21 chap 19 three-dimensional conformal radiation therapy

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21 chap 19 three-dimensional conformal radiation therapy

  1. 1. Chapter 19Three-Dimensional Conformal Radiation Therapy 1
  2. 2. 19.1 Introduction What is 3D-CRT: • Based on 3D anatomic information • Dose distribution conforms to the target, and • Avoids critical organs and normal tissues • May also include clinical objectives such as TCP and NTCP Difficulties of 3D-CRT: • Tumor (CTV) delineation • Treatment uncertainties (setup uncertainty, organ motion, etc.) • Lack of clinical data to verify TCP and NTCP models 2
  3. 3. 19.2 Treatment-Planning Process 3D patient Image Plan optimization (# of (CT, MRI, PET…) beams, beam angle, energy, wedge, weight, intensity distribution) Target, organ delineation (segmentation) Dose calculation BEV field design Plan evaluation (isodose (beam angle, aperture) display, TCP, NTCP) 3
  4. 4. 19.2 Treatment-Planning Process A. Imaging Data Digitally Reconstructed Radiograph (DRR) 4
  5. 5. 5
  6. 6. 19.2 Treatment-Planning Process A. Imaging Data CT: attenuation coefficients (µ), can be converted to electron density, used for treatment planning/dose calculation. Spatial resolution ~1mm in X/Y directions, variable (1-10 mm) in Z-direction, which affects the quality of DRR MRI: proton density, better soft tissue delineation (brain, head/neck, prostate), but insensitive to calcification and bony structures. Spatial resolution ~1mm in all directions. PET: functional imageCT MRI PET 6
  7. 7. 19.2 Treatment-Planning Process B. Image Registration • Point-based registration - minimizes discrepancy between corresponding point pairs • Surface-based registration – minimizes discrepancy between two surfaces • Image (intensity)-based registration – minimizes a similarity metric (mutual information, cross correlation, etc.) between two images. • Deformable registration – usually image-based, point-to-point transformation to minimize a similarity metric between two images. 7
  8. 8. 19.2 Treatment-Planning Process B. Image Registration Point-based registration 8
  9. 9. 19.2 Treatment-Planning Process B. Image Registration Surface-based registration Before registration after registration 9
  10. 10. 19.2 Treatment-Planning Process B. Image Registration Image-based registrationbefore registration after registration 10
  11. 11. Lung - CTI/Siemens PET/CTCT PET PET/CT 11
  12. 12. 19.2 Treatment-Planning Process B. Image Registration deformable registration – before registration 12
  13. 13. 19.2 Treatment-Planning Process B. Image Registration deformable registration – after registration 13
  14. 14. 19.2 Treatment-Planning Process C. Image Segmentation Manual segmentation – Auto segmentation – Laborious, time-consuming A very difficult problem Contours drawn by physician Contours drawn by auto- deformation from another contour 14
  15. 15. 19.2 Treatment-Planning Process D. Beam Aperture Design Beam’s Eye View (BEV) 15
  16. 16. target 16
  17. 17. 19.2 Treatment-Planning Process E. Field Multiplicity and Collimation 17
  18. 18. 19.2 Treatment-Planning Process F. Plan Optimization and Evaluation Isodose curves Isodose surface eyes PTV cord 18
  19. 19. 19.2 Treatment-Planning Process F. Plan Optimization and Evaluation DVH for a prostate plan 100 D=77Gy V=90% 75 Volume (%) D=75Gy 50 V=30% target bladder 25 rectum femurs 0 0 25 50 75 100 Dose (Gy) D=72Gy 19
  20. 20. 19.3 Dose Calculation Algorithms • Correction-based: semi-empirical, based on measured data such as TMR, OCR, etc. • Model-based: based on phase-space data (energy spectra, angular distribution), Monte-Carlo generated dose kernels, ray-tracing 3D inhomogeneity correction. • Monte Carlo: simulation of physical events by random sampling; commonly used codes EGS4, MCNP, FLUKA’ GEANT, etc; still too slow for routine clinical use 20
  21. 21. 19.3 Dose Calculation Algorithms A. Correction-based Algorithms Based on data (PDD/TMR/TPR, OCR) measured in homogeneous phantom (water) at standard distance (e.g. SSD = 100 cm) For individual plans, corrections needed for: • Surface contours • Irregular field shape/size • Distance (inverse-square corrections) • Non-uniform intensity (wedge, IMRT) • Inhomogeneity correction 21
  22. 22. 19.3 Dose Calculation Algorithms B. Model-based Algorithms Convolution-superposition: Inhomogeneity correction Atten coeff Primary energy fluence       made along the ray lines D( r ) = µ ( r ) Ψ p ( r ) A( r − r ) d r ∫     = T p ( r ) A( r − r ) d r ∫ TERMA Dose kernel 22
  23. 23. Point kernel convolution in homogeneous medium       D( r ) = µ ( r ) Ψ p ( r ) A( r − r ) d r ∫Primary fluence Point kernel dose 23
  24. 24. 19.3 Dose Calculation Algorithms C. Monte Carlo calculation in CT grids Simulates the physical processes of particle transport and interactions Photon Electron• Coherent scattering • Continuous energy loss• Photoelectric • Multiple scattering• Compton ,Ω 2 • Delta ray production E 2• Pair production • Bremsstrahlung production interaction  • Positron annihilation r2 ,Ω 1 t E1 s por E’ t ran ,Ω ’r1 24
  25. 25. 19.3 Dose Calculation Algorithms C. Monte Carlo calculation in CT gridsThe number of first collisions in eachvoxel is sampled exactly based onattenuation through ray-tracing incident photon Primary dose: direct energy deposition usingScatter Dose: e- tracklength & mass e- stopping powerE>Eth, direct energydeposition pE<Eth, KERMA Particle allowed toapproximation step across voxels until entering a different medium, step size scaled by local density 25
  26. 26. Lung Treatment with 15 MV photons PTV GTV100 95 90 70 50 20 Monte Carlo Conventional 26
  27. 27. 27

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