry applications including arc-IMRT and dynamic IMRT, giving comparable dose results to the well-studied integrated acquisition mode, although caution should be used in low MU applications. Time-resolved EPID dose information also compared well to time-resolved ion-chamber measurements.]]> Sat 24 Mar 2018 08:34:10 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:14366 Sat 24 Mar 2018 08:23:11 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:11207 Sat 24 Mar 2018 08:12:45 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:11002 Sat 24 Mar 2018 08:12:40 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18230 Sat 24 Mar 2018 08:04:51 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:17704 Sat 24 Mar 2018 07:57:30 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:17977 Sat 24 Mar 2018 07:56:42 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:16972 Sat 24 Mar 2018 07:55:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:5040 Sat 24 Mar 2018 07:45:40 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:5384 Sat 24 Mar 2018 07:43:54 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:26271 20% of prescription, excluding PTV and skin build-up region). Conclusions: An algorithm to reconstruct delivered patient 3D doses from EPID exit dosimetry measurements was presented. The method was applied to phantom and patient data sets, as well as for dynamic IMRT and VMAT delivery techniques. Results indicate that the EPID dose reconstruction algorithm presented in this work is suitable for clinical implementation.]]> Sat 24 Mar 2018 07:40:16 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:24849 0.6 while all haematuria endpoints and longitudinal incontinence models produced AUROC<0.6. Conclusions: Logistic regression and MARS were most likely to be the best-performing strategy for the prediction of urinary symptoms with elastic-net and random forest producing competitive results. The predictive power of the models was modest and endpoint-dependent. New features, including spatial dose maps, may be necessary to achieve better models.]]> Sat 24 Mar 2018 07:11:24 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43095 Mon 29 Jan 2024 17:50:50 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43582 99mTc-carbon (‘Technegas’), a clinical V-SPECT modality featuring smaller radioaerosol particles with less clumping. Methods: Eleven lung cancer radiotherapy patients with early stage (T1/T2N0) disease received treatment planning four-dimensional CT (4DCT) scans paired with Technegas V/Q-SPECT/CT. For each patient, we applied three different CTVI methods. Two of these used deformable image registration (DIR) to quantify breathing-induced lung density changes (CTVI_DIR-HU), or breathing-induced lung volume changes (CTVI_DIR-Jac) between the 4DCT exhale/inhale phases. A third method calculated the regional product of air-tissue densities (CTVI_HU) and did not involve DIR. Corresponding CTVI and V-SPECT scans were compared using the Dice similarity coefficient (DSC) for functional defect and nondefect regions, as well as the Spearman's correlation r computed over the whole lung. The DIR target registration error (TRE) was quantified using both manual and computer-selected anatomic landmarks. Results: Interestingly, the overall best performing method (CTVI_HU) did not involve DIR. For nondefect regions, the CTVI_HU, CTVI_DIR-HU, and CTVI_DIR-Jac methods achieved mean DSC values of 0.69, 0.68, and 0.54, respectively. For defect regions, the respective DSC values were moderate: 0.39, 0.33, and 0.44. The Spearman r-values were generally weak: 0.26 for CTVI_HU, 0.18 for CTVI_DIR-HU, and −0.02 for CTVI_DIR-Jac. The spatial accuracy of CTVI was not significantly correlated with TRE, however the DIR accuracy itself was poor with TRE > 3.6 mm on average, potentially indicative of poor quality 4DCT. Q-SPECT scans achieved good correlations with V-SPECT (mean r > 0.6), suggesting that the image quality of Technegas V-SPECT was not a limiting factor in this study. Conclusions: We performed a validation of CTVI using clinically available 4DCT and Technegas V/Q-SPECT for 11 lung cancer patients. The results reinforce earlier findings that the spatial accuracy of CTVI exhibits significant interpatient and intermethod variability. We propose that the most likely factor affecting CTVI accuracy was poor image quality of clinical 4DCT.]]> Mon 26 Sep 2022 10:33:32 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:43579 4DCBCT) can complement existing 4DCT-based methods (CTVI^4DCT) to track lung function changes over a course of lung cancer radiation therapy. However, the accuracy of CTVI^4DCBCT needs to be assessed since anatomic 4DCBCT has demonstrably poor image quality and small field of view (FOV) compared to treatment planning 4DCT. We perform a direct comparison between short interval CTVI^4DCBCT and CTVI^4DCT pairs to understand the patient specific image quality factors affecting the intermodality CTVI reproducibility in the clinic. Methods and materials: We analysed 51 pairs of 4DCBCT and 4DCT scans acquired within 1 day of each other for nine lung cancer patients. To assess the impact of image quality, CTVIs were derived from 4DCBCT scans reconstructed using both standard Feldkamp-Davis-Kress backprojection (CTVI4DCBCT/FDK) and an iterative McKinnon-Bates Simultaneous Algebraic Reconstruction Technique (CTVI4DCBCT/MKBSART). Also, the influence of FOV was assessed by deriving CTVIs from 4DCT scans that were cropped to a similar FOV as the 4DCBCT scans (CTVI4DCT/crop), or uncropped (CTVI4DCT/uncrop). All CTVIs were derived by performing deformable image registration (DIR) between the exhale and inhale phases and evaluating the Jacobian determinant of deformation. Reproducibility between corresponding CTVI^4DCBCT and CTVI^4DCT pairs was quantified using the voxel-wise Spearman rank correlation and the Dice similarity coefficient (DSC) for ventilation defect regions (identified as the lower quartile of ventilation values). Mann–Whitney U-tests were applied to determine statistical significance of each reconstruction and cropping condition. Results: The (mean ± SD) Spearman correlation between CTVIRDCBCT/FDK and CTVI4DCT/uncrop was 0.60 ± 0.23 (range −0.03–0.88) and the DSC was 0.64 ± 0.12 (0.34–0.83). By comparison, correlations between CTVI4DCBCT/MKBSART and CTVI4DCTuncrop showed a small but statistically significant improvement with = 0.64 ± 0.20 (range 0.06–0.90, P = 0.03) and DSC = 0.66 ± 0.13 (0.31–0.87, P = 0.02). Intermodal correlations were noted to decrease with an increasing fraction of lung truncation in 4DCBCT relative to 4DCT, albeit not significantly (Pearson correlation R = 0.58, P = 0.002). Conclusions: This study demonstrates that DIR based CTVIs derived from 4DCBCT can exhibit reasonable to good voxel-level agreement with CTVIs derived from 4DCT. These correlations outperform previous cross-modality comparisons between 4DCT-based ventilation and nuclear medicine. The use of 4DCBCT scans with iterative reconstruction and minimal lung truncation is recommended to ensure better reproducibility between 4DCBCT- and 4DCT-based CTVIs.]]> Mon 26 Sep 2022 10:30:30 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:47483 Mon 23 Jan 2023 11:47:59 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:33870 Mon 21 Jan 2019 10:42:48 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:35819 Mon 09 Dec 2019 15:43:23 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19934 Fri 20 Jul 2018 15:10:00 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21588 Fri 20 Jul 2018 15:08:55 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19690 Fri 20 Jul 2018 15:04:41 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:21566 Fri 10 Nov 2023 16:00:54 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19879 10% maximum field dose) for each case, over all fields. Conclusions: This work presents the first validation of the integration of a comprehensive fluence model with a patient and EPID radiation transport model that accounts for patient transmission, including complex factors such as patient scatter and the energy response of the a-Si detector. The portal dose image prediction model satisfies the 3% and 3 mm criteria for IMRT fields delivered to slab phantoms and could be used for patient treatment verification.]]> Fri 10 Nov 2023 15:59:41 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19880 Fri 10 Nov 2023 15:59:12 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19933 Fri 10 Nov 2023 15:58:39 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:18991 Fri 10 Nov 2023 15:57:38 AEDT ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:19932 Fri 10 Nov 2023 15:57:09 AEDT ]]>