Monaco Concepts and IMRT / VMAT Planning - Elekta Concepts and IMRT... · 2 | Focus where it...

Monaco Concepts and IMRT / VMAT Planning

LTAMON0003 / 3.0

2 | Focus where it matters

Monaco Concepts and Planning

By the end of this presentation you can:

• Describe the cost functions in Monaco and recognize their application in building a successful plan.

• Explain the sequencing parameters and how they affect planning.

• Produce IMRT and VMAT plans.

Objectives


Advanced Monaco

• Part 1: Monaco Concepts- Constrained Optimization

- Voxels and Structure Layering

- Biological Optimization

- Cost Functions• Serial

• Parallel

• Targets

• Settings

• Part 2: Sequencing Parameters

• Part 3: Monaco Planning

• Part 4: Multicriterial Planning

Contents


Monaco Templates

Monaco is a template-based planning system

What does this mean?• Templates store beam geometries, calculation parameters, calculation

settings, physician’s intent, IMRT constraints, and so on.

• In a few clicks, the plan is ready for calculation.

What are the benefits of template-based planning?• Provides efficient ways to standardize the planning approach.

• With consistent templates, planning VMAT / IMRT is much easier.

• Decreases time to build plans.


Monaco Templates

• Templates can be used to create class solutions; a method of standardizing planning approaches across a whole clinic.

• Templates can be stored by delivery type and anatomical site.

Class Solutions


Monaco Templates

• Templates can be imported and exported from your system with the Manage Templates option. - This allows for template sharing.

- The use of predefined templates is in no way to be construed as Elekta acting in any way to provide medical direction or advice. Full responsibility of the clinical use of shared template(s) resides with the healthcare professional providing patient care services.

Class Solutions


Monaco Templates

• Class solutions are prescription templates you can create for one patient and use on most others for a particular anatomical site.

• Class solutions are possible for the following reasons:- Patient geometries in a population do not vary much.

- Constrained optimization guarantees consistency in most treatment goals.

- Cost functions capture rules for ranking dose distributions correctly.

- The definition of treatment goals is comprehensive.

Class Solutions


Monaco Concepts

• The approach to optimization in Monaco is different to early inverse planning.

• In traditional systems, you create a plan to achieve target coverage then lower doses to organs at risk (OAR’s) as a secondary process until the target coverage is compromised.

• With Monaco, the OAR doses (dose limiting cost functions) are prioritized and will be achieved before dose to targets (objective functions) are met.

• Only when the OAR doses are achieved will Monaco prioritize target objectives.

Constrained Optimization


Monaco Concepts

• There may be limits to the target dose to achieve this.

• If the Target dose has not been met, it will typically be because an OAR or dose limiting cost function is too harsh.

• You have the option to relax or change the cost function or accept the lower target dose.



Monaco ConceptsConstrained Optimization

OAR

OAR

PTV

PTV

Compromise


Monaco Concepts


PTV

PTV

OAR

OAR


Monaco Concepts

• Constrained Optimization is a more structured and logical way to plan.

• As Monaco has completed the OAR constraints problem, Monaco is able to inform where conflicts are and highlight which cost functions are affecting the dose to targets.

• This gives you power in terms of optimization. There is no guess work in what to change to achieve the target objective.

• This is a more structured approach to planning and leads to less iterations.



Monaco Concepts

• Think of constrained optimization as if it were a children's party at feeding time.

• On one side of the Table, we sit the big children, our Targets.

• On the other side, we sit the small children, our OARs.

• We put all the food in the middle and allow the small children to get what they want first.

• When they have their food, we allow the big kids to eat.

Constrained Optimization – The Children's Party


Monaco Concepts

• If one of our big children does not have enough food, we can look at our small children and know which of our small children has too much food and is preventing the big kid from eating.

• We have the decision of taking away from the small child to give to the big child.

• This is how Monaco works. The system gives you the information you need to complete your plan.

Constrained Optimization – The Children's Party


Monaco Concepts

• Its not quite that simple.

• There is an order in which cost function objectives or constraints must be met.

• Some of them (Quadratic Overdose) will be used in conjunction with target coverage and this can add a little confusion.

• So the next slide is important to understand how to plan on Monaco.



Monaco Concepts

1st Order Constraints• Goal will always be met.

• Serial, Parallel, Quadratic Overdose, Max Dose

2nd Order Constraints• Goal will be met UNLESS there is a 1st Order constraint.

• Quadratic Under Dose, Under Dose DVH

1st Order Objective• Goal will be met unless a 1st or 2nd Order Constraints prevents this.

• Target EUD, Target Penalty

2nd Order Objective• Goal will be met or succeeded unless Constraints prevent and UNLESS 1st order

objectives are not met.• Cost functions that have “Multi Criterial” option



Monaco Concepts

• How does constrained optimization make it easier to produce a plan?

• How does Monaco tell us where the conflicts are?

• Lets take a look at an example.



Monaco Concepts

• After Stage 1 calculation look for areas

of high weight and impact. Try to get

these as low as you can. Values around

1 – 2 show a reasonable return in plan

quality in Stage 2.

• Look for areas where there is low weight

and impact. These areas can be

reduced to make the plan work harder.

• When you are happy with Stage 1

calculation, calculate Stage 2.



Monaco ConceptsConstrained Optimization

• The IMRT constraints tab is a key source of information for the planner.

• This is where you can see how your plan is performing.

• Locate conflicts and adjust parameters in order to achieve your goal.

• Most of your planning time will be spent in this tab.


Monaco Concepts

• After the plan has run through to Stage 2, you can review the dose.

• Note how PTVnx is under the required dose.

• Now, use the constrained optimization concept in the IMRT constraints to resolve this.



Monaco Concepts

• Note the Isoeffect for the PTVnx is under the Isoconstraint – this is confirming what we saw in the DVH statistics. The rest of the targets look pretty good and their impact and weight are relatively low.

• This tells us, it is not the targets that are impacting on each others coverage. On the whole, the cost functions (CF’s) in the targets are relatively good.



Monaco Concepts

• The dose limiting cost functions of the Targets are not restricting dose to PTVnx, so we can focus on the OARs.

• Note the high weight and relative impact for the brainstem.

• This is the system informing us that the brainstem is restricting dose to the target.

• This is the indicator from constrained optimization.



Monaco Concepts

• Look closer and you can see the parotids and optic nerves are also a problem.

• In this case, our body constraint is also too tight. Let’s focus on this structure initially.

• We want target coverage but not at the expense of our OAR.



Monaco Concepts

• If we primarily focus on the body quadratic overdose of 54Gy, we can see the high weight of 45.60.

• This cost function is controlling dose to the patient.

• This weight suggests that the body structure comes into close proximity to PTVnx.



Monaco Concepts

• With the constrained optimization concept,

expect the PTVnx dose to increase and the

weight to decrease.

• Once this is done, re-run the calculation.

There is no need to change anything else

at this stage. Doing one step at a time will

speed up the calculation and make it

easier to understand your changes.

• Repeat this step until the weight and

impact has significantly reduced or when

you are no longer happy with the dose to

the body.



Monaco Concepts

• As expected, PTVnx coverage has improved. The weight and relative impact of this cost function has reduced.

• If we look again, we can see the 30Gy quadratic overdose now has a high weight.

• Repeat the process with this cost function.



Monaco Concepts

• To recap:- Dose limiting cost functions will always be achieved.

- Only when these are met will dose to the targets be prioritized.

- Use this to your advantage and follow the clues.

- The weight, isoeffect, and relative impact on the IMRT constraints is your key to identifying conflicts and achieving your planning objectives.



Monaco Concepts

• Monaco is a voxel-based planning system.

• The entire volume is split into tiny voxels.

• The advantage is being able to control voxels and not structures.

• The voxels extend out from the isocenter and are based on the grid size.

• The finer the grid size, the greater the number of voxels.

Voxels and Structure Layering


Monaco Concepts

• By optimizing voxels rather than contours, we reduce the need for

additional ‘help contours’ seen in more primitive systems.

• This gives us simpler prescriptions, a faster overall planning time,

and a more intuitive planning experience. Especially, in

conjunction with constrained optimization.



Monaco Concepts

• Since Monaco is a voxel-based planning system, there are multiple voxel-based tools that also give useful feedback.

• The means the feedback received in constrained optimization is not limited to the IMRT constraints tab.

• This is an example of the “Variation” tool.



Monaco ConceptsVoxels and Structure Layering

Rectum

1. PTV higher in layering

order than Rectum.

PTV

Rectum

2. Rectum higher

in layering order

than PTV.

PTV

3. PTV2 higher in

layering order than

PTV1.

PTV2

PTV1

Rectum

PTV 2a. Rectum

“optimized over

all voxels.”


Monaco Concepts

• Accounts for the response of tissues to dose as well as the volume effect of organs using Equivalent Uniform Dose.

• This means that the biological based cost functions ensure the required dose volume affect is achieved.

• It is a more intuitive way to plan for improved dose distributions compared to the traditional DVH physical point methods.

Biological Optimization

32


Monaco Concepts

• The Equivalent Uniform Dose (EUD) represents the dose that causes the same effect if applied homogeneously to the entire organ volume.

• Stated another way, the EUD represents any two or more dose distributions that yield the same radio-biological effect.

• In Monaco, it is expressed as the Isoconstraint and Isoeffect.

• The Isoconstraint is the EUD you are asking for.

• The Isoeffect is the calculated EUD, or what you are getting.



Monaco Concepts

• EUD is similar to the mathematical concept of an “average”.

• That is why the Isoeffect may or may not match your DVH statistics.

• While you may not be able to evaluate your plan directly using Isoeffect, the concept of EUD is a useful planning tool when combined with the logical concept of constrained optimization.



Monaco Concepts

• To manage this in Monaco, we work on the theory there are two types of biological responses to radiation exhibited by normal tissue.

• Monaco is able to take these responses into account while optimizing to ensure the best possible outcomes for the patient.

• The two dose limiting cost functions are based on two types of organs.



Monaco ConceptsBiological Optimization

Serial OAR’s

“Will not function once any of the volume has received a maximum dose”

Example: Spinal Cord

Parallel OAR’s

“Will not function once a % of the volume has received a specific dose” or

“if they are paired organs where one of the organs will compensate for loss of function in the damaged organ”

Example: Lungs, kidney


Monaco Concepts

• Serial is represented by the chain because when one link breaks, the chain is broken. If you irradiate a small portion to a high dose, the organ no longer functions regardless of the volume that receives a low dose.

• Parallel is represented by the rope because you can break some strands, but it will still function. There is a point where you break too many strands and the rope becomes useless. Parallel organs respond to radiation in the same way.

• There are also organs like the rectum that although technically are serial there is a dose volume response as well so they may have both parallel and serial constraints to take care of the high dose and restrict the volume receiving a certain dose.



Monaco Concepts

• The serial constraint allows the optimizer to put its resources into constraining a max dose to keep hot spots under control but still work to reduce the high dose.


Monaco – Solid Line Other Anonymous TPS - Dashed Line

• This DVH shows use of Monaco Serial constraint on the spinal cord and brainstem vs. another systems plan using maximum dose.

• Both achieve the specified max dose but the Monaco plan spares significantly more cord and brainstem for the same dose to the target.


Monaco Concepts

• The parallel constraint controls the dose to a specified portion of the organ while allowing overlapping target structures to receive the prescribed dose.


• This DVH shows use of Monaco parallel constraint to control the dose to the bladder while maintaining dose to the target and significantly improving the dose to the Gross Tumor Volume (GTV) in this case.

Monaco – Solid Line Other Anonymous TPS - Dashed Line


Monaco Concepts

• In the simplest terms, biological optimization is a much simpler method of controlling and achieving the required DVH.

• We control them changing the EUD and with a power law exponent value.

• Before we come to that though, it is important to understand how the EUD works for both cost functions.- For target EUD, target penalty, and serial cost functions, the

isoconstraint/effect is a dose unit.

- For parallel cost function, the Isoconstraint/effect is a volume.



Monaco Concepts

• To control the serial, you can change the EUD

value. Remember the EUD value is not a max

dose, therefore the value set may not give you

the max you require – but more the dose effect.

Cost Functions – Serial

EUD 32 Gy EUD 36 Gy EUD 40 Gy

PTV 70 Gy

(k = 12 for this example) Slide information courtesy of M. Alber -2006


Monaco ConceptsCost Functions – Serial

Decreasing the EUD (Isoconstraint) will decrease the Maximum Dose



Decreasing the EUD (Isoconstraint) will decrease the Maximum Dose


Monaco Concepts

• This diagram shows a common effect of a physical cost function in other treatment planning systems.

• This can be achieved with physical cost functions within Monaco.

• But as we will learn, the biological cost functions prove a more powerful planning tool.


44

Maximum Dose Cost Function

(Controls only a single point on DVH)

Spinal Cord < 45 Gy

45 Gy

Vo

lum

e


Monaco Concepts

• With one cost function, we are controlling much more of the DVH.

• By selecting a suitable EUD value, we are able to achieve the max dose effect and impact on the lower doses in the volume.


45

EUD-based Serial Cost Function

(Controls many points on DVH, emphasis

on high doses)

dose

Vo

lum

e

Spinal Cord

EUD < 35 Gy


Monaco Concepts

• The voxel-based tool ‘Variation’ shows where the cost function is applied and you can see the effect upon the DVH and where the cost function is being applied.



Monaco Concepts

• You have seen how the EUD effects the cost function, but the real strength comes from the Power Law Exponent (PLE) or ‘K’ Value.

• The K value (PLE) works different for the serial and parallel.

• The K value (PLE) for the serial cost function ranges from 1 through 20.



Monaco ConceptsCost Functions – Serial • A Power Law Exponent (PLE) or K value of 1

will apply evenly across the whole curve.

• This will give a mean dose effect.

48



• A Power Law Exponent (PLE) or K value of 10 will apply more towards the maximum end of the curve.

49



• A Power Law Exponent or K value of 20 will exhibit much more of a maximum dose effect.

50


Monaco Concepts

• The Variation tool shows where the cost function is applying.

• The image below shows the larynx.

• As you can see, the CF is applying evenly across the structure – giving the mean dose.



Monaco Concepts

• Similarly, the variation will show where the cost function is applying.

• Note how it applies more towards the high dose Planning Target Volume (PTV).



Monaco Concepts

• A Monaco parallel cost function applies over a much greater area giving you much more control over the DVH curve.

Cost Functions – Parallel

doseV

olu

me

Parallel Model Cost Function(Controls many points on DVH, emphasis

on mean dose)

30 Gy

50 %

Biological Optimization – Parallel

54


Monaco Concepts

• The Power Law Exponent or K value for the parallel affects the cost function different to the serial cost function.

• The values range from 1 - 4 and there can be a decimal increase.



Monaco Concepts

• This is an example of a physical cost function.

• Note how it is only applying to one single point and we have no control above and below this value.


doseV

olu

me

DVH Cost Function(Physical - Controls only a single point on DVH)

50 % of parotid < 30 Gy

30 Gy

50 %


Monaco ConceptsCost Functions – Parallel • In this example, we have asked for 30Gy at

50% with a K value of 1. The low K value applied evenly across the whole curve.

57

Vo

lum

e

dose

50 %

30 Gy


Monaco ConceptsCost Functions – Parallel • A medium K value will apply more towards

the values you have set but still has some control over the higher and lower doses.

58

Vo

lum

e

dose

50 %

30 Gy


Monaco ConceptsCost Functions – Parallel

• A high K value will apply more directly on the value you

have selected and will not have any control above and

below the selected values.

• It then starts to act more like a physical cost function.

59

Vo

lum

e

dose

50 %

30 Gy


Monaco Concepts

• The parallel can be tricky to use at times.

• If you use a low K value, be sure to compensate for that effect by reducing the values you require as the cost function will apply above and below the values.

• This will often incur a penalty on the target.

- Although this will be highlighted by high weight and relative impact in the IMRT constraints tab.


50

%

30Gy


Monaco Concepts

• Ref dose 3500, mean Organ damage 40%, Power Law Exponent 4.0.

• Higher K value gives better target coverage.



Monaco Concepts

• Ref dose 3500, mean Organ damage 40%, Power Law Exponent (K value) 1.0.

• Low K gives better mean dose, but worse target coverage, because it works on entire curve, including high dose area.



Monaco Concepts

• The Variation tool shows the effect of the cost function within the structure and DVH.



Monaco Concepts

• Remember one rule does not fit all.

• The biological cost functions are a way to better control the DVH.

• Think about the effect you want, then assign the cost function with an EUD and a K value that will achieve it.

Cost Functions


Monaco Concepts

• There are two Target cost functions:- Biological cost function named Target EUD

- Physical cost function named Target Penalty

• They react differently and these will be discussed next.

Cost Functions – Targets


Monaco Concepts

• Equivalent uniform dose (EUD) is the absorbed dose that, when homogeneously given to a tumor, yields the same mean surviving clonogen number as the given non-homogeneous irradiation.

• It allows the optimizer flexibility when searching for a good overall solution.

Cost Functions – Target EUD

• Can be controlled by cell sensitivity.

Increasing cell sensitivity decreases

the volume that is allowed to be

below the Prescription, but may not

be achievable depending on other

(hard) constraints.


Monaco Concepts

• Target Penalty is a physical cost function.

• It is an objective version of the Quadratic Underdose.

• It is a quadratic penalty constraint which starts at the threshold dose.

Cost Functions – Target Penalty

• The iso effect is a DVH-based physical parameter.

• Can result in a steeper Target DVH than an EUD-based cost function.


Monaco Concepts

• There are different scenarios where both cost functions perform well.

• If the target is simple with minimal OAR, then the Target EUD works well. It also performs well in SBRT plans where a target dose max is not a requirement.

• If the plan requires more complexity, then the Target Penalty is the stronger performing cost function.

• The Target Penalty can also be worked harder by increasing the minimum volume %.

Cost Functions – Targets


Monaco Concepts

• The Quadratic Overdose allows a Max dose and an RMS excess to be set.

• The RMS is really just a dose tolerance.

• It is much more flexible than a hard max and gives the system room to breath.

Cost Functions – Quadratic Overdose


Monaco ConceptsCost Functions – Quadratic Overdose

This shows the difference between using

a Target Penalty with and without a

Quadratic Overdose controlling hot

spots.


Monaco Concepts

• The Quadratic Overdose is a powerful cost function that can be used to control multiple areas of the plan.

• The RMS is a key value to allow flexibility.

• Remember you will know if the values you have set are working because the weight and relative impact from constrained optimization will tell you.

Cost Functions – Quadratic Overdose


Monaco Concepts

• Maximum Dose is a hard constraint.

• No penalty is applied until the threshold dose.

• Very inflexible when compared to Biological cost functions.

• Can be used to control the global max when applied to the external contour and optimize over all voxels is selected.

Cost Functions – Maximum Dose


Monaco Concepts

• Monte Carlo can result in more small hot spots.

• Try not to be too harsh on these, remember it is a more accurate representation of actual patient dose.

• Most protocols are moving towards a volume maximum rather than a point dose max because of this.

Cost Functions – Monte Carlo


Monaco Concepts

• “Although the appropriate range for use is 0-3 g/cm3, the

patient CT and CT to ED tables may generate RED values as

high as 15 g/cm3. The user must be cognizant of the dose

calculation inaccuracies associated with use of values out of

the appropriate range.”

- Appreciate that the max doses in or near high density

structures have a degree of error in the calculated dose.

- Do not let these “false” hotspots contribute to the penalty

applied to the cost function you have chosen to control

overall maximums, which may inadvertently suppress plan

quality and target coverage.

Cost Functions – Monte Carlo – Densities > 3.0g/cm3


Monaco Concepts

• There are two options to calculate the statistical uncertainty:- Statistical Uncertainty Per Control Point

• Is the Percentage uncertainty per voxel on a per segment basis you are willing to accept.

- Statistical Uncertainty Per Calculation• Is the Percentage uncertainty you are willing to accept per

calculation.

• Using the Per Calculation method is generally better as the calculation time is faster due to a lower number of histories.

• Default value is 1% per calculation.- Lower values can be helpful when planning cases with low

density structures.

Cost Functions – Monte Carlo – Statistical Uncertainty


Monaco Concepts

• There are several methods of controlling hot spots in Monaco.

• Remember to consider the Monte Carlo effect.

• Quadratic Overdose can control ‘maximum dose’ within targets well.

• It is more flexible than the maximum dose cost function.

• Maximum Dose is useful when used on the external contour with apply over all voxels enabled. This controls the global max.

Cost Functions – Controlling Hot Spots


Monaco Concepts

• To use the maximum dose to control the global max dose, apply it to the external contour.

• Use the ‘Optimize over all voxels in volume’ option to apply it to all voxels in the study set.

• This applies a global ceiling to the optimization.

Cost Functions – Controlling Hot Spots


Monaco Concepts

• The Shrink Margin removes voxels in a structure away from adjacent targets. These voxels will not be used by the cost function for optimization.

• Extremely useful when multiple targets are being optimized to transition between a high dose and a low dose.

• Can be used in place of Optimization rings.

• Allows a transition zone between a high dose target and an overlapping or adjacent OAR.

Cost Functions – The Shrink Margin


Monaco Concepts Cost Functions – The Shrink Margin – Multiple Targets


Monaco Concepts

• For the transition zone from PTV76 to PTV45, we apply two different Quadratic Overdose.

• One has a zero shrink margin and is set to the higher dose target.

• This will work to keep the high dose inside the target.

• The second Quadratic Overdose has a shrink margin of 0.6cm applied and a lower dose value.

• This will work to keep the dose inside the structure homogeneous.

Cost Functions – The Shrink Margin


Monaco Concepts Cost Functions – The Shrink Margin – Multiple Targets


Monaco Concepts

• The Shrink margin applied should always be divisible by the grid size.

• If not, there maybe an over or under estimation of the margin applied.

• Remember if the values are not working you will have an idea of this by the weight, relative impact, and by using the Voxel tools.

Cost Functions – The Shrink Margin – Multiple Targets


Monaco Concepts

• The purpose of the dose conformality cost function is to shape the high dose volume tightly around one or several target volumes without being too restrictive to the optimum dose.

• The concept of the dose conformality cost function is novel in two ways:

- The cost function uses local importance weights that depend on the distance of a voxel to the nearest target volume and the dose prescribed to this voxel to modify the local effectiveness of the cost function.

- The cost function does not require an absolute iso-constraint for prescription, which would be difficult to determine. Instead, it estimates a feasible measure of dose conformality for each case, and then requires you to prescribe how much more or less conformal the dose distribution should be than this estimate. Hence, it requires only a relative iso-constraint out requiring too many defined parameters.

Cost Functions – Conformality


Monaco Concepts

• This means the cost function is going to squeeze dose into the target by penalizing voxels further away.

• The cost function can optimize over a 4cm radius or an 8cm.- This is adjusted by selecting the ‘Optimize over all voxels’ option.

• Values can be set from 0.1 – 1.0.

• A good starting point is 0.75.

• The value can then be lowered until the desired conformity.



Monaco Concepts

• Conformality works well for single target volumes and stereo volumes.

• It can struggle more with complex head and neck plans.- This is due to multiple dose volumes as well as additional structures and

large changes in the patient geometry (remember if conformality is applied to the patient, higher structures will own the voxels).



Monaco Concepts

• You can control the dose to the patient by using the quadratic overdose cost functions and a series of stepped shrink margins.

• The shrink margins are used instead of optimization contours.

• Start with a value close to the PTV target dose with a small RMS value.

Cost Functions – Controlling conformity with Quadratic Overdose


Monaco Concepts

• The first quadratic overdose is set to the same value as the target dose with no shrink margin.

• The cost function is applying directly against the PTV keeping the target dose inside the target.



Monaco Concepts

• The second quadratic overdose is set to a smaller dose value and has a shrink margin of 0.9cm applied.

• This is not applying in the voxels 0.9cm to the target. It only applies its penalty in the colored voxels.



Monaco Concepts

• The third quadratic overdose is set to an even smaller dose value and has a shrink margin of 2.4cm applied.

• This is not applying in the voxels 2.4cm to the target. Again, it only applies its penalty in the colored voxels.



Monaco Concepts

• You can adjust the values in Stage 1 until weight and relative impact is involved.

• At this point, you know you have reasonable values and the cost functions are working.

• The values set may need adjusting for the Stage 2 calculation.

• However, the system will tell you this through the constrained optimization.



Monaco Planning

• The sequencing parameters will affect the quality AND deliverability of your plan just as much as the constraints will.

• Sequencing parameters will define and shape control points/segments as defined in the above workflow.

• The following slides will help to explain and review the parameters as well as give some tips.

Sequencing

91


Monaco Planning

• The Surface Margin is available for Target cost functions.

• It is designed to limit the need for clipping contours at the patient surface.

• The surface margin allows the system to ignore low doses in the build up region.

• Meaning the system does not try to force dose into the build up region.

Cost Functions – The Surface Margin


Monaco Planning

• Where targets are drawn out to the patient surface and the margin is applied there may be a drop in coverage because the system is not forcing dose there.

• The advantage of the surface Margin is a decrease in MU and a decrease in the global max dose.

• The Physician should try to avoid drawing targets to the skin surface.

Cost Functions – The Surface Margin


Monaco Planning Cost Functions – The Surface Margin


Monaco PlanningCost Functions – The Surface Margin


Monaco Planning

• If you apply a larger margin, Monaco assigns more of the surrounding voxels to the target in optimization.

• If you apply a smaller margin, Monaco assigns fewer surrounding voxels to the target in optimization.

Sequencing Parameters – Target Margin


Monaco Planning

• For ‘normal’ planning, try not to restrict the margin too tight, this can affect the sequencing result.

Sequencing Parameters – Target Margin

97

Normal Tight Very Tight


Monaco Planning

• When Segment Shape Optimization is selected, the optimizer has the freedom to move the MLC leaves to better meet the IMRT Constraints, starting with the open field as designated by the Target and Avoidance margins. The MLCs may only move +/- 1 mm for each SSO loop and the optimizer will use up to five SSO loops allowing for a maximum of +/- 5 mm from the original positions.

• This allows much improved plan quality and deliverability.

Sequencing and Parameters – Segment Shape Optimization


Monaco Planning

• Filter “shapes changed” in the Optimization Console.

• Do not make Stage 2 changes until 2-3 loops have occurred.

• This will give it a chance to converge prior to altering its optimization pathway.

• Skipping Forward will bypass SSO loops continuing.

Sequencing and Parameters – Segment Shape Optimization


Monaco Planning

• The Monaco Smart Sequencer is not limited by number of segments, nor segments per degree.

• This means that areas of high modulation can be provided where required and areas requiring less modulation to have less control points.

• The sequencer will always try to reduce the number of control points.

Sequencing and Parameters – Control Points


Monaco Planning

• The number of segments allowed should represent the complexity of the plan.

• For example, a head and neck will require more control points than a prostate.

• That said, give the system room to work out the modulation and always go slightly above the control points you require.

• Monaco will generally give less control points than the maximum set.



Monaco Planning

• Typically, 120 will work well for Prostates and 150 – 180 will work for head and neck.

• Work these values out as part of commissioning which gives you the best plan quality, deliverability, and QA results.

• Increasing the Max Number of Control Points will increase calculation time.



Monaco Planning

• There are two ways to set multiple rotations in Monaco.

• One method is to have two or more beams within the beam spreadsheet.

• Monaco will not allow duplicate beams so for example, if all parameters are the same, one must be defined clockwise and the other counter-clockwise.

• The advantage of this method is to allow collimator rotations and floor twist or to vary the increment setting per beam.

Sequencing and Parameters – Number of Arcs


Monaco Planning

• The second method is to allow two rotations of the same beam.

• This is done by setting the Max Number of Arcs to greater than 1.

• This method will automatically rotate the beam twice.

• It will export only one beam to MOSAIQ.

Sequencing and Parameters – Number of Arcs


Monaco Planning

• Do not be afraid of having multiple rotations.

• Remember having double the rotation does not mean double the delivery time.

• The number of segments does increase but the number of MUs is relatively the same.

• Although Monaco copes well with complex plans due to smart sequencing and only one full rotation. Allowing multiple arcs has several advantages.

Sequencing Parameters – Number of Arcs


Monaco Planning

• When you add two Arcs rather than two beams, Monaco enhances the segmentation process. It essentially splits the fluence through the central X axis.

• On one rotation, Monaco will optimize one half of the volume. With the second rotation, Monaco will optimize the other half.

• Lets take a look at an example.



Monaco Planning

• Take a prostate with nodes and a unilateral nodal volume:


Monaco

Optimizes and

segments this

half during the

first Rotation.


Monaco Planning

• Take a prostate with nodes and a unilateral nodal volume:


Monaco

Optimizes and

segments this

half during the

second rotation.


Monaco Planning

• If we look at the segment for 200 degrees on the first rotation, we can see the segments optimizing the right side of the volume.



Monaco Planning

• The same gantry angle on the return rotation is segmenting the other side of the volume.



Monaco Planning

• The effect of this is seen in the two plans below:


1 Arc 2 Beams 1 Beam 2 ArcsNote how the dose between

the unilateral volumes is better.


Monaco Planning

• The increment value splits the beam into a series of sectors.

• Each sector represents one sweep of the sweep sequencer.

Sequencing Parameters – The Increment Value

112

Number of sector in a full rotation

= arc / Inc


Monaco Planning

• Prior to Stage 1, the system divides the arc into sectors to simulate the arc during Stage 1.

• The number of sectors is determined by diving the total arc degree by the increment value.

• For example, a 360 arc with a 30 degree increment equals 12 sectors.



Monaco Planning

• The sectors are created in 30 degree increments starting 15 degrees in front and after each angle.

• Generally, the treatment starts at 180 degrees so the first sector is split.



Monaco Planning

• The sweep sequencer moved the leaves from their start to end position in a continuous, uni-directional manner.

• The length that they do this is determined by the sector.

• From the first sector, the leaves will ‘sweep’ to the left direction of the BEV then change to the left as sectors alternate.

• Therefore the increment value is key.

• A starting value of 30 generally works well, lowering to 20 for more complex volumes.- Lowering the sweep sequencer allows time for the MLC to move and

modulate to accommodate the complex target.

Sequencing Parameters – Sweep Sequencer


Monaco Planning

• Beamlet width is used during Stage 1 to define the resolution of the fluence map.

• In Stage 2, you use it during SSO to fine tune the segment shapes.

• The smaller the beamlet width, the finer the fluence grid.

• A good starting point for most plans is 0.2 – 0.3.

• The fluence length is determined by the MLC left width.

Sequencing Parameters – Beamlet Width


Monaco Planning

• Note the difference between the two resolutions.

• The plan quality maybe improved by using a finer grid, but there will likely be more smaller segments.

Sequencing Parameters – Beamlet Width


Monaco Planning

• Lets follow this theory and apply it to a simple prostate plan.

• We will keep beam and segmentation parameters simple and focus on cost functions.

• General rules:- Keep things simple.

- If a cost function is not required, do not put it in.

- If its already there and not needed, take it out.

Building a Simple Prostate Plan


Monaco PlanningBuild a Simple Prostate Plan


Monaco PlanningBuild a Simple Prostate Plan – Parameters

120



121

Keep the Constraints simple.

In this example, we have gone for a Target Penalty.

Remember the Target Penalty works best when

adjacent to the OAR.

Enter the Rx dose and coverage required.


Monaco Planning Build a Simple Prostate Plan

122

To control the max dose to the target, we are going to

add a Quadratic Overdose to the PTV Structure.

We have gone 1Gy above the target as the reference

dose and the RMS of 1.5Gy.

This means that we are saying the PTV is allowed to go

above the Target dose by 1Gy. Plus an average of 1.5Gy

higher than this.



123

To control the rectum dose, we are only using one Serial cost

function.

We have set the EUD at 45Gy and a K value (PLE) of 5.

This will mean the cost function will apply at the low and high

doses.

A shrink margin has been used to reduce conflict with Target

coverage.



124

To control the bladder dose, we are also only using one

serial cost function.

This time we have set the EUD at 52Gy and a K value of 8.

Optimize over all voxels is being used here. This means

the voxels in the overlap region will be shared with the PTV

Target Penalty cost function.


Monaco PlanningBuild a Simple Monaco Plan

125

For the Patient dose, we have multiple approaches.

A Quadratic Overdose of the target dose plus a very

minimal RMS. This will ensure coverage but make

sure target dose stays within the target.

The Max dose cost function is being used with an

optimize over all voxels and has been set to limit

the global maximum dose.


Monaco Planning

• Focus first on the Isoconstraint and the Isoeffect.

• The Isoconstraint tells us what we asked the system for.

• The Isoeffect is the observed result.

• With Constraint Optimization, we know the Isoconstraint will ALWAYS be met for Dose Limiting cost functions.

Build a Simple Monaco Plan

126


Monaco Planning

• We can see that the Isoeffect for the target penalty is lower than the Isoconstraint.

• This indicates that the target dose has not been met.

• Therefore, an OAR or quadratic overdose on the patient or Target must be preventing it.

• This will be indicated by a high weight.


127


Monaco Planning

• So if the theory is correct – we can increase the isoconstraint on the PTV Quadratic Overdose and our coverage will improve.


128


Monaco Planning

• We increased the QO in 0.1 Gy step increments until we reached our objectives. Note how the weight has decreased and isoeffect on the target penalty increased. Our PTV coverage has also increased.


129

Thank you

Monaco Concepts and IMRT / VMAT Planning - Elekta Concepts and IMRT... · 2 | Focus where it...

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Transcript of Monaco Concepts and IMRT / VMAT Planning - Elekta Concepts and IMRT... · 2 | Focus where it...