Comparison of catheter reconstruction techniques for the lunar ovoid channels of the VeneziaTM applicator

Abstract

Purpose: The aim of this study was to compare catheter reconstruction methods for lunar ovoid channels of the Venezia TM advanced gynecological applicator (Elekta, Sweden). Material and methods: Three available lunar ovoid sizes (22, 26, and 30 mm effective diameter) were evaluated. Computed tomography (CT) scans were performed with a dummy wire inserted and with the Flexitron ® source position simulator (SPS) at step sizes of 5 mm from the most distal dwell position. Treatment plans were generated in Oncentra ® (version 4.5.3) with different catheter reconstruction techniques: centerline reconstruction, tracing a CT dummy wire, using a source path model provided by Elekta, and using the SPS at each planning dwell position. Source position agreement was assessed in registered CT images, and dose differences were calculated with the SPS-based treatment plan as a reference. Finally, dose-volume histogram (DVH) parameters were evaluated for clinical plans with the Venezia TM applicator. Results: For the most distal dwell position, the manufacturer's model had the closest agreement with the SPS at 0.6 ±0.3 mm across applicator sizes. Relative to the SPS, maximal dose differences outside of the applicator were between 16-39% for a 0.1 cm 3 volume and 3.6-9.1% for a 2.0 cm 3 volume. For two clinical plans, volume-based DVH parameters agreed ≤ 3.9%, while deviations ≤ 5.3% were seen for point metrics. Conclusions: Relative to the SPS-based plan, large local dose discrepancies were reduced, but not eliminated, using the manufacturer's source path model. The choice of reconstruction technique was found to have relatively limited impact on DVH parameters for regions outside of the vaginal mucosa.

Deep learning applications in automatic segmentation and reconstruction in CT-based cervix brachytherapy

“…Applicator reconstruction is one of the most critical steps in brachytherapy treatment planning [2,20]. Motivated by recent advances in deep learning, we investigated a deep learning method to automatically segment and reconstruct applicators in CT images for cervix brachytherapy treatment planning with the Fletcher applicator.…”