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Basics of treatment planning III

Sastry Vedam PhD DABR

Introduction to Medical Physics III: Therapy Spring 2015

IMRT and Inverse Planning

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Compensators

Dynamic Modulation using Jaws in LINAC

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Static modulation using Jaws

1D

2D

NOMOS MiMIC

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Linear accelerator

The Multileaf collimator

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Static (SMLC) IMRT

Dynamic (DMLC) IMRT

!   Sum of 1D dynamic deliveries

!   Leaves move while radiation is delivered

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Intensity modulated arc therapy (IMAT)

!   In addition to moving the collimators during radiation delivery, gantry also moves

!   Minimize leaf movement between gantry locations

!   Combination of !   Leaf speed

!   Gantry speed

!   Dose rate

Interdigitation and tongue and groove effect

!   When subfields are joined, tongue and groove artefacts lead to underdosing

!   Reduced by factoring in: !   Rotation of

collimator between fields

!   Electron transport

!   Patient motion

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Helical tomotherapy

Accuray Cyberknife

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Physical Basis of IMRT

IMRT basis: The inverse problem

!   D – Dose distribution

!   b – Vector of individual beam weights

!   A – Matric liking each dose-space element to corresponding beam-space element !   Physics of photon tissue interaction !   Can be pre-calculated using simple

equations/Convolution-superposition/Monte Carlo

!   Inverse problem !   Given D and A, can we calculate ‘b’?

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Inverse problem

!   Negative beam weights

!   Require construction, storage and inversion of too large a matrix A

!   It would take too long

!   No method to control the behavior of the beam weight vector ‘b’

Degeneracy

!   Multiple “solutions” to the inverse problem

!   Which solution to accept and which to reject?

!   Global and local minima

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Concept of ‘Cost’

!   I – importance of each voxel

!   Higher importance to PTV voxels – Tumor conformal planning

!   Higher importance to Organs at Risk – Conformal avoidance

Constraints

!   Planning constraints

!   Physics constraints

!   Human constraints

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Optimization

What we want …

!   All types of radiation

!   All energies

!   All possible number of beams

!   All possible number of beam geometries

!   All possible fractionation schemes …

What we can want…

!   Beams available on a machine

!   Number of beams within a reasonable delivery time

!   Delivery mechanics

!   Time available for treatment planning …

Simulated annealing

!   Way to guarantee achievement of local minima

!   Instead of rejecting all changes that lead to an increase in the cost function, accept changes with a probability

!   T – Temperature

!   k – Boltzmann constant

!   Set T to be high initially so that a lot of “wrong way” changes are accepted and then reduce T slowly, so that only downhill changes are accepted as iterations progress

!   T must be lowered slower than the reciprocal of the logarithm of the iteration number

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Simulated annealing

Gradient descent

!   When the cost function is guaranteed to have no local minima

!   Cost function written mathematically and its derivatives exist

!   Advantage – Fast

!   Disadvantage – Can generate negative beam weights and may require a posteriori adjustment !   Setting negative beam weights to zero

!   Addition of constant fluences

!   Disturb the optimization

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Genetic algorithms

!   Based on Darwinian theory of evolution

!   Categories of solutions proposed via an iterative process

!   “Fittest” solutions survive

!   Definition of fitness

!   Mechanism for evolution

! Bixel intensity values can be considered as chromosomes and are exchanged and/or mutated during the evolution process

Biological optimization

!   TCP – Tumor control probability

!   NTCP – Normal tissue complication probability

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Concave and

Convex beams

Clinical IMRT milestones

!   First clinical IMRT (excluding wedges/compensators) – NOMOS MIMiC @ Baylor College of Medicine, Houston 1994

!   First DMLC treatments – Memorial Sloan Kettering Cancer Center, New York 1996

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IMRT delivery techniques