Poster Poster Program Therapy Physics

Diagnostic CT–Based Pre-Planning and CBCT Adaptive Recalculation on a Varian Hypersight Linac Using Raystation

Abstract
Purpose

Palliative radiotherapy patients may present emergently and often have recent diagnostic CT (dCT) imaging suitable for treatment planning. Requiring an additional CT simulation can delay treatment and adds burden to patients for whom transport is difficult and time is limited. We developed and validated a workflow that enables dCT-based pre-planning with on-unit CBCT (HyperSight) dose recalculation for rapid plan verification/adaptation.

Methods

Representative palliative cases were exported from the institutional PACS and imported into RayStation for dCT-based planning. CBCT electron density mapping was performed using RayStation’s CBCT image conversion workflow, in which HU values are assigned to density bins. A site-specific CBCT HU–density calibration was generated using an RMI phantom. A custom script was developed to retrieve the treatment CBCT from MOSAIQ, apply recorded setup shifts to align the plan geometry to the on-treatment position, and perform rapid dose recalculation in RayStation. Validation included end-to-end testing with an anthropomorphic Rando phantom to introduce realistic anatomic/setup differences. For comparison, the same plans were also recalculated on a conventional planning CT (TPCT) in addition to the original dCT and the on-unit CBCT.

Results

For a thoracic target (T3) with auto-contouring performed on both dCT and CBCT, CTV mean dose was 2188 cGy (dCT), 2167 cGy (TPCT), and 2163 cGy (CBCT). CTV minimum dose remained within 1% of the 2000 cGy prescription across datasets, supporting the adequacy of a 1 cm margin under the evaluated setup/anatomic variability.

Conclusion

A clinically deployable workflow for dCT-based pre-planning with CBCT-based adaptive dose recalculation on a HyperSight-equipped linac is presented, enabling faster palliative treatment initiation while maintaining dosimetric agreement with conventional CT-based approaches.

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