Abstract
Background and purpose: The recently introduced high-performance CBCT imaging system called HyperSight offers improved Hounsfield units (HU) accuracy, a larger CBCT field-of-view and improved image quality compared to conventional ring gantry CBCT, possibly enabling treatment planning on CBCT imaging directly. In this study, we evaluated whether the dose calculation accuracy on HyperSight CBCT was sufficient for treatment planning in prostate and lung cancer patients. Materials and methods: HyperSight CBCT was compared to planning CT (pCT) in terms of HU-to-mass density (MD) calibration curves. For twenty prostate patients and twenty lung patients, differences in DVH parameters, and 3D global gamma between dose distributions calculated on pCT and free breathing HyperSight CBCT were evaluated. For this purpose, HyperSight CBCT acquired at the first fraction was rigidly registered to the pCT, delineations from the CT were propagated and the dose was recalculated on the HyperSight CBCT. Results: For each insert of the HU-to-MD calibration phantom, the HU values of HyperSight CBCT and pCT agreed within 35 HU. For prostate maximum deviations in PTV D mean, V 95% and V 107% were 1.8 %, −1.1 % and < 0.1 % respectively. For lung PTV V 95% was generally lower (median −1.1 %) and PTV V 107% was generally higher (median 1.1 %) on HyperSight CBCT due to breathing motion artifacts. The average (±SD) 2 %/2mm gamma pass rate was 98.7 %±1.2 % for prostate cancer patients and 96.2 %±2.1 % for lung cancer patients. Conclusion: HyperSight CBCT enabled accurate dose calculation for prostate cancer patients, without implementation of a specific HyperSight CBCT-to-MD curve. For lung cancer patients, breathing motion hampered accurate dose calculations.
| Original language | English |
|---|---|
| Article number | 110596 |
| Number of pages | 7 |
| Journal | Radiotherapy and Oncology |
| Volume | 202 |
| DOIs | |
| Publication status | Published - Jan 2025 |
Bibliographical note
Publisher Copyright:© 2024 The Author(s)
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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