IMRT Inverse Planning Flashcards
How does forward planning work?
Planner specifies beams
TPS calculates dose distribution
Planner assesses plan and alters beams in iterative process
How does inverse planning work?
Planner specifies dose distrubtion
TPS calculates beam profiles, iterates using cost function
Planner assesses plan and alters dose limits/ constraints if necessary
What is a cost function?
Summarise the merit of the plan into a single figure used to drive optimisation. Usually uses dose, interest on basing it on biological things like TCP
When is a cost function zero?
For a plan where all objectives are met
How long do you let TPS iterate?
Could set x number of iterations
Could stop when iterations are barely changing cost function
What are objectives vs constraints?
Constraints must be achieved
Objectives are things that would be good
Remember, don’t need to use clinical goals, could change dose instead of penalt.
What are examples of physical constraints?
Minimum MU, maximum leaf speed
Example of cost function equation
C = sum Pi (Di (x) - Dp)^2
Pi is penalty, Di and Dp are calculated and prescribed doses
What kind of optimisation algorithms can be used?
Analytical techniques (eg inverse CT)
Iterative techniques (1. deterministic methods, 2 stochastic methods)
What is deterministic method and an example of it?
A deterministic method gives you the same result for the same input
Output determined by parameter values and initial conditions
An example is the gradient method
How does gradient method work?
Follows path of gradients to minima, but can end up in local minima.
What are stochastic methods and give an example
A method where the same result may not come from the same input, eg simulated annealing.
How does simulated annealing work?
Step size and direction are random, explore the whole space to find global minima. Requires more steps. Start with large ones and get smaller, steps can increase the cost function with certain probability. Size of step and probability of accepting increase cost function decrease over time
Beamlet based optimisation
Field split into beamlets with fluence and corresponding dose distributions computed
Beamlet weights optimised to produce optimal fluence map
Optimal fluence map translated into deliverable segments (S+S) or leaf trajectory (dynamic)
Final dose distribution, slightly degraded vs optimal
Aperture based optimisation
Leaf segmentation step is eliminated
Segment shapes and weights are optimised together
Takes MLC limitations into account during optimisation
Fewer vs more levels in optimisation
Fewer, less segments, greater efficiency
More, closer to optimum but less efficient (slower to deliver)
Why might we not just want biological optimisation?
TPS can do things we aren’t happy with, give extremely high dose to the PTV, don’t have clinical evidence that this is not harmful
Things to consider when planning
Beam arrangement
Help/tuning volumes
Dose prescription
Planning with class solutions
Planning with class solutions?
Set of inverse planning parameters used for patients with disease at certain staging
EG, outline these volumes, use this number of beams at this orientation and optimise using this
Help/tuning volumes
What are overlap regions assigned to?
Clip PTV from skin
Extent fluence for breathing motion
Dummy volumes to prevent hotspots
Subvolumes to deal with known conflicts
Dose prescription planning considerations
Normalise to volume rather than point
Report near min and near max not absolute
Beam arrangement planning considerations
Odd number of evenly spaced beams avoids overlap
Beam direction less important as beam numbers increased
Coming in through an OAR may give a better plan
Choice of beam energy has little impact on optimisation - above 10MV rarely used
What are some advanced planning tools?
Multi criteria optimisation
Knowledge based planning
Robust optimisation
Arc duplication
When is a treatment plan pareto optimal?
When there is no other plan that is at least as good in all objective functions and strictly better in one objective function
