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Use of GammaPlan convolution algorithm for dose calculation on CT and cone-beam CT images
Radiation Oncology Journal ; : 129-138, 2021.
Article in En | WPRIM | ID: wpr-903261
Responsible library: WPRO
ABSTRACT
Purpose@#The aim of this study was to assess the suitability of using cone-beam computed tomography images (CBCTs) produced in a Leksell Gamma Knife (LGK) Icon system to generate electron density information for the convolution algorithm in Leksell GammaPlan (LGP) Treatment Planning System (TPS). @*Materials and Methods@#A retrospective set of 30 LGK treatment plans generated for patients with multiple metastases was selected in this study. Both CBCTs and fan-beam CTs were used to provide electron density data for the convolution algorithm. Plan quality metrics such as coverage, selectivity, gradient index, and beam-on time were used to assess the changes introduced by convolution using CBCT (convCBCT) and planning CT (convCT) data compared to the homogeneous TMR10 algorithm. @*Results@#The mean beam-on time for TMR10 and convCBCT was found to be 18.9 ± 5.8 minutes and 21.7 ± 6.6 minutes, respectively. The absolute mean difference between TMR10 and convCBCT for coverage, selectivity, and gradient index were 0.001, 0.02, and 0.0002, respectively. The calculated beam-on times for convCBCT were higher than the time calculated for convCT treatment plans. This is attributed to the considerable variation in Hounsfield values (HU) dependent on the position within the field of view. @*Conclusion@#The artifacts from the CBCT’s limited field-of-view and considerable HU variation need to be taken into account before considering the use of convolution algorithm for dose calculation on CBCT image datasets, and electron data derived from the onboard CBCT should be used with caution.
Full text: 1 Index: WPRIM Type of study: Prognostic_studies Language: En Journal: Radiation Oncology Journal Year: 2021 Type: Article
Full text: 1 Index: WPRIM Type of study: Prognostic_studies Language: En Journal: Radiation Oncology Journal Year: 2021 Type: Article