photon dose algorithm, modeling in RTP

Question 1

3D vs IMRT vs SRS vs VMAT

Answer 1

1. RTP data requirements vending specific
2. IMRT- modeling of mlc critical, small mlc defined fields
3. SRS- modeling of small circular fields (cones) or small MLC fields
4. VMAT-no specific modeling requirements.

Question 2

Attributes of a good dose algorithm

Answer 2

1. Based on first principles
2. Accuracy (against a standard, typically beam data)
3. Speed
4. Expandable( for advancements in treatment delivery)

Question 3

Why have accurate algorithms?

Answer 3

RT driven by maximizing TCP and minimizing NTCP, which are both very sensitive to absorbed dose- 5% change in dose yields a 20% change in NTCP.

We learn from protocols, and they depend on accurate dose reporting

Question 4

the photon beam

primary photons before flattening filter…after?

Answer 4

primary photons
scatter photons
scatter electrons

Challenge in combining these

Question 5

factors influencing photon scatter

Answer 5

as depth goes up, so does scatter
As field size goes up, so does scatter
As energy increases, scatter decreases

Question 6

Sources of errors/accuracy from 1976

Answer 6

Absorbed dose at cal point...2.0%
additional uncertainty for other points...1.1%
Monitor stability...1.0
Beam flatness...1.5
patient data uncertainties...1.5
Beam and patient setup...2.5
overall minus dose calcs...4.1
dose calcs...2,3,or 4
overall...4.6,5.1,5.7. driven by dose calcs

Question 7

two types of dose algorithms

Answer 7

measurement based…rely on water phantom measurements along with correction factors…Clarkson, ETAR

Model Based…use measurement data to derive model parameters, but don’t calc with it. model uses parameters and physics to calc dose…convolution, MC

Question 8

effective path length…when does it not work?

Answer 8

lung in high energy- electron disequilibrium

best for calcs away from inhomogeneities

Question 9

Limitations of RTAR

Answer 9

does not consider position or size of inhomogeneity

Question 10

Limitations of Batho

Answer 10

works well below less dense materials, otherwise overestimates dose
Assumes CPE

Question 11

equivalent TAR (ETAR)

Answer 11

first designed for computer and to use CT data
correction factor method
3D in principle

Question 12

FFT convolution, Differential scatter air ratio, delta volume

Answer 12

attempt to improve on scatter, dimension at location of heterogeneity

too complex at the time…abandoned

Question 13

Premodeling era algorithms

Answer 13

correction based
cumbersome- cobalt era
but opened door to convolution, superposition, and monte carlo

Question 14

Monte Carlo

Answer 14

not used except for electrons and small fields.
simulate random trajectories of the individual particles by using machine generated random numbers to sample the probability distributions governing the physical processes involved

by simulating a large number of histories, info can be obtained, particle by particle, about the average of macroscopic quantities such as energy deposition

codes built on foundations of measured and calced probability distributions and are updated as new discoveries are made about how radiation interacts with matter, typically by high energy physicists.

Serve as the ultimate cavity theory and inhomogeneity corrections

Question 15

Monte Carlo Advantages

Answer 15

algorithms are relatively simple

if algorithm is good, accuracy determined by the cross section beam data

method is microscopic, boundaries not a problem

geometries may be complex, but that is ok

Question 16

Monte Carlo disadvantages

Answer 16

algorithms are microscopic, so little theoretical insight derived in terms of the macroscopic characteristics of the beam

consumes computing resources

Electron and photon MC still relies on condensed history algorithms that employ some assumptions, yielding the possibility of systemic errors

Question 17

MC and noise

Answer 17

very histories makes more noise…want no more than 2% noise

Question 18

Convolution integration

Answer 18

integrates over a volume the product of three factors… Mass Attenuation Coefficient, the Primary Fluence, and a Polyenergetic Kernal

Terma (energy deposition) convolved with kernal yields dose.

Question 19

typical model parameters

Answer 19

electron contamination (build up region)
incident fluence shape
radiation source size
head scatter
jaw/mlc transmission
resolution of calculation (dose grid)

Question 20

Role of the Convolution Geomoetry

Answer 20

TERMA…all energy released…by particles or photons. May be deposited locally or elsewhere

The convolution kernal will describe how that energy will be deposited.

Question 21

role of CT

Answer 21

to get at density. RTP will have a CT/density look up table and a mu/rho lookup table

Question 22

dose deposition kernel

Answer 22

monte carlo simulation of photons of a single energy interaction at a point in water…the resulting energy released is absorbed in the medium in a drop-like pattern called a dose deposition kernal

Kernel changes as a boundary between materials is involved

Question 23

what if we have a spectrum?

Answer 23

polyenergetic kernel

summed kernal to that spectrum

Question 24

pencil beam

Answer 24

not scaled laterally to account for changes in rad transport due to inhomogeneities

breaks down at interfaces and for structures smaller than the beam because of the assumption of uniform field

advantage…fast

suffers in inhomogeneity situations

Question 25

superposition vs PB

Answer 25

PB errors higher, up to 7%

Question 26

MC vs superposition

Answer 26

MC possible in clinic now, but not that much better than superposition convolution.

Question 27

typical set of data requirements

Answer 27

1. CT Scanner characterization
2. absolute calibration and geometry done under
3. CAX depth dose PDD
4. Relative dose profiles- open/wedged
5. Output factors (Sc,Sc,p)
6. Wedge and tray factors
7. Electron applicator/insert factors
8. VSD