Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
eTarget
Primary
Collimator
Flattening
Filter
IC
Jaw
X1
Jaw X2
Objectives
MLC Leaf
Monte Carlo in Treatment Planning
Applications:
IMRT and 4D Radiation Therapy
J.V. Siebers
EPID
MC in IMRT QA
MC in IMRT optimization
MC for 4D planning
Virginia Commonwealth University
Medical College of Virginia Hospitals
Richmond, Virginia USA
Mid exhale
End exhale
Early exhale
Peak inhale
Peak exhale
Respiration
End inhale
Why Monte Carlo for
IMRT?
Conventional dose algorithms can be
inaccurate for
Small fields
Regions of dose gradients (radiation
disequilibrium)
Heterogeneous conditions
54% within 2%,2mm
Early inhale
Mid inhale
Dose Calculation is a two step
process
Incident fluence
prediction
Energy deposition in
the patient/phantom
1
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Conventional method for IMRT
fluence prediction
Why Monte Carlo
for IMRT?
Target
Collimator
Vacuum Win
Flattening Filter
Ion Chamber
Phantom-based measurement
IMRT QA often shows dose
deviations
Jaws
Intensity Matrix
MLC
Profile
Dose difference
histogram over
image
Convolution
Dose difference vs. DTA
54% within 2%,2mm
Ψ ( x, y ) f = Ψ ( x , y ) i × I ( x , y )
MC using TPS
Intensity Matrix does Not improve
fluence prediction
Why MC for IMRT?
MC can directly transport particles through moving MLC
Target
Collimator
Vacuum Win
Flattening Filter
Ion Chamber
Convolution
Jaws
(b)
(c)
MLC
Monte Carlo
(same fluence)
Effects of MLC on fluence are approximated
Ignores MLC scatter, beam hardening, …
Transport
particles through
detailed geometry
of moving MLC
MLC tonguegroove, leakage,
scatter, and
particle energy
dependent effects
are inherently
taken into account
2
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Why MC for IMRT?
Use in of Monte Carlo in IMRT QA
Patient Dose Verification
MC agrees with phantom measurements
Measured
Calculated
Obtain acceptable IMRT plan
Copy plan and compute with MC
Monte Carlo can accurately predict incident
(b)
(c)
fluence
Measurement and Monte Carlo
97% within 2%,2 mm
Yes
<3% DVH difference?
No
Notify planning team
Print and sign DVHs
and dose differences
Yes
Differences
acceptable?
Include in chart
No
Modify plan based on MC
Typical IMRT QA result
Head and Neck Comparison
Study
31 plans, 28 patients
Initial plans with VCU-IMRT
Superposition/Convolution dose algorithm
Intensity modulation via transmission compensator matrix
Plans recomputed with Monte Carlo
EGS4-based system
Intensity modulation via simulation through moving MLC leaves
Isodoses and dose-volume indices compared
GTV: D98, DMean, D50, D2, EUD, and homogeneity index (HI)
CTV: D95 and DMean
ETC: D90 and DMean
Parotids: DMean and D50
Cord/Brainstem: D2
3
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Head and Neck
Head and Neck Comparison
Superposition/Convolution
Monte Carlo
Target structure dose comparisons
Difference
(DMC-DSC)
DSC
Dose differences typically < 5%
Head and Neck
Comparison Study
How algorithms compare with film
measurements?
Results
Average dose difference < 3%
4/31 plans within 3% for all dose indices
28/31 plans within 5% for all dose indices
Possible Causes
Fluence Prediction
Heterogeneities
MC better predicts fluence
4
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Phantom differences are predictive of
patient dose differences
Sub-studies:
Fluence prediction error
(TU-FF-A1-01, …)
Heterogeneity error (SU-EE-A1-01, …)
If use MC to predict fluence modulation,
then SC dose prediction is within 3% for
all dose indices.
Initial
Intensity (I I(x,y))
Patient Comparison
1
7
Create Leaf Sequence
Create Deliverable Intensities
(ID (x,y))
8
Compute
Dose (D O)
2
Evaluate
Plan Objective
3
Converged?
4
Recomputed with MCMLC
Adjust
I(x,y)
6
SC optimized
Monte Carlo in IMRT
Optimization
No
Yes
Optimized
Intensity (IO(x,y))
and Dose D O = D D
5
Why rather than just post-process
recalculation?
Reduce optimization convergence error
How differ from normal
optimization?
66 Gy Hot-Spot
57 Gy line not cover PTV
Leaf trajectories are required (to transport
particles through)
5
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
MC optimized to
reduce OCE
Optimized with MC
(a) Approved plan that did not agree with MC
Original SCopt
Head and Neck
IMRT plan
Deliverable
Plan SC
MC of
Deliverable
MCopt
(deliverable)
(b) MC optimized plan restores target coverage
MC
deliverable
optimization
can restore
original
optimized plan
Initial desired dose distribution was achievable,
but it required different intensities / leaf sequences
than predicted by SC to be achieved in the patient
Conclusions
MC IMRT Optimization
MC and IMRT
MC/IMRT researchers are
developing fast methods to
optimize IMRT plans using MC
algorithms
e-
Faster MC codes
Hybrid dose calculation algorithms
Smoothing/denoising techniques
Target
Primary
Collimator
Flattening
Filter
IC
Jaw
X1
Jaw X2
MLC Leaf
MC more accurate since it inherently
includes MLC effects
MC accounts for heterogeneities and
fluence
MC fluence predictors may improve
conventional dose calculation accuracy
MC optimization reduces optimization
convergence errors
MC is not currently used for IMRT outside
of research centers, but, should be in the
future
EPID
6
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Need for MC for lung dose calculation and
benefits of 4D Tx previously established
tumor
Monte Carlo applications to
four-dimensional radiotherapy
Conventional
Need for MC for lung dose calculation and
benefits of 4D Tx previously established
With 4D imaging
The 4D radiotherapy process
4D CT Imaging
4D
Radiotherapy
Acquisition of a sequence of CT
image sets over consecutive
phases of a breathing cycle
4D Treatment Planning
PTV
PTV
CTV
CTV
The explicit inclusion of
the temporal changes in
anatomy during the
imaging, planning and
delivery of radiotherapy
Designing treatment plans on CT
image sets obtained for each
phase of the breathing cycle
4D Treatment Delivery
Continuous delivery of the 4D
treatment plans throughout the
breathing cycle
Conventional
4D
7
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Aims
Demonstrate a 4D Monte Carlo
Method
4D Dose Calculation Method
1
Create deformation fields
2
Define anatomy on reference CT
3
Map anatomy to all CT sets
4
Create treatment plan
on reference CT
5 Create treatment plan on all CT sets
6
4D Field Arrangement
Combine dose distributions and
display on reference CT
4D plan of single beam
6 fields
Non-opposed
Coplanar
6 MV
Autoblock
PTV + 0.5 cm
8
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
e-
Monte Carlo setup
Target
Isodoses from 6% uncertainty
calculation per beam at peak inhale
Primary
Collimator
Flattening
Filter
Treatment head: EGS4/BEAM
IC
Jaw X
1
Jaw X2
MLC Leaf
DMLC: VCUDMLC
Patient: EGS4/DOSXYZ
Run each for phase
for 6% statistics
EPID
Isodoses from 6% uncertainty
calculation per beam for all phases
Mid exhale
End exhale
Early exhale
Peak exhale
Map dose
distributions
to reference
(equi-time)
Mid exhale
Early exhale
Peak inhale
Peak exhale
Respiration
End inhale
Peak inhale
Respiration
End inhale
Mid exhale
Early inhale
Mid inhale
Early inhale
Mid inhale
9
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Isodoses from all phases deformed
to peak inhale
6% uncertainty
beam
Mid exhale
PTV DVHs
Peak exhale
Respiration
End inhale
Combined
Individual
phases
100
σ=0
σ=2
σ=4
σ=8
σ=16
Volume (%)
80
60
40
Effect of statistical
uncertainty on plans
20
End exhale
Early exhale
Peak inhale
Combined 8
phases
Mid inhale
Early inhale
Conclusion
Advantages of Monte Carlo dose
calc for 4D radiotherapy
(1) higher accuracy for calculation in
electronic disequilibrium conditions
such as those encountered during lung
radiotherapy
(2) if deformable image registration is
used, the calculation time for Monte
Carlo is independent of the number of
3D CT image sets constituting a 4D CT
(same time for 4D and 3D calculation)
0
0
1000
2000
3000
4000
5000
6000
7000
Dose (cGy)
10
Monte Carlo and IMRT
ASTRO 2003
Jeffrey V. Siebers, VCU
Acknowledgements /
Contributors
Paul Keall
Ivaylo Mihaylov
Weidong Li
Iwan Kawrakow
Radhe Mohan
S. Joshi
11