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Title: SU-F-T-302: Implementation of the RadCalc Image Analysis Tool for IMRT QA

Abstract

Purpose: To assess RadCalc as a means for analyzing fluence maps of IMRT QA patient plans. Eclipse generated fluence maps were compared to RadCalc fluence maps generated from both Eclipse parameters and from dynalog trajectory files. Methods: Six IMRT plans consisting of fifty individual fields were compared both field-by-field and as composite plans. These plans were exported from Eclipse to RadCalc. Each plan was then delivered in QA mode on a Varian Clinac iX, while recording and saving the dynalog trajectory files. These files were then imported into RadCalc, providing three sources of generating fluence maps: Eclipse, RadCalc, and dynalog files. Using the image analysis tool in RadCalc, a gamma analysis was then performed as a point-by-point comparison for each IMRT field. The Eclipse fluence map was used as the baseline for each comparison. Results: Based on the manufacturer’s recommendations, all fields were normalized to the maximum pixel value within each Eclipse field. All data was analyzed using a gamma index of 3mm/3% with passing criteria of 90%. For both dynalog analysis and RadCalc fluence analysis, 48/50 created fluence maps passed at greater than 90% when compared with the Eclipse baseline. In analyzing the composite of each of the sixmore » patients’ plans, all plans passed over 90%. Conclusion: Using the dynalog trajectory files in combination with RadCalc Version 6.3 image analysis is a promising metric for verifying IMRT QA passing rates. Notably, if a field failed, it did so for both dynalog and RadCalc compared to Eclipse. This suggests RadCalc can accurately simulate fluence maps, with similar results to dynalog comparisons. Limitations within the RadCalc software were discovered in the analytical process such as pixel resolution and the inability to set minimum threshold values. Comparisons could be extended to include dose map and distance to agreement analysis by expanding software capabilities.« less

Authors:
 [1]; ; ;  [2]
  1. University of Pennsylvania, Philadelphia, PA (United States)
  2. Albert Einstein Medical Center, Philadelphia, PA (United States)
Publication Date:
OSTI Identifier:
22648910
Resource Type:
Journal Article
Resource Relation:
Journal Name: Medical Physics; Journal Volume: 43; Journal Issue: 6; Other Information: (c) 2016 American Association of Physicists in Medicine; Country of input: International Atomic Energy Agency (IAEA)
Country of Publication:
United States
Language:
English
Subject:
60 APPLIED LIFE SCIENCES; 61 RADIATION PROTECTION AND DOSIMETRY; COMPUTER CODES; IMAGE PROCESSING; RADIATION DOSES; RADIOTHERAPY

Citation Formats

Soldner, A, Fitzherbert, C, Zhou, F, and Hand, C. SU-F-T-302: Implementation of the RadCalc Image Analysis Tool for IMRT QA. United States: N. p., 2016. Web. doi:10.1118/1.4956487.
Soldner, A, Fitzherbert, C, Zhou, F, & Hand, C. SU-F-T-302: Implementation of the RadCalc Image Analysis Tool for IMRT QA. United States. doi:10.1118/1.4956487.
Soldner, A, Fitzherbert, C, Zhou, F, and Hand, C. 2016. "SU-F-T-302: Implementation of the RadCalc Image Analysis Tool for IMRT QA". United States. doi:10.1118/1.4956487.
@article{osti_22648910,
title = {SU-F-T-302: Implementation of the RadCalc Image Analysis Tool for IMRT QA},
author = {Soldner, A and Fitzherbert, C and Zhou, F and Hand, C},
abstractNote = {Purpose: To assess RadCalc as a means for analyzing fluence maps of IMRT QA patient plans. Eclipse generated fluence maps were compared to RadCalc fluence maps generated from both Eclipse parameters and from dynalog trajectory files. Methods: Six IMRT plans consisting of fifty individual fields were compared both field-by-field and as composite plans. These plans were exported from Eclipse to RadCalc. Each plan was then delivered in QA mode on a Varian Clinac iX, while recording and saving the dynalog trajectory files. These files were then imported into RadCalc, providing three sources of generating fluence maps: Eclipse, RadCalc, and dynalog files. Using the image analysis tool in RadCalc, a gamma analysis was then performed as a point-by-point comparison for each IMRT field. The Eclipse fluence map was used as the baseline for each comparison. Results: Based on the manufacturer’s recommendations, all fields were normalized to the maximum pixel value within each Eclipse field. All data was analyzed using a gamma index of 3mm/3% with passing criteria of 90%. For both dynalog analysis and RadCalc fluence analysis, 48/50 created fluence maps passed at greater than 90% when compared with the Eclipse baseline. In analyzing the composite of each of the six patients’ plans, all plans passed over 90%. Conclusion: Using the dynalog trajectory files in combination with RadCalc Version 6.3 image analysis is a promising metric for verifying IMRT QA passing rates. Notably, if a field failed, it did so for both dynalog and RadCalc compared to Eclipse. This suggests RadCalc can accurately simulate fluence maps, with similar results to dynalog comparisons. Limitations within the RadCalc software were discovered in the analytical process such as pixel resolution and the inability to set minimum threshold values. Comparisons could be extended to include dose map and distance to agreement analysis by expanding software capabilities.},
doi = {10.1118/1.4956487},
journal = {Medical Physics},
number = 6,
volume = 43,
place = {United States},
year = 2016,
month = 6
}
  • Purpose: To assess the impact of interfractional variations of shape and setup uncertainties on the dose to the parotid glands (PGs) in head-and-neck cancer intensity-modulated radiotherapy and image-guided radiotherapy (IGRT). Methods and Materials: Two scenarios were analyzed retrospectively for 10 head-and-neck cancer patients, treated with helical TomoTherapy (TomoTherapy Inc., Madison, WI): the IGRT scenario and the non-IGRT scenario. The initial dose-volume histograms derived from the planning computed tomography (PCT) scan and 120 recalculated dose-volume histograms of the PGs of each scenario and of corresponding fractions were compared. Setup errors, cumulative median doses (CMDs) for 6 fractions, overall volumes of themore » PGs, and volumes that received less than 1 Gy or more than 1.6 Gy per fraction were analyzed. Results: The mean decrease in the PG volume was 0.13 cm{sup 3}/d. There was a significantly higher CMD than initially predicted (mean increase for 6 fractions, 1.13 Gy for IGRT and 0.96 Gy for non-IGRT). The volume that received less than 1 Gy per fraction decreased (mean difference to PCT, 1.36 cm{sup 3} for IGRT [p = 0.003] and 1.35 cm{sup 3} for non-IGRT [p = 0.003]) and the volume that received more than 1.6 Gy per fraction increased with increasing fraction number (mean difference to PCT, 1.14 cm{sup 3} for IGRT [p = 0.01] and 1.16 cm{sup 3} for non-IGRT [p = 0.006]). There was no statistically significant difference between the two scenarios (CMD, p = 0.095; volume that received <1 Gy per fraction, p = 0.896; and volume that received >1.6 Gy per fraction, p = 0.855). Conclusions: In the analyzed group the actual delivered dose to the PGs does not differ significantly between an IGRT and a non-IGRT approach. However, IGRT in head-and-neck cancer intensity-modulated radiotherapy is strongly recommended to improve patient setup.« less
  • Purpose: To estimate the dose distributions delivered to the patient in each treatment fraction using deformable image registration (DIR) and assess the radiobiological impact of the inter-fraction variations due to patient deformation and setup. Methods: The work is based on the cone beam CT (CBCT) images and treatment plans of two lung cancer patients. Both patients were treated with intensity modulated radiation therapy (IMRT) to 66Gy in 2Gy/fraction. The treatment plans were exported from the treatment planning system (TPS) to the Velocity AI where DIR was performed and the same deformation matrix was used for the deformation of the plannedmore » dose distribution and organ contours to each CBCT dataset. A radiobiological analysis was performed based on the radiobiological parameters of the involved organs at risk (OARs) and planning target volume (PTV). Using the complication free tumor control probability (P+) index, differences in P+ were observed between each CBCT as well as between CBCT and planning dose distributions. Results: The optimal CBCT P? values ranged from 91.6 % to 94.8 % for patient #1 and from 88.8 % to 90.6 % for patient #2. At the dose level of the clinical prescription, the CBCT P+ values ranged from 80.3% to 80.7% for patient #1 and from 80.7% to 81.0% for the patient #2. The planning CT P+ values were 81.0% and 80.7% for the two patients, respectively. These differences emphasize the significance of using the radiobiological analysis when assessing changes in the dose distribution due to the tumor motion and lung deformations. Conclusion: Daily setup variations yield to differences in the actual dose delivered versus the planned one. The observed differences were rather small when only looking at the dosimetric comparison of the dose distributions, however the radiobiology analysis was able to detect clinically relevant differences among the studied dose distributions.« less
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