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1 Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA

What’s next in IMRT? - Optimizing the Optimization - T . Bortfeld 1 , C. Thieke 1,2 , K.-H. Küfer 3 , H. Trinkaus 3. 1 Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA 2 Department of Medical Physics, Deutsches Krebsforschungszentrum, Heidelberg, Germany,

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1 Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA

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  1. What’s next in IMRT?- Optimizing the Optimization - T. Bortfeld1, C. Thieke1,2, K.-H. Küfer3, H. Trinkaus3 1Department of Radiation Oncology, Massachusetts General Hospital, Boston, USA 2Department of Medical Physics, Deutsches Krebsforschungszentrum, Heidelberg, Germany, 3Fraunhofer Institut für Techno- und Wirtschaftsmathematik, Kaiserslautern, Germany

  2. Current technical/physical developments in IMRT • Make IMRT more efficient • Streamlined, integrated solutions • Minimize MLC segments • Optimized inverse planning • Make IMRT more accurate • Better dose calculation (superposition, MC) • Image guidance • Online verification with portal imaging • Gating/Tracking to reduce breathing artifacts • Proton and heavy ion IMRT

  3. Current technical/physical developments in IMRT • Make IMRT more efficient • Streamlined, integrated solutions • Minimize MLC segments • Optimized inverse planning • Make IMRT more accurate • Better dose calculation (superposition, MC) • Image guidance • Online verification with portal imaging • Gating/Tracking to reduce breathing artifacts • Proton and heavy ion IMRT

  4. Change “penalties” or “weight factors”

  5. Weight factor approach Optimize F is a single number!

  6. Difficulty 1 • By how much do you change the weight factors, w ? • Trial and error

  7. Example: Head&Neck Brainstem Parotis Spinal Cord

  8. Plan 2 w=10000 100 Plan 1 80 Target w=1 60 Spinal Cord Volume (%) 40 20 0 0 25 50 75 100 Dose (Gy)

  9. Difficulty 2 • “Sensitivity” of the solution?

  10. Difficulty 3 Constraint optimization: Solutions may not be “efficient”!

  11. Example: Head&Neck Brainstem Parotis Spinal Cord

  12. Plan 2 100 Plan 1 80 Brainstem 60 Spinal Cord Volume (%) Target 40 20 0 0 25 50 75 100 Dose (Gy)

  13. Optimization of the Optimization: Solutions • Use Equivalent Uniform Dose (EUD) to characterize the dose in every relevant structure • Find efficient (“Pareto optimal”) solutions • Calculate database with representative solutions, use interpolation

  14. Solutions, part 1 • Use Equivalent Uniform Dose (EUD) • A. Niemierko “A generalized concept of equivalent uniform dose (EUD)” Med. Phys. 26:1100, 1999EUD = uniform dose to the organ that leads to the same effect

  15. Lung:EUD = 25 Gy Spinal Cord:EUD = 52 Gy EUD example Question: What is the homogeneous dose that would give the same effect? 100 75 Volume [%] 50 25 0 100 0 20 40 60 80 Dose [Gy]

  16. Power-Law (p-Norm) Model Mohan et al., Med. Phys. 19(4), 933-944, 1992 Kwa et al., Radiother. Oncol. 48(1), 61-69, 1998 Niemierko, Med. Phys. 26(6), 1100, 1999 “p-norm” Examples:

  17. Solutions, part 2 • Find efficient (Pareto optimal) solutions

  18. Efficient (Pareto optimal) Plan EUD=10 Gy 100 80 Brainstem 60 Spinal Cord EUD = 34 Gy Volume (%) EUD=25 Gy Target EUD = 70 Gy 40 20 0 0 25 50 75 100 Dose (Gy)

  19. Solutions, part 3 • Fill database with solutions for different combinations of EUD values(over night)

  20. Summary • New concept in IMRT optimization • Multi-criteria EUD optimization • Find better solution faster

  21. Power-Law (p-Norm) Model Power-law relationship for tolerance dose (TD):

  22. 100 EUD=72.4 (a=-8) Plan 1 Plan 2 EUD=50.2 Gy 80 Target EUD=4.0 (a=-8) 60 Spinal Cord Volume (%) 40 EUD=18.7 Gy 20 0 0 25 50 75 100 Dose (Gy)

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