current imrt optimization algorithms: principles ... · – optimization techniques • outlook:...

Harvard Harvard Medical SchoolMedical School

Current IMRT Optimization Algorithms:Current IMRT Optimization Algorithms:Principles, Potential and LimitationsPrinciples, Potential and Limitations

TThomashomas Bortfeld Bortfeld et al.et al.

Mass. General HospitalMass. General HospitalNortheast Proton Therapy CenterNortheast Proton Therapy Center30 Fruit St, Boston 0211430 Fruit St, Boston 02114ee--mail: mail: [email protected]@partners.org


•• IMRT: Why and HowIMRT: Why and How•• Current Optimization AlgorithmsCurrent Optimization Algorithms

–– VariablesVariables–– Objective FunctionsObjective Functions–– Optimization TechniquesOptimization Techniques

•• Outlook: Potential for Improvements, Outlook: Potential for Improvements, ChallengesChallenges


Crossfireirradiation withthree beams

BasicsBasics


Problem: Tumor with concave regions

Tumor

CriticalstructureSolution: IMRT

IMRT PrinciplesIMRT Principles


TreatedVolume

Tumor Tumor

OAR

TargetVolume

Intensity Modulation

TreatedVolume

OAR

Target Volume

Collimator

"Classical" Conformation



TreatedVolume

OAR

Target Volume

Collimator

TreatedVolume

OAR

Target Volume

Inverse Planning"Conventional" Planning




•• Outlook: Potential for ImprovementsOutlook: Potential for Improvements


Treatment Parameters to be OptimizedTreatment Parameters to be Optimized

•• Intensity maps for each beamIntensity maps for each beam•• or: weights of field segmentsor: weights of field segments

•• Beam angles (gantry angle, table angle)Beam angles (gantry angle, table angle)•• Number of beamsNumber of beams•• Energy (especially in charged particle therapy)Energy (especially in charged particle therapy)•• Type of radiation (photons, electrons, ...)Type of radiation (photons, electrons, ...)


Calculated Fluence(Intensity map)

Treatment ParametersTreatment Parameters


1

654

32

+

+++

+

Field Segments Shaped with a Field Segments Shaped with a MultileafMultileaf Collimator (MLC) Collimator (MLC)


IMRT with MLCIMRT with MLC

© Dept. of Medical PhysicsDKFZ Heidelberg


•• Consider leaf sequencing as an optimization Consider leaf sequencing as an optimization problemproblem–– Find the sequence that requires the smallest Find the sequence that requires the smallest

number of segments at the minimum number of number of segments at the minimum number of total total MUsMUs

–– M. Langer: “Improved Leaf Sequencing reduces M. Langer: “Improved Leaf Sequencing reduces Segments or Monitor Units Needed to Deliver IMRT Segments or Monitor Units Needed to Deliver IMRT using using Multileaf Multileaf Collimators”Collimators”Med. Phys, 2002Med. Phys, 2002


Alternative: Weight Optimization of Field SegmentsAlternative: Weight Optimization of Field Segments

•• Determine a number of field segments based Determine a number of field segments based on anatomical considerationson anatomical considerations

•• Optimize weights of segmentsOptimize weights of segments–– Ann Arbor groupAnn Arbor group–– W. DeW. De NeveNeve et al.et al.–– J. Galvin et al.J. Galvin et al.


Clinical Data

OptimizedPlanning

BiologicalModels(TCP, NTCP)

PhysicalCriteria

(Dmin, Dmax, DVH)


Physical optimization:Based on physical parameters (dose, volume) • Target dose close to prescribed dose• Dose in OARs within tolerance• DVH constraints• Conformal dose distribution

Biological optimization:Based on biological models (TCP, NTCP)• Maximize TCP for given NTCP• Maximize P+


Objective Functions Objective Functions -- ExampleExample

C aa a

+ =≥

{ }forotherwise

00

Constraint operator, e.g.:

weight dose atvoxel j

in OAR k

tolerancedose

( )F x w C D x Dk k k j kj

Nk

( ) { ( ) },maxr r

= −+=

∑2

1


Small weight (w)

Vol

ume

DoseDmax

Large weight (w)DVH

Vol

ume

Dmax

Dose

DVH


Vol

ume

Dose

DVH

Dmax

Dmin


Vol

ume

Dose

DVH

Dmax

Vmax

DVH based optimizationDVH based optimization


Smoothing intensity mapsSmoothing intensity maps

•• Include smoothness term in objective functionInclude smoothness term in objective function–– M.M. AlberAlber, F., F. NusslinNusslin: “Intensity Modulated Photon Beams : “Intensity Modulated Photon Beams

Subject to a Minimal Surface Constraint”,Subject to a Minimal Surface Constraint”,Phys. Med. Biol. 45, N49Phys. Med. Biol. 45, N49--N52, 2000N52, 2000

•• Apply smoothing filter during optimizationApply smoothing filter during optimization–– S. Webb: “Inverse Planning with Constraints to Generate S. Webb: “Inverse Planning with Constraints to Generate

smoothed intensitysmoothed intensity--modulated beams”, Phys. Med. Biol. 43, modulated beams”, Phys. Med. Biol. 43, 27852785--2794, 19982794, 1998

–– A. A. KessenKessen, K., K.--H. Grosser, T. H. Grosser, T. BortfeldBortfeld: “Simplification of : “Simplification of IMRT intensity maps by means of 1IMRT intensity maps by means of 1--D and 2D and 2--D median D median filtering...”,filtering...”,Proceedings of ICCR 2000Proceedings of ICCR 2000


Smoothing intensity maps

Original After smoothing

Dose distributions are almost identical


Optimization TechniquesOptimization Techniques

•• Deterministic TechniquesDeterministic Techniques–– GradientGradient–– Conjugate gradientConjugate gradient–– Linear programmingLinear programming–– Maximum likelihoodMaximum likelihood–– ......

•• Techniques Based on RandomTechniques Based on Random–– Simulated annealingSimulated annealing–– Genetic algorithmsGenetic algorithms–– ......


Why do gradientWhy do gradient--like techniques work?like techniques work?

•• Simple objective functions have no local minimaSimple objective functions have no local minima

•• Selecting a good initial guess avoids Selecting a good initial guess avoids local minimalocal minima

•• Local minima are not much worse than the Local minima are not much worse than the global optimumglobal optimum




•• Outlook: Potential for Improvements, Outlook: Potential for Improvements, ChallengesChallenges


Potential for Improvements, ChallengesPotential for Improvements, Challenges

1.1. Directly optimize segment shapes and weightsDirectly optimize segment shapes and weights(aperture(aperture--based optimization)based optimization)

2.2. More relevant objectives, multiMore relevant objectives, multi--criteria criteria optimizationoptimization

3.3. MotionMotion--forgiving plansforgiving plans4.4. More accurate dose calculationMore accurate dose calculation5.5. Proton IMRTProton IMRT


Challenge 1: Current IMRT approach (Divide and Conquer)Challenge 1: Current IMRT approach (Divide and Conquer)

Clinical Objectives, Constraints

Intensity Maps

MLC Segments1

654

32


Challenge 1: Aperture IMRTChallenge 1: Aperture IMRT

Clinical Objectives, Constraints

MLC Segments1

654

32


Challenge 2: MultiChallenge 2: Multi--Criteria OptimizationCriteria Optimization

K o n R a d

1

2

3

4

0

20

40

60

80

100

0 20 40 60 80 100 120

Dose (%)

Vo

lum

e (

%)

Lunge

Herz

Target

80 30 50 70 25 45 60 20 40 56 15 35 Target Lunge Herz

ITWM Kaiserslautern, DKFZ Heidelberg, Germany

Target Lung Heart


Challenge 3: MotionChallenge 3: Motion--forgiving IMRTforgiving IMRT

JH Kung and P Zygmanski, MGH, 2000

static beamIMRT


Challenge 4: More Accurate Dose CalculationChallenge 4: More Accurate Dose Calculation

•• Correction approachCorrection approach–– J.J. SiebersSiebers et al.: “Acceleration of dose calculations for et al.: “Acceleration of dose calculations for

intensityintensity--modulated radiotherapy”, Med. Phys. 28, 903modulated radiotherapy”, Med. Phys. 28, 903--910, 910, 20012001

•• PrePre--calculate the dose contribution of each calculate the dose contribution of each bixel bixel to each to each voxelvoxel–– P. Cho, M. Phillips: “Reduction of computational P. Cho, M. Phillips: “Reduction of computational

dimensionality in inverse radiotherapy planning using sparse dimensionality in inverse radiotherapy planning using sparse matrix operations”, Phys. Med. Biol. 46, N117matrix operations”, Phys. Med. Biol. 46, N117--N125, 2001N125, 2001

–– C. C. ThiekeThieke et al., “Acceleration of IMRT dose calculation by et al., “Acceleration of IMRT dose calculation by importance sampling of the calculation matrices”, Med. importance sampling of the calculation matrices”, Med. Phys. 2002 Phys. 2002


Challenge 5: Proton IMRT (IMPT)Challenge 5: Proton IMRT (IMPT)

Photon IMRT Proton IMPT


Potential for Improvements, ChallengesPotential for Improvements, Challenges

1.1. Directly optimize segment shapes and weightsDirectly optimize segment shapes and weights(aperture(aperture--based optimization)based optimization)

2.2. More relevant objectives, multiMore relevant objectives, multi--criteria criteria optimizationoptimization

3.3. MotionMotion--forgiving plansforgiving plans4.4. More accurate dose calculationMore accurate dose calculation5.5. Proton IMRTProton IMRT

current imrt optimization algorithms: principles ... · – optimization techniques • outlook:...

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