Computerized treatment planning systems - II CRISTER CEBERG Modern computerized treatment planning Modern computerized treatment planning Generally available hardware
» Network solutions
– Servers
– Workstations
– Citrix clients
Dell Precision T5400/T5500 ...not so accessible software
» Documentation
– Scientific papers
– Reference manuals
» The implementation is still often a ”black box”!
» We will look at general features and examples Types of dose calculation algorithms
IAEA TRS 430 Types of dose calculation algorithms
IAEA TRS 430 CT for patient information
»Diagnostic tools »Target determination »Basis for calculations
– Hounsfield units HU 1000 H2O H2O Conversion to electron density The Hounsfield scale
+1000
+800 Bone +100 Bonecompact +90 substance compact +80 Coagulated substance +600 blood +70 SD +60 Liver Whole Blood +400 +50 Spleen Muscle +40 Pancreas Fatty Bone +30 Kidney mixed spongy +200 Exudate substance +20 Transudate tissue +100 +10 Ringer solut. 0 Water Fat -100 -10 -20 -200 -30 Fatty -40 mixed tissue -400 -50 -60 -70 -600 -80 Lung tissue -90 Fat -100 -800
-1000 Patients in motion Creating a voxel matrix
» Map to each voxel – Tissue type – Composition and density – Interaction properties
» sphoto, scompton, spair General components of calculation models
» Incident photon fluence
» Raytracing
» Redistribution of energy Incident fluence Primary and secondary sources
RayStation Primary source
Beam profile Gaussian Incident primary source primary profile
(can also be a wedge profile)
Profile slope and source width are fitting parameters Flattening filter source
Gaussian secondary source
Calc point’s view
Head scatter contribution Collimator scatter source
» Transmission and leakage
» Scatter from visible surfaces Incident photon fluence
» The total incident photon fluence – Primary source – Flattening filter – Collimator scatter
» Spectrum 0(Ei ) Incident photon fluence
» The total incident photon fluence – Primary source – Flattening filter – Collimator scatter » Raytracing through density matrix Raytracing
Incident ray 0(Ei )
Attenuation r (r,E ) (E )exp (r,E )dl i 0 i i r0
Total energy released in matter (TERMA) (r,E) T(r) (r,E)dE E m(r) Redistribution of energy
Incident ray 0(Ei )
Raytracing T(r) Redistribution of energy Kernel
Incident ray 0(Ei )
Raytracing T(r) Redistribution of energy
A(rc,r) Monte Carlo calculated kernels
0.5 cm Primary » Absorbed dose distribution around a single interaction point
» Example for 1.25 MeV
10 cm First scatter
10 cm Total
Mackie et al, Phys Med Biol 33:1-20, 1988 Monte Carlo calculated kernels
» Library of mono-energetic kernels
» Estimate of linac spectrum (fitting parameter)
» Composite poly-energetic kernel is obtained by summation
RayStation Monte Carlo calculated kernels
Kernel anatomy - 5 MeV Kernel anatomy - 15 MV
0.1 0.1
0.01 0.01
]
]
3
-
3
-
[cm [cm 0.001 0.001 Total Total First scatter kerma 0.0001 0.0001 5 10 15 20 25 5 10 15 20 25
Fractional energy volume per unit imparted energy Fractional Radius [cm] Radius [cm] Fractional energy volume per unit imparted energy Fractional
c ear c ebr A(r) 1 2 r 2 r 2
Ahnesjö, Med Phys 16:577-592, 1989 Kernel
Incident ray 0(Ei )
Raytracing T(r) Redistribution of energy
A(rc,r) Kernel integration
Incident ray 0(Ei )
Raytracing T(r) Redistribution of energy
A(rc,r)
D(r ) T(r) A(r ,r)dV c c V Kernel integration
» Interaction vs. deposition point-of-view Kernel integration
D(r ) T(r) A(r ,r)dV c c V » Convolution
– Invariant kernel
1 – D(rc ) T(r) A(rc,r) FFT FFT(T)FFT(A) » Superposition
– Variation of energy, divergence, density
– D(rc ) T(r) A(rc,r) Convolution (2D)
=
Dose TERMA Deposition = Absorbed Dose Kernel
Tommy Knöös Example – XiO FFT
» Poly-energetic kernels are obtained from Mackie’s kernel library, re-sampled to Cartesian coordinates
» Separate kernels for primary and scatter dose
– Primary part integrated with high resolution in small volume
– Scatter part with lower resolution over larger volume
» FFT-based convolution
– Large time saving (65%)
– Effects of invariant kernel Limitations
» Kernels are not invariant
– Energy distribution varies with position (beam hardening and beam softening)
– Divergence
– Density variations
» Approximations are needed
– FFT convolution requires invariant kernels
– Analytical methods are time consuming Pencil-beam approximation
» Pre-convolving depth dimension
1.0 0.9 0.8 0.7 0.6 0.5 0.4 = 0.3
Relative fluenceRelative 0.2 0.1 0.0 0 10 20 30 40 50 Depth (cm)
Tommy Knöös Pencil-beam approximation
» Reduce calculation from 3D -> 2D => faster
=
Dose Energy fluence Deposition = Absorbed Dose Kernel
Tommy Knöös Pencil-beam kernels
» Monte Carlo calculations (standard)
» Extracted from measurements Example – MasterPlan PB
» Pencil-beam kernels based on Monte Carlo calculated point- spread kernels, integrated and fitted to depth dose curves
» Separates primary and scatter components
» Heterogeneities handled by effective path-length (i.e. only longitudinal scaling)
1.0E-01
1.0E-02 z=5 z=10 1.0E-03 z=20
1.0E-04 P(r) a(z)r b(z)r 1.0E-05 A(z) e B(z) e p(r,z) 1.0E-06 r r 1.0E-07
1.0E-08
1.0E-09
1.0E-10 0 5 10 15 20 25 30 35 40 45 50 Radie (cm) Pencil-beam limitations
» Assumes scatter-dose integration at effective depth Pencil-beam limitations
» Assumes electron equilibrium in inhomogeneities
Build up
Water equivalent Low-density inhomogeneity
Penumbra
Penumbra
Build down Example – Eclipse AAA
» Monte Carlo calculated pencil-beams
» Divergent, finite-size beamlets
» Heterogeneity correction
– Effective depth in longitudinal direction
– Lateral density-scaling Collapsed-cone approximation
» The kernel is ”collapsed” to a finite number of cones
Continuous Dose Discrete Dose Spread Kernel Spread Directions Collapsed-cone approximation
...not so
...more like so Collapsed-cone approximation
» Fixed number of transport directions (”cones” or ”channels”)
» >100 cones are required Incident beam
Transport lines
Ahnesjö, Med Phys 16:577, 1989 Flexible approach
» Density scaling along each transport ray
» Heterogeneity correction in all directions (3D)
» Can account for beam-hardening and off-axis softening
» Can include kernel tilt in divergent beams
» Sparse or variable calculation grid (”multi-grid” or ”adaptive”)
– Gradient dependent
– Interpolation Summary
» Incident photon fluence
» Raytracing
» Redistribution of energy
– Pencil-beam
– Collapsed cone Summary
» Incident photon fluence
» Raytracing
» Redistribution of energy
– Pencil-beam
– Collapsed cone Electron contamination
» Integration of electron pencil-beam kernel
RayStation Electron contamination
» Important for in vivo dosimetry Comparison between different algorithms
» Type A models
– Primarily based on effective-path length correction
– Longitudinal scaling only
» Type B models
– Approximate handling of lateral electron transport
– Longitudinal and lateral scaling
Tommy Knöös Comparison between different algorithms
» Water phantom with low-density (0.2 g/cm3) ”lung” insert Comparison between different algorithms
» Water phantom with low-density (0.2 g/cm3) ”lung” insert
PDD
Depth Comparison between different algorithms
6 MV 15 MV
6 MV 15 MV
Fogliata et al, Phys Med Biol, 2007 Clinical example – breast treatment
10, 30, 50, 70, 90, 95, 100 & 105 %
MasterPlan
CC: Slightly lower dose to breast and especially in lung in proximity to the target. However, larger irradiated lung volume
Tommy Knöös Clinical example – lung treatment
10, 30, 50, 70, 90, 95, 100 & 105 %
MasterPlan CC: Average dose in PTV 2-4% lower, and wider penumbra. Max dose to ipsi-lateral lung 10% lower. Similar dose to contra-lateral lung. Tommy Knöös Two atoms are sitting in a field of ionizing radiation.
One atom says, "I think I lost an electron."
The other says, "Are you sure?"
The first atom says, "I'm positive!"