monaco

Question 1

What are cost functions?

Answer 1

Monaco’s ROI objectives

Question 2

Main difference between Monaco and other TPS

Answer 2

Monaco is a voxel-based TPS/template based TPS

Question 3

Optimization approaches:

Answer 3

1. Constrained optimisation (normal tissue priority) - more common. OARs are met before target objectives
2. Pareto optimisation (target volume priority)

Question 4

Algorithms Available

Answer 4

Monte carlo and pencil beam
More hot spots with monte carlo

Question 5

Energies Available

Answer 5

6MV or 6MV FFF (for sbrt)

Question 5

Gantry functions

Answer 5

can only select start angle and arc length
Arc increment - splits beam into a series of sectors
Number of sectors in a full rotation = arc/inc
A 360 degree arc with a 30 degree increment equals 12 sectors
ELEKTA has 1024 control points
VARIAN has 180

Question 6

Statistical uncertainty

Answer 6

Statistical uncertainty <2% is sufficient for treatment planning (1.5% good, does slow down planning process)

Question 7

Segment Shape Optimisation

Answer 7

Moves MLCs to achieve balance between plan quality and speed (5-20)
By increasing SSO:
Increase optimisation time
Decrease delivery time
Decrease number of segments - reducing delivery time
Mus can increase
Plan quality improves

Question 8

Fluence smoothing (OFF, low, med, high)

Answer 8

Off: creates many segments
Low: creates more segments
Medium: average amount of segments (use for less complex plans)
High: Creates few segments

Question 9

MLC GAP

Answer 9

minimum 1cm
Less leakage in elekta -> Can treat with a colli of 0

Question 10

What are objectives

Answer 10

anatomy-specific functions that establish dose and biological response goals

Question 11

What are constraints

Answer 11

anatomy specific functions that must be met. Often are referred to as hard constraints

Question 12

Types of cost function

Answer 12

Biological and physical

Question 13

What constraints can be used for targets

Answer 13

Target penalty -min dose
Quadratic overdose- max dose

Question 14

What objectives can be used for OARs

Answer 14

Biological parallel or serial
Traditional DVH based (overdose DVH)- max DVH

Question 15

How to improve conformity

Answer 15

Quadratic overdose -use to create ring like effects
Conformality

Question 16

RMS

Answer 16

Root mean square - allows flexibility on max dose

Question 17

Use of K VALUE

Answer 17

The length of the weight arrows corresponds to where and how hard the parallel cost function is working to meet the constraint of the OAR
K value - controls max dose in serial organs
- Controls mean dose in parallel organs

Question 18

Parallel K value

Answer 18

1-4
Lower the value –> lower the impact

Question 19

Serial K value

Answer 19

1-20

Question 20

K equation

Answer 20

0.15 x Dref

Question 21

Conformality function

Answer 21

start at 1. can be used for the patient contour. Lower the value - the more conformal it is
Uses it 4cm or 8cm away from edge of target

Question 22

Shrink margins

Answer 22

enable the voxels near the targets and other OARs to have a transition zone between competing cost functions
0.3-0.5 margin if ptv is close to build up region (edge)

Question 23

K value stands for

Answer 23

Power law exponent

Question 24

Stages of calculation

Answer 24

Stage 1 - fluence
Stage 2 - MLCs - optimise segment shapes