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Intensity-Modulated Radiotherapy and Inverse Planning

Intensity-Modulated Radiotherapy and Inverse Planning. C-M Charlie Ma, Ph.D. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia, PA 19111, USA. Outline. Rationale for intensity-modulated radiotherapy The IMRT process Elements of an inverse planning system

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Intensity-Modulated Radiotherapy and Inverse Planning

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  1. Intensity-Modulated Radiotherapy and Inverse Planning C-M Charlie Ma, Ph.D. Department of Radiation Oncology Fox Chase Cancer Center Philadelphia, PA 19111, USA

  2. Outline • Rationale for intensity-modulated radiotherapy • The IMRT process • Elements of an inverse planning system • Concepts of inverse planning • Inverse planning algorithms

  3. Clinical Rationale for IMRT • To improve local-regional control through dose escalation to improve overall survival • To reduce normal tissue complications to improve quality of life • To reduce treatment time/cost

  4. IMRT Plan for Vertebral Body Tumor

  5. Prostate and Nodes

  6. Prostate Acceptance Posterior margin: 4mm, the rest 8mm In-house hypofractionated protocol 70.2 Gy - 2.7Gy fx Rectum 65Gy <15%, 31Gy <40%

  7. Plan Verification Structure Segmentation Treatment Optimization Patient Immobilization Treatment Delivery and Verification IMRT - A Complex Process Planning Position Verification target localization Delivery

  8. Immobilization Aquaplast Bite-block Head Holder Vacuum frame Breath Control Spirometer

  9. CT/MRI/PET Image Acquisition

  10. Structure Segmentation

  11. Tumor Tumor Tumor Node #1 Node #2 Image Guidance (PET/CT)

  12. Treatment Delivery: Multileaf Collimator

  13. Beam Delivery with a MLC

  14. Beam Delivery with a MLC 0.03 MU Dose Delivery 2.5 mm spatial longitudinal 5.0mm spatial lateral

  15. Target Localization CT-on-rails BAT

  16. Intensity Modulation and Treatment Optimization

  17. Beam Intensity Modulation 1 cm 1 cm

  18. Does intensity modulation improve the dose distribution? fluence

  19. Intensity Modulated Radiotherapy It works!

  20. How can we determine the individual beamlet weights for IMRT ? Conventional treatment planning starts with a set of beam weights and obtains a plan by a trial-and-error process. This procedure won’t work for IMRT since there are too many unknowns (>2000 beamlet weights).

  21. Conventional Treatment Planning Forward Planning ! ! ! 40% 90% 80% 70%

  22. IMRT Treatment Planning Inverse Planning ? ? ? 40% ! 90% 80% 70%

  23. What is in an Inverse Planning System? Patient data Dose calculation Interface with R&V Optimization Leaf sequencing

  24. Cij -- dose contribution in voxel i from beamlet j in an open beam wj -- Weight for beamlet j j i Di= Cij Wj

  25. j i Dose Calculation for IMRT • Total dose in voxel i • Or dose in any voxel in a more generic form

  26. What’s Inverse Planning ? • Assume D0 is the desired dose • and W0 the required beamlet weights and we have

  27. is an exact mathematical expression of inversely derived beamlet weights for a desired dose distribution D0 However, Unfortunately this inverse process does not work in most, if not all, realistic treatment cases. Practically, what we want is a set of beamlet weights that will give us the best available dose distribution !

  28. What’s Our Solution ? • Assume D0 is the desired dose and W0 the required beamlet weights • What we want is to derive Db the “best achievable” dose and Wb the corresponding beamlet weights Ideal but may be a pie in the sky! Not ideal but achievable The question is how do we know Db is good enough compared with D0?

  29. What’s an Objective Function ? • An objective function is a mathematical evaluation of a treatment dose distribution (wrt. the desired dose distribution). • Ideally, it should include all of our knowledge of radiotherapy: physical as well as biological dosimetric requirements. • The question now is how to “optimize” a given objective function.

  30. A simple dose-based objective function takes the form 0.30 0.20 0.10 0 20 40 60 80 A Sample Objective Function Objective function Iteration step

  31. There are many ways to build an objective function (everybody wants his/her own)! There are many ways to optimize a treatment plan for a given objective function (forward, backward, hybrid, etc)! An inverse planning system may use any optimization algorithms (more likely it is a forward planning, or a hybrid, process).

  32. Optimization of a Multi-Dimensional • Objective Function Computer simulated annealing (Corvus) Gradient method (Helios, Pinnacle) Filtered back-projection (Konrad) Other iterative methods (CMS) Parallel vector method (?)

  33. Most optimization methods use an iterative approach, one way or another Major differences between optimization systems are the construction of the objective function and the methods for search directions and step-length

  34. 100 75 50 25 A random walk Global minimum Small perturbation to avoid local minima Computer Simulated Annealing

  35. 100 75 50 25 Not a random walk Global minimum Other iterative methods

  36. 100 75 50 25 Gradient Method Global minimum local downhill gradient [ -grad f(wi)].

  37. 100 75 50 25 Parallel Vector Method Global minimum Independence and local minimum avoidance

  38. Change Wb Compute Db=CWb Evaluate O=f(Db-D0) How Inverse Planning Is Done? Output D0 Optimal Input W Compute C Inside a computer W is generally not optimal

  39. Factors Affecting Optimization Results • Number of beams • Beam orientation • Optimization parameter and dose constraints • Optimization algorithm and objective function • Experience is gold!

  40. Regions for forced dose gradient

  41. Prostate Plan with 5 intensity levels, 7 beam directions 100% PTV 60% 50% 49 segments  ~ 11.8 min (6MV)

  42. 100% PTV 50% Prostate Plan with 5 intensity levels, 6 beam directions (using a forced dose gradient method) 38 segments  ~ 9.1 min (6MV)

  43. Conclusions • An inverse planning system does not give an optimal plan, but a customized plan • Inverse planning generally works but it is not magic! • It works better for you if you know how it works

  44. Conclusions (cont.) • If it does not work, it’s more likely due to the complexity of the situation…

  45. Conclusions (cont.) • If it does not work, maybe the situation is too simple ...

  46. Conclusions (cont.) • Fortunately, we are very familiar with the situation and we also learn from each other. Therefore, we reach more or less the same goal ...

  47. Conclusions (cont.) • Treatment optimization is an integral part of IMRT • Much more work is needed for the clinical implementation of IMRT • Much more effort is needed to keep it running smoothly and keep pace with upgrades and future enhancements

  48. Thank You

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