Online adaption approaches for intensity modulated proton therapy for head and neck patients based on cone beam CTs and Monte Carlo simulations
To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging. A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the ne...
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| Main Author: | |
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| Format: | Article (Journal) |
| Language: | English |
| Published: |
19 December 2018
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| In: |
Physics in medicine and biology
Year: 2018, Volume: 64, Issue: 1 |
| ISSN: | 1361-6560 |
| DOI: | 10.1088/1361-6560/aaf30b |
| Online Access: | Verlag, Volltext: https://doi.org/10.1088/1361-6560/aaf30b Verlag, Volltext: https://doi.org/10.1088%2F1361-6560%2Faaf30b 
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| Author Notes: | P. Botas, J. Kim, B. Winey and H. Paganetti |
MARC
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| 520 | |a To develop an online plan adaptation algorithm for intensity modulated proton therapy (IMPT) based on fast Monte Carlo dose calculation and cone beam CT (CBCT) imaging. A cohort of ten head and neck cancer patients with an average of six CBCT scans were studied. To adapt the treatment plan to the new patient geometry, contours were propagated to the CBCTs with a vector field (VF) calculated with deformable image registration between the CT and the CBCTs. Within the adaptive planning algorithm, beamlets were shifted following the VF at their distal falloff and raytraced in the CBCT to adjust their energies, creating a geometrically adapted plan. Four geometric adaptation modes were studied: unconstrained geometric shifts (Free), isocenter shift (Iso), a range shifter (RS), or isocenter shift and range shifter (Iso-RS). After evaluation of the geometrical adaptation, the weights of a selected subset of beamlets were automatically tuned using MC-generated influence matrices to fulfill the original plan requirements. All beamlet calculations were done with a fast Monte Carlo running on a GPU (graphics processing unit). Geometrical adaptation alone only worked with small anatomy changes. The weight-tuned adaptation worked for every fraction, with the Free and Iso modes performing similarly and being superior than the two range shifters modes. The mean V95 and V107 were 99.4 ± 0.9 and 6.4% ± 4.7% in the Free mode with weight tuning. The calculation time per fraction was 5 min, but further task parallelization could reduce it to 1–2 min for delivery adaptation right after patient setup. An online adaptation algorithm was developed that significantly improved the treatment quality for inter-fractional geometry changes. Clinical implementation of the algorithm would allow delivery adaptation right before treatment and thus allow planning margin reductions for IMPT. | ||
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