Basics of treatment planning III...Basics of treatment planning III Sastry Vedam PhD DABR...
Transcript of Basics of treatment planning III...Basics of treatment planning III Sastry Vedam PhD DABR...
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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