DERNIERS AVANCEMENTS
DU MODULE CIVA TOMO
11 octobre 2012
Club Image 3D NOESIS | Marius COSTIN
Laboratoire Images Tomographie et Traitement
CEA | 11 OCTOBRE 2012 | PAGE 1
CONTEXT
Simulation for Non-Destructive Testing
Why ?
Prepare & optimize the experimental set-up
Optimize the usage of the imaging system improve lifetime
Interpretation of results: analysis, diagnosis
NDT performance anticipation
Qualification of methods
Uncertainty analysis (POD)
« Virtual testing » in product design phases (reduce physical mock-ups)
Improve CT reconstructions from experimental data (artifacts reduction)
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OUTLINE
Introduction P.4
CIVA RT
Radiographic testing module P.5
From CIVA RT to CIVA CT P.7
CIVA CT
CT Simulation and Reconstruction P.9
Import Experimental Data P.13
Probability of Detection (POD) P.16
Conclusion P.20
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INTRODUCTION
CIVA Simulation Platform
The world reference for NDT simulation & expertize
More than 170 customers worldwide (> 250 licences)
Industries, SMEs, research centers, academics
A multi-technique software platform
UT, ET, RT, CT, GWT
A valorization platform
Collaborations with many leading labs (industries and academics)
ET : 2D map of a complex defect
UT : Transmitted beam computation
RT : weld inspection
Developed by 4 labs at CEA-LIST ~ 35 developers permanently
CT : tomographic reconstruction
of complex parts
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INTRODUCTION
CIVA Simulation Platform
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RT
… CT, GWT
CIVA RT
CIVA Radiographic Testing (CIVA RT)
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Simulation of complex parts
Direct and scattered radiation (Monte Carlo computation)
Gamma and X sources
Scattered radiation Direct radiation Final image
+
Simulation studies of radiographic inspections with CIVA (since 2007)
CIVA RT
From CIVA RT to CIVA CT (CIVA Tomo)
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« Radiology » parameters
Geometry
Spectra, intensity, exposure time
Filters
Processing (for digital radiology)
Specific « CT » parameters
Source trajectory
Number of projections
Reconstruction algorithms
Post processing
CIVA RT CIVA Visualisation
Algorithms – XML parameters
CT Interface
Analytical
FDK
Iterative (algebraic, statistical)
PixTV, SparseTV, OSEM, …
CIVA RT
From CIVA RT to CIVA CT (CIVA Tomo)
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CT set-up Part, source,
detector, motion,
defects
Source positions
Rotation axis
Detector
X-Ray
projections
CIVA
RT
CT
Reconstruction
CT data (images & volume)
CIVA CT
CT Reconstruction Algorithms
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1. FDK (Feldkamp-Davis-Kress) algorithm is a 3D analytic reconstruction
method:
Reconstructs the function f(x,y,z), which is a map of the linear
attenuation coefficients of the imaged sample
It is a three step algorithm of the filtered back-projection type (FBP) for
cone-beam data:
- Weighting:
- Filtering (convolution):
- Backprojection:
pgpRpQ *,, '
dU
z
U
yxQ
Uzyxf
,
)sin()cos(1),,(
2
0 2
SO
SO
D
yxDU
)cos()sin( where:
CIVA CT
CT Reconstruction Algorithms
| PAGE 10 CEA | 11 OCTOBRE 2012
2. PixTV is an iterative reconstruction algorithm which minimizes the TV (total
variation) norm:
Uses the linear data model for the CT problem
Projection and image space discretized and represented as a system of equations
N
l
llkk fap0
,
pk is a vector containing the projection data
for a ray k,
N = n² is the total number of pixels,
a is the system matrix,
f is the image to be reconstructed (reshaped
as a vector containing the attenuation values)
convex optimization problem with TV (total
variation) regularization
CftsfpAf
TVf ..,
2min
2
CIVA CT
Reconstruction From Few Projections - Comparison
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Example:
- Object: contrast phantom
- Source : 100 kV X-ray generator
- Detector : flat panel 512x512 pixels (200μm)
“Normal” reconstruction : 512 projections
FDK PixTV SparseTV GradTV
CIVA CT
Reconstruction From Few Projections - Comparison
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FDK PixTV SparseTV GradTV
Reconstruction from 32 projections
CIVA CT
Import Experimental Data New functionality to import experimental data process and reconstruct
Import wizard
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CIVA CT
Import Experimental Data Automatic CIVA model simulation
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CIVA CT
Import Experimental Data Reconstruction
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CIVA CT
POD (Probability of Detection) Module
Input Data = set of simulated cases with variable parameters and incertitude
e.g. Flaw size = variable parameter
Flaw position = uncertainty parameter
Procedure
1. Detection with quantitative criteria : Rose Criterion, ellipse, …
2. Compute “observed POD”
3. Compute POD function with a parametric fit on a chosen function (a priori)
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)( trialsofNumber
)(Hits ofNumber )(
a
aaPODobs
• A.P. Berens, Metals Handbook, vol. 17, 9th edition: Nondestructive Evaluation and Quality Control
CIVA CT
POD Module
Improvement of the POD function
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CIVA CT
CEA | 10 AVRIL 2012 | PAGE 18
POD in Practice with CIVA (version 11)
Uncertainties description
A POD panel definition
CIVA CT
POD Results Page
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Data table
Data plot +
Statistical regression
Plot of
residuals
POD curve
Statistics
Signal Response
Thresholds
CONCLUSION
Future Research
Advanced reconstruction algorithms (statistical, incomplete/truncated data, … )
Data analysis
Improvement of POD functionality
Experimental data
Corrections
Robotized CT
PhD on CT reconstruction on non-standard trajectories
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DRT LIST
DISC
LITT
Commissariat à l’énergie atomique et aux énergies alternatives
Institut Carnot CEA LIST
Centre de Saclay | 91191 Gif-sur-Yvette Cedex
Etablissement public à caractère industriel et commercial | RCS Paris B 775 685 019
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CEA | 11 OCTOBRE 2012