Monte Carlo based treatment planning systems - …acquaviva.com.br/CBFM2015/trabalhos/CBFM_Palestras… · · 2015-09-15Monte Carlo based treatment planning systems Joanna E. Cygler,

Monte Carlo based treatment planning

systems

Joanna E. Cygler, Ph.D., FCCPM, FAAPM, FCOMP

The Ottawa Hospital Cancer Centre, Ottawa, Canada

Carleton University, Dept. of Physics, Ottawa, Canada

University of Ottawa, Dept. of Radiology, Ottawa, Canada

Rationale for Monte Carlo dose

calculation for electron beams

• Difficulties of commercial pencil beam based algorithms

– Monitor unit calculations for arbitrary SSD

values – large errors*

– Dose distributions

in heterogeneous media

have large errors for

complex geometries

*can be circumvented by entering separate virtual

machines for each SSD – labor consuming

Ding, G. X., et al, Int. J. Rad. Onc. Biol. Phys.

(2005) 63:622-6332

Rationale for MC dose calculations

for photon beams

Arnfield et al. (MCV), Med. Phys, 27 (6) 2000

water water

3

Commercial MC-based TPS:

electron beams

• MDS Nordion (NucletronElekta) 2001

– First commercial Monte Carlo treatment planning for electron beams

– Kawrakow’s VMC++ Monte Carlo dose calculation algorithm (2000)

– Handles electron beams from all clinical linacs

• Varian Eclipse eMC 2004

– Neuenschwander’s MMC dose calculation algorithm (1992)

– Handles electron beams from Varian linacs only (23EX)

– work in progress to include beam models for linacs from other vendors (M.K. Fix et al, Phys. Med.

Biol. 58 (2013) 2841–2859)

• Elekta-CMS XiO eMC for electron beams 2010

– Based on VMC (Kawrakow, Fippel, Friedrich, 1996)


• Elekta – Monaco

– Kawrakow’s VMC++ Monte Carlo dose calculation algorithm (2000)


4

• Elekta (CMS) - Monaco (IMRT)

• Brainlab - iPlan

Based on XVMC (Kawrakow, Fippel, Friedrich, 1996 and Fippel 1999)

Commercial MC-based TPS:

photon beams

• Accuray - Multiplan in CyberKnife TPS

Based on MCDOSE (Ma et al 2002 and 2008)

5

Components of Monte Carlo based dose

calculation system

There are two basic components of MC dose calculation, see

the next slide:

1. Particle transport through the accelerator head

– explicit transport (e.g. BEAM code)

– accelerator head model (parameterization of primary and

scattered beam components)

2. Dose calculation in the patient

6

Example of a beam model

Sub-sources

1 - the main diverging source

of electrons and photons;

2 - edge source of electrons;

3 - transmission source of

photons;

4 - line source of electrons

and photons.

M.K. Fix et al, Phys. Med. Biol. 58 (2013) 2841–28597

Clinical implementation of MC

treatment planning software

• Beam data acquisition and fitting

• Software commissioning tests*

– Beam model verification

– Dose profiles and MU calculations in a homogeneous water tank

– In-patient dose calculations

• Clinical implementation

– procedures for clinical use

– possible restrictions

– staff training

*should include tests specific to Monte Carlo

A physicist responsible for TPS implementation should have a thorough understanding of how the system works.

8

Issues to consider

• Statistical noise

• Voxel size (spatial resolution)

• Isodose smoothing

• Dose to water vs. dose to medium

• Differences between standard (water tank based)

and MC based MU calculations

• Potential clinical implications of MC calculated dose

distributions

– prescription change?

9

Guidance on how to proceed - AAPM Task Group Report No. 105: Issues

associated with clinical implementation of photon and electron beam

Monte Carlo-based treatment planning (Med. Phys. 34, 4818-53, 2007 )

Issues to consider





• Differences between standard and MC based MU

calculations


distributions


10




AAPM TG-105: Summary of Recommendations

Patient Simulation:

• Statistical Uncertainties:

Should be specified to doses within volumes

consisting of many voxels; single-voxel dose

uncertainty estimates should be avoided as should be

specification to the maximum or minimum dose voxels,

Rogers and Mohan, (2000)

11

2

5.05.0

2

maxmax

1

DD i

i

DD D

D

K

10 million50 million150 million1.5 billion

Effect of uncertainties on the 95% IDL

I.Chetty et al, Red Journal, (2006), 1249-5912

Issues to consider






calculations


distributions


13




Voxel size – required spatial

resolution

• Small voxel size when high spatial resolution required

• Smaller voxel – increased calculation time

14

Monte-Carlo Settings: Effect on

computation time

Timing Results XiO TPS:

For 9 and 17 MeV beams, 10x10

cm2 applicator and the trachea

and spine phantom, timing tests

were performed for a clinical XiO

Linux workstation, which employs

8 processors, 3 GHz each, with

8.29 GB of RAM.

y = 3.4x-2.0

y = 6.4x-2.1

y = 0.7x-2.0

y = 0.4x-2.0

0

5

10

15

20

25

30

0 0.5 1 1.5 2 2.5

MRSU %

tim

e /

min

9 MeV 2.5 mm voxel

17 MeV 2.5 mm voxel

17 MeV 5 mm voxel

9MeV 5 mm voxel

Cygler, J.E., and Ding, G.X., in Monte Carlo Techniques in Radiation Therapy,

ISBN-10: 1466507926, Taylor & Francis (CRC Press INC ) Boca Raton 2013, p 155-16615

Issues to consider






calculations


distributions


16




Eclipse eMC

Effect of voxel size and smoothing

Ding, G X., et al (2006). Phys. Med. Biol. 51 (2006) 2781-2799. 17

Issues to consider






calculations


distributions


18




Dose-to-water vs. dose-to-medium

Ding, G X., et al Phys. Med. Biol. 51 (2006) 2781-2799.

Dm - energy absorbed

in a medium voxel

divided by the mass of

the medium element.

Dw - energy absorbed in

a small cavity of water

divided by the mass of

that cavity. Voxel of medium

w

mmw

SDD

Small volume

of water

Voxel of medium

Clinical Examples: 6MV Dw and Dm

DmDw

Dogan, et al, Phys Med Biol 51, (2006) 4967-4980 20

Dose-to-water vs. dose-to-medium

electron beams

DTM DTW

DTW-DTM

6 MeV beam, 15x15 cm2 applicator, both 602 MU

MRSU=2%, voxel size=4×4×4 mm3

21


Patient Simulation:

• Dose to water and dose to medium:

Vendors

should state explicitly to which material dose is

reported;

allow for conversion between Dw and Dm

At this point only Elekta XiO fully complies

22


Experimental Verification:

• In addition to standard dose algorithm commissioning

tests, verification should include testing in complex

situations to verify the expected improved accuracy

with the MC method;

• Detector perturbations need to be carefully assessed

particularly under conditions of electronic

disequilibrium;

• Measurement uncertainties should be understood and

estimated, where possible, in the verification process 23

Example of beam model verification

XiO eMC: cutout factors

Vandervoort et al, Med. Phys. 41, 2014; http://dx.doi.org/10.1118/1.4853375

Cutout Output Factors: 9 MeV

0.350

0.450

0.550

0.650

0.750

0.850

0.950

1.050

1 2 3 4 5 6 7 8 9

Square Cutout Length (cm)

Ou

tpu

t F

acto

r (c

Gy/M

U)

Experimental

XiO Calculated

Cutout Output Factors: 17 MeV

0.600

0.650

0.700

0.750

0.800

0.850

0.900

0.950

1.000

1.050

1 2 3 4 5 6 7 8 9


Ou

tpu

t F

acto

r (c

Gy/M

U)

Experimental

XiO Calculated

SSD=100 cm

SSD=115 cm

0.800

0.850

0.900

0.950

1.000

1.050

1 3 5 7 9


0.390

0.440

0.490

0.540

0.590

0.640

0.690

0.740

1 3 5 7 9


SSD=100 cm

SSD=115 cm

24

More clinical issues to consider:

electron beams

25

MU - MC vs. hand calculations

Monte Carlo Hand calculations

Real physical dose

calculated on a patient

anatomy

Rectangular water tank

Heterogeneity correction

included

Contour irregularities

No heterogeneity

correction

Arbitrary beam anglePerpendicular beam

incidence only

26

9 MeV, full scatter phantom (water tank)

RDR=1 cGy/MU

100% isodose at the nominal (reference) dmax depth 27

Lateral scatter missing

Real contour / Water tank =

=234MU / 200MU=1.17

Reason for more MU: % isodose at the nominal (reference) dmax depth is

less than 100% 28

MU real patient vs. water tank

MC / Water tank= 292 / 256=1.14

29

30

More clinical issues to consider:

photon beams

DVH for the PTV

Pe

rce

nt

Vo

lum

e

Percent Dose

MC

EPL

DVH for the PTV

Pe

rce

nt

Vo

lum

e

Percent Dose

MC

EPL

DVH for the PTV

Pe

rce

nt

Vo

lum

e

Percent Dose

MC

EPL

Under-dosage of the PTV

Treatment Planning: The main

dosimetric issue

Solid = MC , 100%

Dashed = EPL, 100%

Blue = PTV

6 MV oblique fields

TG-105, courtesy of I.Chetty31

CyberKnife dose calculation issues

• Two dose calculation algorithms:

• Ray tracing or EPL (path length correction) algorithm

– no corrections for changes in electron transport or

lateral scatter disequilibrium that may develop in the

presence of low-density heterogeneities.

• Monte Carlo algorithm - based MCDOSE (Ma et al 2002

and 2008)

32

Recommendations Van der Voort van Zyp et al

Radiotherapy and Oncology 96 (2010) 55–60

• The EPL algorithm overestimates the actual delivered dose

• Dose reduction with MC depended on tumor size and

location

• Separate prescription dose according to tumor size

• Recommendation for peripheral tumors

– 3 x 16 Gy for tumors <3 cm,

– 3 x 17 Gy for tumors of 3–5 cm

– 3 x 18 Gy for tumors >5 cm

• Central tumors- no recommendation given yet - longer

follow-up needed

33

• MC-calculated doses can in some instances be significantly

different (10-20%) from conventional algorithms, such as

radiological path length, and convolution-based methods

• In light of these differences:

- should dose prescriptions change with MC-based

calculations ?

- How?

Dose prescription issues - summary

34

Dose prescription - AAPM TG-105

perspective

• MC method is just a more accurate dose algorithm

• Dose prescription issues are not specific to MC-based

dose calculation

• As with other changes to the therapy treatment process,

users should correlate doses and prescriptions with respect to

previous clinical experience

35

Conclusions

• Clinical implementation of MC-based systems must be performed

thoughtfully and users, especially physicists, must understand the

differences between MC-based and conventional dose algorithms

• Successful implementation of clinical MC algorithms requires strong

support from the clinical team and an understanding of the paradigm

shift with MC algorithms

• A properly commissioned MC-based dose algorithm will improve dose

calculation accuracy for electron and photon beams

• More accurate dose calculations

may improve dose-biological effect correlations

Lead to prescription changes in some cases

Acknowledgements

George X. Ding Indrin Chetty and other co-authors of TG-105

George Daskalov Margarida Fragoso

Charlie Ma Neelam Tyagi

Eric Vandervoort Ekaterina Tchistiakova

Junior Akunzi David W.O. Rogers

In the past I have received research support from Nucletron, Varian and Elekta.

TOHCC has a research agreement with Elekta.

Thank You

38

Monte-Carlo Settings: Effect on

computation time

Timing Results XiO TPS:

For 9 and 17 MeV beams, 10x10

cm2 applicator and the trachea

and spine phantom, timing tests

were performed for a clinical XiO

Linux workstation, which employs

8 processors, 3 GHz each, with

8.29 GB of RAM.

y = 3.4x-2.0

y = 6.4x-2.1

y = 0.7x-2.0

y = 0.4x-2.0

0

5

10

15

20

25

30

0 0.5 1 1.5 2 2.5

MRSU %

tim

e /

min

9 MeV 2.5 mm voxel

17 MeV 2.5 mm voxel

17 MeV 5 mm voxel

9MeV 5 mm voxel

Cygler, J.E., and Ding, G.X., in Monte Carlo Techniques in Radiation Therapy,

ISBN-10: 1466507926, Taylor & Francis (CRC Press INC ) Boca Raton 2013, p 155-16639

Timing – Nucletron TPS

Oncentra 4.0

4 MeV Timer Results:

Init = 0.321443 seconds

Calc = 42.188 seconds

Fini = 0.00158201 seconds

Sum = 42.5111 seconds

20 MeV Timer Results:

Init = 0.311014 seconds

Calc = 110.492 seconds

Fini = 0.00122603 seconds

Sum = 110.805 seconds

Anatomy - 201 CT slices

Voxels 3 mm3

10x10 cm2 applicator

50k histories/cm2

Faster than pencil beam!40

Timing – Varian Eclipse

Eclipse MMC, Varian single CPU Pentium IV

XEON, 2.4 GHz

10x10 cm2, applicator, water phantom,

cubic voxels of 5.0 mm sides

6, 12, 18 MeV electrons,

3, 4, 4 minutes, respectively

Chetty et al.: AAPM Task Group Report No. 105: Monte Carlo-based

treatment planning, Med. Phys. 34, 4818-4853, 200741

Selected references

1. Kawrakow, I., M. Fippel, and K. Friedrich. (1996), 3D electron dose

calculation using a Voxel based Monte Carlo algorithm (VMC). Med Phys

23 (4):445-57.

2. Kawrakow, I. “VMC++ electron and photon Monte Carlo calculations

optimized for radiation treatment planning”, Proceedings of the Monte

Carlo 2000 Meeting, (Springer, Berlin, 2001) pp229-236.

3. Neuenschwander H and Born E J 1992 A Macro Monte Carlo method for

electron beam dose calculations Phys. Med. Biol. 37 107 – 125.

4. Neuenschwander H, Mackie T R and Reckwerdt P J 1995 MMC—a high-

performance Monte Carlo code for electron

beam treatment planning Phys. Med. Biol. 40 543–74.

5. Janssen, J. J., E. W. Korevaar, L. J. van Battum, P. R. Storchi, and H.

Huizenga. (2001). “A model to determine the initial phase-space of a

clinical electron beam from measured beam data.” Phys Med Biol

46:269–286.

Selected references cont.

6. Traneus, E., A. Ahnesjö, M. Åsell.(2001) “Application and Verification

of a Coupled Multi-Source Electron Beam Model for Monte Carlo

Based Treatment Planning,” Radiotherapy and Oncology, 61, Suppl.1,

S102.

7 Cygler, J. E., G. M. Daskalov, and G. H. Chan, G.X. Ding. (2004).

“Evaluation of the first commercial Monte Carlo dose calculation

engine for electron beam treatment planning.” Med Phys 31:142-153.

8 Ding, G. X., D. M. Duggan, C. W. Coffey, P. Shokrani, and J. E.

Cygler. (2006). “First Macro Monte Carlo based commercial dose

calculation module for electron beam treatment planning-new issues

for clinical consideration.” Phys. Med. Biol. 51 (2006) 2781-2799.

9. Popple, RA., Weinberg, R., Antolak, J., (2006) “Comprehensive

evaluation of a commercial macro Monte Carlo electron dose

calculation implementation using a standard verification data set”. Med Phys 33:1540-1551.


10. B.A. Faddegon, J.E. Cygler: “Use of Monte Carlo Method in Accelerator

Head Simulation and Modelling for Electron Beams”, Integrating New

Technologies into Clinic: Monte Carlo and Image-Guided Radiation Therapy,

AAPM Monograph No. 32, edited by B.H. Curran, J.M. Balter, I.J. Chetty,

Medical Physics Publishing (Madison, WI, 2006) P.51-69.

11. J.E. Cygler, E. Heath, G.X. Ding, J.P. Seuntjens: “Monte Carlo Systems in

Preclinical and Clinical Treatment Planning: Pitfalls and Triumphs”, Integrating New Technologies into Clinic: Monte Carlo and Image-Guided

Radiation Therapy Monograph No. 32, edited by B.H. Curran, J.M. Balter,

I.J. Chetty, Medical Physics Publishing (Madison WI, 2006) p.199-232.

12. I. Chetty, B. Curran, J.E. Cygler et al.,(2007) Report of the AAPM Task

Group No. 105: Issues associated with clinical implementation of Monte

Carlo-based photon and electron external beam treatment planning. Med

Phys 34, 4818-4853.


13. Reynaert, N., S. C. van der Marck, D. R. Schaart, et al. 2007. Monte Carlo

treatment planning for photon and electron beams . Radiat Phys Chem 76:

643–86.. Radiat Phys Chem 76: 643–86.

14. Fragoso, M., Pillai, S., Solberg, T.D., Chetty, I., (2008) “Experimental verification

and clinical implementation of a commercial Monte Carlo electron beam dose

calculation algorithm”. Med Phys 35:1028-1038.

15. Edimo, P., et al., (2009) Evaluation of a commercial VMC++ Monte Carlo based

treatment planning system for electron beams using EGSnrc/BEAMnrc

simulations and measurements. Phys Med, 25(3): 111-21.

16. J.E. Cygler and G.X. Ding, “Electrons: Clinical Considerations and Applications

“ in Monte Carlo Techniques in Radiation Therapy, ISBN-10: 1466507926,

Taylor & Francis (CRC Press INC ) Boca Raton 2013, p 155-166

17. M. K. Fix, J. E. Cygler, D. Frei,W. Volken, H. Neuenschwander, E.J. Born and P.

Manser, (2013), Generalized eMC implementation for Monte Carlo dose

calculation of electron beams from different machine types, Phys. Med. Biol.

58, 2841–2859,

18. 4. E.J. Vandervoort, E. Tchistiakova, D.J. La Russa, J.E. Cygler. Evaluation of a

new commercial Monte Carlo dose calculation algorithm for electron beams,

Med. Phys. 41 (2), http://dx.doi.org/10.1118/1.4853375 (8 pages), 2014

Documents

Monte Carlo based treatment planning systems - …acquaviva.com.br/CBFM2015/trabalhos/CBFM_Palestras… · · 2015-09-15Monte Carlo based treatment planning systems Joanna E. Cygler,