IMRT Flashcards

1
Q

Define IMRT

A

intensity modulated RT

  • uses mlcs to create non-uniform fluences for any fields to create uniform dose distribution within the target that spares critical structures simultaneously
  • assigns non uniform intensities to beamlets/rays
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2
Q

difference between 3DCRT and IMRT

A

3d -target coverage using uniform beam

imrt - uses same tools as 3d but intensities are diff within each beam

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3
Q

beamlet

A

segments of a beam

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4
Q

forward planning vs inverse planning

A

forward - design shapes manually

inverse - begin with desired dose

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5
Q

x5 steps inverse planning

A
  1. contour structures, add margins, create opti structures
  2. choose number of configurations of beams/arcs
  3. define prescription/optimization objectives
  4. computer optimization
  5. plan evaluation
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6
Q

reducing irradiated volume outside target allows? x4

A
  • higher dose to tumour vol
  • decreased OAR, and risks
  • large fields and boosts can be integrated into a plan
  • sharper fall off beyond PTV
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7
Q

IMRT initial disadvantages x5

A
  • specialized equipment
  • non-intuitive intensity maps
  • involves lots of QA**
  • longer tx times
  • more wear and tear on machine
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8
Q

IMRT process (flowchart) x10 points x3 categoires

A

IMAGING AND PREPLANNING
-immobilization, CT sim/imaging, target organ delineation
OPTIMIZATION
-specifiy objective function and beam, optimization, dose calc
QA AND DELIVERY
leaf sequence generation, dosimetric verification of beams, patient set up, i/ex vivo dosimetry

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9
Q

Opti-structures

A
  • structures that are made due to competing objectives
  • for example if theres overlap between 2 structures that causes competing objectives the overlap is integrated into one of the structures
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10
Q

NTO

A

normal tissue objective

-designed to increase conformity of prescription isodoses - eliminates dose dumping

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11
Q

Target and OAR objectives

A

-objectives in the form of DVH metrics (max/min/mean dose, dose volume objectives)

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12
Q

Fluence objectives

A

-objectives designed to eliminate unnecessary fluence modulation

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13
Q

clinical objectives

A

-parameter to characterize the dose distribution if the computer is to find the best plan

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14
Q

characteristics of a good plan x4

A
  • adequate tumour dose homogeneity
  • normal structures are spared
  • deliverable by machine
  • possible to QA
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15
Q

objective function and cost function

A
  • both are measurements of quality of a plan

- cost function - measure of how much the actual plan deviates from the desired plan

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16
Q

step 4 of IMRT categories x2

A

fluence based optimization

direct aperture optimization

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17
Q

Direct Aperture optimization

A

where the beamlet apertures and weights (ie beam-on times) are optimized simultaneously

18
Q

step 4 optimization flow chart

A
  • select the initial beamlet weights
  • compute dose distributions
  • determine updated factors
  • recompute dose distribution
  • acceptable? yes or no

if no go back to determining factors

19
Q

fluence based optimization x2 steps

A
  • find the optimal fluence

- convert optimal fluence to deliverable fluence

20
Q

minimization methods; stochastic vs deterministic

A

stochastic: elements of randomness (same conditions may not mean same solution); can escape the local minima to find global minima
deterministic: same initial conditions = same solution; downhill techniques; gets trapped in minima

21
Q

interactive inverse planning

A
  • where planners job is to figure out what objectives and weights will lead to optimal beams rather than trying to find the optimal beams directly through DVH
  • it allows for the planner to see how adjustments are affecting the optimization process
22
Q

step and shoot/static mlc

A
  • leaves do not move when beam is on

- leaf motion and the radiation are executed sequentially

23
Q

dynamic MLC

A
  • leaves move when the beam is on

- leaves motion and radiation are controlled separately and can be executed simultaneously

24
Q

advantages of static mlcs x5

A
  • easy to understand
  • easy to resume and interuppt
  • relatively simple linac control system
  • can verify individual segments
  • fewer MUs than DMLC
25
QA x4
- hand calcs no longer possible because of complexity - deliver clinical plan to water phantom - measure dose distribution with film and point dose with ion chamber - compare to phantom plan generated on the planning computer
26
knowledge based planning
allows treatment planning systems to learn from a database of best plans and extrapolate to new pts
27
advantages of dynamic MLCs
efficient delivery of highly modulated fields - less treatment time - finer modulation resolution (less errors) - more degrees of freedom (soln always found)
28
MLC leaf sequencing
-tells the mlcs how to move in order to deliver given fluence
29
PRV
planning OAR volume | -margins added to the oar to compensate for uncertainty/variation in OAR position
30
RVR
remaining volume at risk | -diff between external contour of the pt and the CTVs/OARs on the slice that have been imaged
31
RVR
remaining volume at risk | -diff between external contour of the pt and the CTVs/OARs on the slice that have been imaged
32
typical energy used at CCMB for imrt
6MV | >10MV due to neutron contamination
33
what are some things that need to be avoided
POP - minimize overlap - attenuating structures -bed rails, metal - avoid beams through oars
34
what are things that the optimizer needs to know?
-type of objective - upper vs lower -NTO clinical goal dose -priority
35
fluence smoothing
- high frequency noise is produced during optimization - smoothing helps remove noise - noise increases modulation, MU, can strain MLC delivery
36
what happens when there is too little smoothing
fluence is noisy increase in MU decrease deliverability of the plan
37
what happens if there is too much smoothing
-dose gradient is affected -effects dose to OARs slow dose fall of to normal tissues
38
interactive optimization
- makes adjustments as optimization occurs - can observe the progress and modify the dose objectives in real time - helps you make clinical tradeoffs as the plan evolves
39
leaf motion calculator
- done after the optimal fluence is found - we now need to deliver the fluence using the MLCs - takes into account dosimetric characteristics of MLCs, limits of the MLCs, actual fluence that will be delivered, MU
40
what can be used to evaluate your plan
DVH dose color wash/isodose lines Dmax Look for: comformality, coverage, dose to OARs, hot/cold spots,, low dose region