Transcript Monte Carlo Based adaptive EPID dose kernel accounting for

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. Siebers
3582 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
Iraw (x, y)  DF (x, y)
I(x, y)  [
][FF (x, y)  DF (x, y)] mean
FF (x, y)  DF (x, y)
o
o
o
o
FF=flood field, obtained by irradiating EPID with
the largest allowable field size
DF=dark field, background electronics without
any irradiation
SDD=105cm
Dose: 100MU
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

K
allinone
bsi
(E,x,y)  {K
...,K (E,x,y),...}
EPID
bs1
(E,x,y)K (E,x,y),
EPID IMAGE PREDICTION
ALGORITHM

Algorithm:
N
I
predicted
(x, y)  (K mono (E j , x, y)   j (x, y))
j1
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

EPID IMAGE PREDICTION
ALGORITHM
SCORING THE ENERGY FLUENCE

The energy fluence in bin j

M
particle
j
(x, y)   wi  (x  x i , y  y i )
i1
Where δis the impulse function,
the M photons have
weights w, the monokernel uses the central energy of
the bin.


The weighted fluence
M
weighted
(x, y)  
j
IEi
 wi   (x  x i , y  y i )
IE
j
i1 energy
 Where IE is the integrated

To tune the imager-specific monokernel, least-square
method was used to minimize the difference between
MC
 and measurement
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