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

| PAGE 11 CEA | 11 OCTOBRE 2012

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

| PAGE 12 CEA | 11 OCTOBRE 2012

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)

| PAGE 16 CEA | 11 OCTOBRE 2012

)( 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

| PAGE 17 CEA | 11 OCTOBRE 2012

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

| PAGE 20 CEA | 11 OCTOBRE 2012

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