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TP 2 > IMRT > Flashcards

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
Q

QA x4

A
  • 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
Q

knowledge based planning

A

allows treatment planning systems to learn from a database of best plans and extrapolate to new pts

27
Q

advantages of dynamic MLCs

A

efficient delivery of highly modulated fields

  • less treatment time
  • finer modulation resolution (less errors)
  • more degrees of freedom (soln always found)
28
Q

MLC leaf sequencing

A

-tells the mlcs how to move in order to deliver given fluence

29
Q

PRV

A

planning OAR volume

-margins added to the oar to compensate for uncertainty/variation in OAR position

30
Q

RVR

A

remaining volume at risk

-diff between external contour of the pt and the CTVs/OARs on the slice that have been imaged

31
Q

RVR

A

remaining volume at risk

-diff between external contour of the pt and the CTVs/OARs on the slice that have been imaged

32
Q

typical energy used at CCMB for imrt

A

6MV

>10MV due to neutron contamination

33
Q

what are some things that need to be avoided

A

POP - minimize overlap

  • attenuating structures -bed rails, metal
  • avoid beams through oars
34
Q

what are things that the optimizer needs to know?

A

-type of objective - upper vs lower
-NTO
clinical goal dose
-priority

35
Q

fluence smoothing

A
  • high frequency noise is produced during optimization
  • smoothing helps remove noise
  • noise increases modulation, MU, can strain MLC delivery
36
Q

what happens when there is too little smoothing

A

fluence is noisy
increase in MU
decrease deliverability of the plan

37
Q

what happens if there is too much smoothing

A

-dose gradient is affected
-effects dose to OARs
slow dose fall of to normal tissues

38
Q

interactive optimization

A
  • 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
Q

leaf motion calculator

A
  • 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
Q

what can be used to evaluate your plan

A

DVH
dose color wash/isodose lines
Dmax

Look for: comformality, coverage, dose to OARs, hot/cold spots,, low dose region

TP 2 (4 decks)

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