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Matthias Söhn 1 , Di Yan 2 and Markus Alber 1

4D Stereotactic Lung IMRT Planning using Monte-Carlo Dose Calculations on Multiple RCCT-based Deformable Geometries. Matthias Söhn 1 , Di Yan 2 and Markus Alber 1 University of Tübingen , Radiooncological Clinic, Sect. f. Biomedical Physics, Tübingen, Germany

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Matthias Söhn 1 , Di Yan 2 and Markus Alber 1

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  1. 4D Stereotactic Lung IMRT Planning using Monte-Carlo Dose Calculations on Multiple RCCT-based Deformable Geometries Matthias Söhn1, Di Yan2 and Markus Alber1 University of Tübingen, Radiooncological Clinic, Sect. f. Biomedical Physics, Tübingen, Germany William Beaumont Hospital, Radiation Oncology, Royal Oak, MI, USA Forschungszentrum für Hochpräzisionsbetrahlung

  2. SRT for small lesions with large mobility:Problems of margin-based, static planning • large margins necessary! dose to large normal tissue volume ITV restricts tumor dose PTV • what geometry is to be used for dose calculation?

  3. SRT for small lesions with large mobility:Problems of margin-based, static planning exhale position inhale position tumor spends more time in upper part of PTV region (exhale) than in lower part (inhale) …large CTV-motion relative to static planning geometry …dose distribution changes with CTV-motion • Is it actually necessary to cover whole PTV region with full dose? gated treatment optimize actual dose-to-moving-CTV “Tissue-eye-view” static dose distribution, calculated on average CT …the actual “dynamic” dose distribution! • static planning

  4. 4D IMRT Planning… Our approach in the following: optimization of the expected dose in moving tissue (Tissue Eye View)

  5. The road to Tissue-Eye-View:Dose warping to a reference phase beamlet dose… calc. in different geometries

  6. The road to Tissue-Eye-View:Dose warping to a reference phase beamlet dose… calc. in different geometries warped to reference geometry deformable registration

  7. The road to Tissue-Eye-View:Probability density function (pdf) of breathing 0.5 0.4 amplitude [a.u.] 0.3 0.2 0.1 0 50 100 time [s] 0 0In 25In 50In 75In 75Ex 50Ex 25Ex 100Ex relative time spend in CT-bin => relative weights pdf statistical description of breathing motion! 100 amplitude 0 time [s]

  8. Tissue-Eye-View: expected dose-to-moving-tissue beamlet dose… calc. in different geometries warped to reference geometry optimization in tissue-eye-view! TISSUE EYE VIEW accumulated expected dose distribution in moving tissue, shown in reference geometry accumulated in reference geometry using breathing PDF

  9. 4D- vs. margin-based static planning: A test case • free-breathing PTV-plan: PTV = ITV of all 8 RCCT-phases • exhale-gating PTV-plan: PTV = ITV of 3 RCCT-phases around exhale • free-breathing 4D-plan: optimization of expected dose in ‘tissue-eye-view’ with explicit dose calculation in all 8 RCCT-phases Implemented using IMRT-software Hyperion: EUD-based, constrained optimization Monte-Carlo dose calculation (XVMC) 11 beams Prescription: • 55Gy EUD to target in 11fx • constraints to target (limited overdosage), lung and other unspecified tissue Comparison of 3 plans… Idealized assumption: perfect daily target-based setup, i.e. no setup-margin

  10. Results: Dose distributions (coronal view) lung sparing static dose static dose free-breathing PTV-plan exhale-gating PTV-plan free-breathing 4D-plan accumulated dose accumulated dose accumulated dose 52.2Gy 57.8Gy 46.8Gy 38.5Gy 27.5Gy 22.0Gy 16.5Gy 11.0Gy

  11. Results: Target DVHs (accumulated CTV doses) gating and 4D-plan with similar doses to moving CTV free-breathing PTV-plan: lowest and most inhomogeneous CTV-dose

  12. Results: DVHs of OARs (accumulated doses) constraints met similarly well for all plans right (ipsilateral) lung skin/unspecified left (contralateral) lung

  13. Results: EUDs & dosimetric parameters

  14. Results: EUDs & dosimetric parameters, performance • voxel-size: 3mm; beamlet-size: 4x2mm • stat. accuracy MC dose calculation: 3% (3.5•106 histories/segment) • dual-quadcore Intel Xeon @ 2.66GHz (8 CPU cores), 16GB memory

  15. Summary & Conclusions 4D-planning: optimization in Tissue-Eye-View! • explicit dose calculation in multiple geometries using Monte Carlo • deformable registration, allowing dose warping to reference geometry • optimization of expected dose: dose accumulation in reference geometry using Probability Density Function (pdf) of breathing • potential of dose escalation compared to free-breathing PTV-based planning • equal target coverage as gated-treatment, but reduced workload during treatment

  16. Appendix: Fluence distributions 4D plan PTV plan 155 degree -65 degree -125 degree

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