MONTE CARLO BASED ADAPTIVE EPID DOSE KERNEL ACCOUNTING FOR DIFFERENT FIELD SIZE RESPONSES OF IMAGERS

S. Wang, J. Gardner, J. Gordon

W. Li, L. Clews, P. Greer

J. Siebers3582 Med. Phys. 36(8), August 2009

GOALS

Investigate the field size responses of various EPIDs and the possible causes of variations.

Introduce an efficient MC-based kernel calculation method

Introduce a weighted fluence scoring method to improve the approximation of the energy dependence of EPID response

To illustrate imager specific kernel tuning for investigated EPIDs

METHOD AND MATERIALS

Two Varian EPIDs, the aS500 and aS1000 (with GdO2S:Tb screen layer)

aS500: 512x384 pixels, resolution 0.784x0.784mm2

aS1000: 1024x 768 pixels, doubles resolution ROI: 1x1 cm2 in the center of panel Total 5 imagers ( 2 aS1000, 3 aS500) from 3

institutes Field size (cm2): 5x5, 10x10, 15x15, 25x25,

detector responses are normalized to 10x10 cm2 for comparison

EPID CALIBRATION METHOD

o FF=flood field, obtained by irradiating EPID with the largest allowable field size

o DF=dark field, background electronics without any irradiation

o SDD=105cmo Dose: 100MU

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I(x,y) = [Iraw (x,y) −DF(x,y)

FF(x,y) −DF(x,y)][FF(x,y) −DF(x,y)]mean

MC-BASED EPID MONOKERNEL

Monoenergetic photons on EPID A water slab layer is added to 25 layers of

product EPID to model downstream backscattering

Scoring in 1024x1024 matrix Energy deposition in scoring matrix

normalized to the total number of incident particles to obtain the response per particle

The monokernel is then validated against EPID MC results scored at 107cm (the location of the sensitive screen layer) w.r.t 4 different field sizes.

MC-BASED EPID ALL-IN-ONE KERNEL

The all-in-one kernel allows a tunable backscattering thickness

1 water slab ->25 1mm thick sub-layers Use LATCH to track and score at different

depth Energy deposited in the screen layer is

scored separately

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K all−in−one (E,x,y) = {K EPID (E,x,y)K bs1(E,x,y),

...,K bsi(E,x,y),...}

EPID IMAGE PREDICTION ALGORITHM

Algorithm:

Φ is the energy differential particle fluence for the bin j; K is the imager specific monokernel at the middle of energy bin j, N is the total energy bins spanning the whole energy spectrum.

The convolution uses FFTW, C-bases FFT LINAC head and MLC simulated from BEAMnrc,

patient DOSXYZnrc (or VMC++), then particles fluence extracted at imager plane.

More bins at low energy due to the EPID response characters

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I predicted (x,y) = (Kmono(E j ,x,y)⊗Φ j

j=1

N

∑ (x,y))

EPID IMAGE PREDICTION ALGORITHM

SCORING THE ENERGY FLUENCE The energy fluence in bin j

Where δis the impulse function, the M photons have weights w, the monokernel uses the central energy of the bin.

The weighted fluence

Where IE is the integrated energy To tune the imager-specific monokernel, least-

square method was used to minimize the difference between MC and measurement

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Φ jparticle(x,y) = wi ∗δ(x − x i,y − y i)

i=1

M

∑

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Φ jweighted (x,y) =

IE iIE j

∗wi ∗δ(x − x i,y − y i)i=1

M

∑

RESULTS

Calibration procedure is determined by matching measured and simulated 10x10 cm2 fields.

Quantitative results based on 1x1cm2 ROI with 0.784x0.784 mm2 pixel size.

Field size response of various EPIDs Monokernel All-in-one kernel Comparison between two fluence scoring

methods Imager-specific monokernel tuning

RESULTS-FIELD SIZE RESPONSE

RESULTS-MONOKERNEL

RESULTS-ALL-IN-ONE KERNEL

RESULTS-ALL-IN-ONE KERNEL

RESULTS-FLUENCE SCORING METHODS

RESULTS-FLUENCE SCORING METHOD

IMAGER-SPECIFIC MONOKERNEL TUNING

IMAGER-SPECIFIC MONOKERNEL TUNING

DISCUSSION

The downstream backscattering plays an important role on their dosimetric characteristics

The MC-based all-in-one kernel method allows adjusting the backscatter for specific imager, the number of included backscattering subkernel is tunable till MC matches measurement

More precisely, local kernels can be created with a different backscatter thickness as a function of location.

A weighted fluence scoring method improves the MC measurement agreement

The separation of incident fluence into different energy bins makes the kernels excellent candidates for patient EPID image prediction during treatment