Scattered radiation in projection X

Scattered radiation in projection X-ray
mammography and digital breast
tomosynthesis
Oliver Diaz
Outline
• Introduction: breast cancer, X-ray mammography, etc
• Motivations
• Physics Review  Scattered radiation
• Monte Carlo simulations: Geant4 & validations
• Scatter prediction for DBT: Idealised and realistic geometry
• Conclusions
• Further work
Introduction
Typical female breast1
Cancer: disease where cells grow
abnormally without stopping
Breast cancer
(1) http://nursingcrib.com/anatomy-and-physiology/anatomy-and-physiology-of-mammary-glands-breast/
Introduction
Breast screening programme
X-ray mammography
Breast cancer
incidence1
2
(a) Ludlum
Phosphor
FPDL-011A
(b) Sesales,
FPD3 LLC)
model
(DEQ Technical
Taken from (1)
Breast cancer
mortality
4
(c) Computed radiography
1
(d) Photon counting2
(2) Yaffe & Mainprize (2004). Tech Cancer Res Treat 3(4), p 309
LiPisano
et al. (2009)
Med Phys,
(3)
& Yaffe (2005).
Radiology 234, p353Bakic
36(7), p3122
et al. (2011) Med
Phys, 38(6), p3165
(4) Bick & Diekmann (2010), Digital Mammography
(1) Cancer Research UK
Introduction
Alternative X-ray technology for breast imaging
Digital breast tomosynthesis
(DBT)
2D planar digital mammography
Tomosynthesis planes
Motivations
• Improve breast cancer detection rates
• Digital technology  digital detector  post-process of images
• Techniques for reducing scattered radiation  DBT (no anti-scatter
grid)
• Knowledge of scatter behaviour  perform virtual clinical trials (avoid
irradiation to individuals)
Physics review
X-ray source
Material
Primary
Scatter
X-ray detector
Image = Primary radiation + Scattered radiation
Scatter: how is it quantify?
Magnitude
Breast equivalent 43%, 32 kVp Mo/Mo,
w/o anti-scatter grid 1,2
(1) Boone et al. (2002). Radiology, 222: 519
(2) Nykanen & Siltanen (2003). Med Phys 30(7):845
Spatial distribution
Scatter: how to measure it?
Physical measurements
Monte Carlo simulations
•Direct
P+S
S
•Indirect
Scatter: behaviour
o5 x109 photons
oCylinder (r=116mm; length T)
oIdealised detector
oCircular FOV (r=100mm)
oNo anti-scatter grid
oX-ray beam  0o
•X-ray spectra
Scatter: behaviour
•Glandularity
•Thickness
Scatter: behaviour
•Air gap
•Incidence angle
0o
7.5o
oT = 60mm; G = 50%
o29kVp W/Rh; AG = 0mm
oCircular FOV (r=307mm)
25o
Scatter: behaviour
•Scatter from the system (realistic geometry)
oDirect MC simulations (cone
beam)
oHologic Selenia Dimensions
oT = 90mm (r=80mm)
o40kVp W/Al
MC simulations
Powerful tool to simulate particle interaction with matter.
EGSx/EGSnrc
In-house
PENELOPE
MCNP/MCNPX
FLUKA
GEANT4
GEANT4* toolkit:
oWindows and Unix platforms
oObject-orientated re-use of classes
oSupported by a worldwide scientific community
oWide energy range (250 eV – TeV)
oExtensively used for medical physics applications
oFree (no licence required)
* GEANT4.9.3 version used in this work
MC simulations: GEANT4
•Physics list
oParticles (electrons, gammas X-rays,...)
•Detector
construction
oInteraction processes (photoelectric, coherent scattering,...)
oChemical
elements
(H,C,O,
etc.)distance,...)
oUser-defined
threshold
(energy,
•Stacking action
oMaterials & density (air, adipose tissue, CsI)
oParticle
killing
(local
absorption)
oGeometric
shape
(box,
tube, paralelepiped, etc.)
•Analysis
manager
oe.g. secondary particles in low Z materials
oLocation in space
oSpatial
position
(x,y,z) of computational time
oThis
allows
a reduction
•InputoType
parameters
of interaction (photoelectric, coherent, incoherent scattering)
oNumber
of primary particles to be simulated (X-ray photons)
oMomentum
oMomentum
direction of particles
oKinetic energy
•Visualisation
drivers
oOpenGL
oSource
shape
(i.e. point source)
oDeposited
energy
oDAWN
oEnergy
oVRML spectrum
oRayTracer
MC simulations: GEANT4
MC simulations: Validation
•Validation with published data1,2
(1) Boone & Cooper (2000). Med Phys 27(8), p 1818
(2) Sechopoulos et al. (2007). Med Phys 34(2), p 564
Scatter prediction for DBT
•Necessary
•DBT does not include anti-scatter grids  large scatter fields  knowledge of
scatter is fundamental before reconstruction stage
•Fast
•Direct MC simulations  more than 8hrs using 109 photons (per projection)
•Flexible
•Kernel-based scatter method (indirect MC simulations) which can be used
for a range of breast thickness and glandularities (look-up tables)
•Accurate
• Max. scattered radiation error of 10% when compared with GT
Scatter prediction: ideal DBT
•Idealised DBT geometry
•Relative scatter error maps
Scatter prediction: ideal DBT
•Convolution (Thickness): 5cm thick breast phantom (0o)
Vertical profile
Scatter prediction: simple exp.
Simple experiments: Uniform phantom
Side View
Top View
Scatter prediction: simple exp.
Simple experiments: Edge effects (trapezoid and hexagon)
Side View
Top View
Side View
Top View
Scatter prediction: air gap
Breast phantom edge: Air gap consideration
• Projection of scatter into air
Scatter prediction: air gap
Convolution (Th. & air gap): 5cm breast phantom (0o)
Vertical profile
Th. kernel
Th-AG kernel
Scatter prediction: CF
Correction factor
Scatter prediction
5cm breast phantom (0o)
Vertical profile
Th. kernel
Th-AG kernel
Th-AG kernel+ CF
Scatter prediction: realistic DBT
•Realistic DBT geometry
Scatter prediction: results (50mm)
0o
7.5o
25o
Conclusions
• Development of a MC tool for X-ray mammography and DBT,
validated with less than 4% discrepancy with the literature
•Improvement conventional scatter estimation for DBT (AG
consideration)
•Include scattered radiation from system (i.e. compression
paddle and breast support plate)
•Reduction of computation time (8 hrs MC vs 30-45’ proposed
method)
Further work
• Scatter rejection performance of anti-scatter grids (MC)
oFor a wide range of breast thicknesses and energy spectra
oAnti-scatter grid materials (septa and interspace)
oRevolutionary geometries? (variable septa)
•Quantification of breast curvature in clinical scenarios
oLow cost 3D depth camera technology (i.e. Kinect)
•Revise assumptions made in the proposed scatter estimator
oDo not assume parallel beam within breast region
oSeparation of source of scattered radiation
Thank you
This work is part of the OPTIMAM project and is supported by
CR-UK & EPSRC Cancer Imaging Programme in Surrey, in
association with the MRC and Department of Health (England).