A PRACTICAL ALGORITHM FOR RECONSTRUCTION FROM X-RAY

Similar documents
NON-COLLIMATED SCATTERED RADIATION TOMOGRAPHY

SYSTEM LINEARITY LAB MANUAL: 2 Modifications for P551 Fall 2013 Medical Physics Laboratory

Performance Evaluation of 3-Axis Scanner Automated For Industrial Gamma- Ray Computed Tomography

ULTRASONIC TESTING AND FLAW CHARACTERIZATION. Alex KARPELSON Kinectrics Inc., Toronto, Canada

arxiv: v2 [cond-mat.mtrl-sci] 5 Jan 2010

STATUS AND FUTURE ASPECTS OF X-RAY BACKSCATTER IMAGING

Ch. 4 Physical Principles of CT

Image Acquisition Systems

''VISION'' APPROACH OF CALmRATION METIIODS FOR RADIOGRAPHIC

DUE to beam polychromacity in CT and the energy dependence

Artifact Mitigation in High Energy CT via Monte Carlo Simulation

Advanced Image Reconstruction Methods for Photoacoustic Tomography

Comparison of Probing Error in Dimensional Measurement by Means of 3D Computed Tomography with Circular and Helical Sampling

Arion: a realistic projection simulator for optimizing laboratory and industrial micro-ct

USING cone-beam geometry with pinhole collimation,

COMPUTER SIMULATION OF X-RA Y NDE PROCESS COUPLED

J. Ling Chinese Academy of Science Institute for Ceramics Shanghai, China

ENHANCED IMAGING OF CORROSION IN AIRCRAFT STRUCTURES WITH REVERSE GEOMETRY X-RAY

DEVELOPMENT OF CONE BEAM TOMOGRAPHIC RECONSTRUCTION SOFTWARE MODULE

MEDICAL IMAGING 2nd Part Computed Tomography

Introduction to Biomedical Imaging

Energy resolved X-ray diffraction Cl. J.Kosanetzky, G.Harding, U.Neitzel

ULTRASONIC WAVE PROPAGATION THROUGH NOZZLES AND PIPES WITH

Validation of GEANT4 for Accurate Modeling of 111 In SPECT Acquisition

Phase-Contrast Imaging and Tomography at 60 kev using a Conventional X-ray Tube

Engineered Diffusers Intensity vs Irradiance

GENERAL AUTOMATED FLAW DETECTION SCHEME FOR NDE X-RAY IMAGES

ULTRASONIC INSPECT ABILITY MODELS FOR JET ENGINE FORGINGS

Scatter Correction Methods in Dimensional CT

Spectral analysis of non-stationary CT noise

HIGH-SPEED THEE-DIMENSIONAL TOMOGRAPHIC IMAGING OF FRAGMENTS AND PRECISE STATISTICS FROM AN AUTOMATED ANALYSIS

18th World Conference on Nondestructive Testing, April 2012, Durban, South Africa

Coordinate Measuring Machines with Computed Tomography

NDT OF SPECIMEN OF COMPLEX GEOMETRY USING ULTRASONIC ADAPTIVE

HIGH-PERFORMANCE TOMOGRAPHIC IMAGING AND APPLICATIONS

Investigation on reconstruction methods applied to 3D terahertz computed Tomography

Adapted acquisition trajectory and iterative reconstruction for few-views CT inspection

An Iterative Approach to the Beam Hardening Correction in Cone Beam CT (Proceedings)

Basics of treatment planning II

Representing and Computing Polarized Light in a Ray Tracer

Diffraction and Interference of Plane Light Waves

LAB DEMONSTRATION COMPUTED TOMOGRAPHY USING DESKCAT Lab Manual: 0

Combining Analytical and Monte Carlo Modelling for Industrial Radiology

SIMULATED LIDAR WAVEFORMS FOR THE ANALYSIS OF LIGHT PROPAGATION THROUGH A TREE CANOPY

Identification of Shielding Material Configurations Using NMIS Imaging

CS354 Computer Graphics Ray Tracing. Qixing Huang Januray 24th 2017

Lab2: Single Photon Interference

Characterization of microshells experimented on Laser Megajoule using X-Ray tomography

Constructing System Matrices for SPECT Simulations and Reconstructions

PHYSICS. Chapter 33 Lecture FOR SCIENTISTS AND ENGINEERS A STRATEGIC APPROACH 4/E RANDALL D. KNIGHT

System Optimization and Patient Translational Motion Correction for Reduction of Artifacts in a Fan-Beam CT Scanner

Emission Computed Tomography Notes

Evaluation of Spectrum Mismatching using Spectrum Binning Approach for Statistical Polychromatic Reconstruction in CT

Computer Methods and Inverse Problems in Nondestructive Testing and Diagnostics

CALIBRATIONS FOR ANALYZING INDUSTRIAL SAMPLES ON MEDICAL CT SCANNERS

Switzerland ABSTRACT. Proc. of SPIE Vol N-1

8/7/2017. Disclosures. MECT Systems Overview and Quantitative Opportunities. Overview. Computed Tomography (CT) CT Numbers. Polyenergetic Acquisition

Coupling of surface roughness to the performance of computer-generated holograms

Quality control phantoms and protocol for a tomography system

(Refer Slide Time 00:17) Welcome to the course on Digital Image Processing. (Refer Slide Time 00:22)

MEDICAL EQUIPMENT: COMPUTED TOMOGRAPHY. Prof. Yasser Mostafa Kadah

Distortion Correction for Conical Multiplex Holography Using Direct Object-Image Relationship

Beam Attenuation Grid Based Scatter Correction Algorithm for. Cone Beam Volume CT

Increased Underwater Optical Imaging Performance Via Multiple Autonomous Underwater Vehicles

Multi-slice CT Image Reconstruction Jiang Hsieh, Ph.D.

INFOGR Computer Graphics. J. Bikker - April-July Lecture 10: Shading Models. Welcome!

INDUSTRIAL SYSTEM DEVELOPMENT FOR VOLUMETRIC INTEGRITY

Two slit interference - Prelab questions

Ultrasonic Multi-Skip Tomography for Pipe Inspection

MULTI-MODALITY 3D VISUALIZATION OF HARD-ALPHA IN TITANIUM

Chapter 2: Wave Optics

Supplementary Figure 1 Optimum transmissive mask design for shaping an incident light to a desired

Fast Reconstruction of CFRP X-ray Images based on a Neural Network Filtered Backprojection Approach

Simulation of Beam Hardening in Industrial CT with x-ray and Monoenergetic Source by Monte Carlo Code

RA Y -MODELING FOR COMPUTER SIMULATION OF ULTRASONIC TESTING

Digital Image Processing

Registration concepts for the just-in-time artefact correction by means of virtual computed tomography

FINITE ELEMENT MODELING OF TRANSIENT WAVE PHENOMENA AT

Non-Destructive Failure Analysis and Measurement for Molded Devices and Complex Assemblies with X-ray CT and 3D Image Processing Techniques

A SYSTEM OF DOSIMETRIC CALCULATIONS

Spectrographs. C. A. Griffith, Class Notes, PTYS 521, 2016 Not for distribution.

Study of the Effects of Target Geometry on Synthetic Aperture Radar Images using Simulation Studies

CT vs. VolumeScope: image quality and dose comparison

Scaling Calibration in the ATRACT Algorithm

Workshop on Quantitative SPECT and PET Brain Studies January, 2013 PUCRS, Porto Alegre, Brasil Corrections in SPECT and PET

DAMAGE INSPECTION AND EVALUATION IN THE WHOLE VIEW FIELD USING LASER

Physical Optics. You can observe a lot just by watching. Yogi Berra ( )

Tomography at all Scales. Uccle, 7 April 2014

DUAL energy X-ray radiography [1] can be used to separate

AN ELLIPTICAL ORBIT BACKPROJECTION FILTERING ALGORITHM FOR SPECT

Topics and things to know about them:

CoE4TN4 Image Processing. Chapter 5 Image Restoration and Reconstruction

X-ray Industrial Computed Laminography (ICL) Simulation Study of Planar Objects: Optimization of Laminographic Angle

Simplified statistical image reconstruction algorithm for polyenergetic X-ray CT. y i Poisson I i (E)e R } where, b i

Reconstruction from Projections

A Study of Medical Image Analysis System

Proton dose calculation algorithms and configuration data

Diffraction at a single slit and double slit Measurement of the diameter of a hair

Validation of aspects of BeamTool

Index. aliasing artifacts and noise in CT images, 200 measurement of projection data, nondiffracting

Transcription:

A PRACTICAL ALGORITHM FOR RECONSTRUCTION FROM X-RAY BACKSCATTER DATA Young S. Ham and C.F. Poranski Materials Chemistry Branch Naval Research Laboratory Washington DC 20375 E.C. Greenawald Geo-Centers, Inc. 10903 Indian Head Highway Ft. Washington MD 20744 INTRODUCTION Although numerous applications of x-ray backscatter tomography (XBT) have been demonstrated, only a few have been fully developed to practical implementation [1-5]. In some applications the images produced by direct data acquisition and display methods are plagued with superposition artifacts that can interfere with interpretation [6]. Non-homogeneous materials such as composites or layered structures are particularly susceptible. Reconstruction methods have been proposed to correct the datum from each volume element (voxel) by exploiting the information in data from overlying voxels [7]. Practical inspection systems, however, present a more challenging problem than the monoenergetic highly collimated laboratory demonstration systems. In particular, the use of a bremmstrahlung source and a fan beam, or slit collimated, detector geometry, deprives us of knowledge of the backscattered photon energies and paths that are needed for a true reconstruction. In this paper, we present our work towards a reconstruction using data from a commercial XBT system (Philips ComScan) and a real composite inspection application. Our approach uses pre-processing to remove system artifacts, a priori information about the material, and an iterative method to determine the composition of each voxel. METHODOLOGY Our approach for the reconstruction is a semi-empirical method that utilizes a priori information about the sample. The object of study is sonar rubber dome (SRD), which consists of layers of steel cord reinforced rubber. A combination of several ideas is used to overcome the difficulties associated with the reconstruction Review of Progress in Quantitative Nondestructive Evaluation, Vol. 15 Edited by D.O. Thompson and D.E. Chimenti. Plenum Press, New York, 1996 449

Io(E) It (E) 10 Iz 10 1:1 Figure 1: Schematic diagram to show the shadow artifacts due to the attenuation by incident and scattered beams. Darker circular shading represents material of higher density, i.e., steel cord in rubber matrix. of SRD material. A simple normalization method is used to eliminate the need for calculation of detector efficiency, of solid angle at the scatter point subtended by a rectangular aperture, and of transmission efficiency through the fiber optics. The data obtained from the object is divided against data from a calibration sample, voxel by voxel. These are conveniently converted into 256 levels for visualization or further image processing. The ideal calibration sample for the SRD would be the rubber matrix material. Ideally, the normalized data will show that the rubber has the relative density of unity regardless of depth, ignoring statistical errors. Of course, the shadow artifacts due to the attenuation by the cords would be still visible. Since the attenuation through the rubber was compensated by this normalization process, the problem of reconstruction is reduced to correctly quantify the amount of attenuation by both the primary and scattered beams. Consider an XBT scan of a sample which contains a cord made of steel as shown in Figure 1. A pencil-beam is perpendicularly incident upon the sample, scattered at the point p and collected at the detector. Since the incident beam contains polyenergetic x-rays from an X-ray tube, 10(E) can be defined as (1) where E is loosely defined as the effective energy of the x-ray tube. 1 1(E') is the corresponding integrated intensity when 10(E) is scattered through an angle B. In the notation of the linear attenuation coefficients, subscripts wand 0 stand for cord and the rubber matrix. The I represents that the photons are scattered. If the linear attenuation coefficient of the matrix is different from that of the cord at effective energy E, the intensities, 11, 12, and 13, escaping from the sample corresponding to cases 1, 2, and 3, will be greatly different due to their different beam paths, even if the densities at the scatter sites are the same. Thus, in a practical situation, it would be difficult to recognize whether the difference is due to the 450

different beam path or to the different densities at the scatter site. Complete knowledge of the material above the point P is required in order to evaluate the densities at the scatter site. Assuming such information is available, we would like to calculate compensation factors such that the scattered intensities for cases 1 and 2 are equal to 13. These compensation factors can be calculated by obtaining ratios of 11 or 12 to h That is, the normalization can be used to obtain the compensation factors rather than calculating the absolute intensities for all 3 cases. The compensation factor for the incident beam when it traverses a distance d, the diameter of the cord, can be shown to be h(e') = eciiw-lio)d = a 11(E') Thus, 'l/j1, the compensation factor for the incident beam when it traverses any distance x, is Similarly, the compensation factors for the scattered beam traversing a distance d (through the middle of a cord) and the factor for the distance x are (2) (3) (4) Note that these factors are functions of attenuation coefficients of the cord and rubber, and the cord diameter, but not functions of the distance from the sample entering position to the scatter site. Here the constants a and f3 are determined experimentally, while x can be calculated from the available geometric data. However, since the scatter angles are not uniform in the ComScan data acquisition geometry, the effective energies corresponding to each depth (layer) are all different. Scattered beams at different layers will then experience different amounts of attenuation. Thus, f3 should be measured at every scatter angle for an accurate reconstruction. This simple compensation method could eliminate all attenuation artifacts by the incident and scattered beams. In the derivation of equations (3) and (5), both incident and scattered beams are assumed to be well collimated pencil beams. But ComScan uses a slit collimated fan-beam geometry. To reduce the resulting error, multiple beams are traced from the scatter site to the detector. The average distance through the cord is used. RECONSTRUCTION The reconstruction process utilizes results obtained in previous section. After an object is scanned, the data are normalized against a calibration sample containing only the matrix material of the object. Since the matrix of the object and the calibration material are identical, the difference in intensities will be shown only in the regions where the density of the object differs from that of the calibration material, and/or where the beam traverses through the difference region (cord). When the beam traverses the difference region, the length of the segment (x) traversing the region is calculated. With that information, compensation factors, 'l/j1 and 'l/j2, using (5) 451

eq. (3) and eq. (5) are calculated and multiplied by the normalized data. The reconstruction process works from the top layer to bottom layer as described in the previous report [7], but using the normalized data rather than the raw experimental data. In the first layer, all voxels are determined to belong to either the cord or the matrix using a certain criterion such as a threshold. This information is kept in an array to be used for the reconstruction of lower layers. From the second layer down, the beam paths are accurately traced to identify whether the beam traverses through any voxels whose densities are different from the calibration sample. If different, adjustment on their intensities is made first, then the densities are computed. The information obtained in the current layer is then used to reconstruct the next layer. This process continues until the densities of the deepest layer are obtained. EXPERIMENT A simple phantom was fabricated by casting a plastic resin containing an actual steel cord (radius of 0.74 mm) used in SRD construction. Only a single cord was positioned in the plastic matrix. The phantom was scanned so that the cord was parallel to the x-ray beam sweeping direction. A reference scan was performed with a solid plastic block as calibration material. 10 mm focal apertures and a 10 mm spacing between the scanner head and the phantom were used. The data were saved in a Bernoulli diskette and ported to an SGr workstation for the reconstruction. Fig. 2 shows tomographs of the phantom along the z-axis. Attenuation (shadow) artifacts are visible in the images corresponding to the depth below the cord. Notice that the densities do not decrease gradually with depth due to the variations of detector efficiencies and other data acquisition variables. As expected, the scattered beams are attenuated more than the incident beams. The shadows caused by the scattered beams are slightly larger due to the scattered beam divergence. Experimental data were normalized with the data obtained from a block of plastic. These are displayed as cross-sectional images along the Z-axis in Fig. 3. Note that the inconsistency due to variation in detector efficiencies was removed. To obtain the values of Q: and f3 needed for reconstruction, one has to acquire the data corresponding to the shadows, but this is difficult to do directly from the data. This process was facilitated by visually displaying the data as cross-sectional images and interactively plotting density profiles across the cord. The minimum values at two different dips, corresponding to attenuation through the center of the cord, were obtained seven times per image and averaged to reduce experimental errors. As expected, the attenuation by the incident beam was uniform regardless of the depth. While the attenuation by the scattered beam was supposed to increase with depth, since the scatter beam loses more energy as the scatter angle increases, there was no measurable decrease in attenuation. This is probably due to the small differences in scatter angle (approximately 10 ) between the first and the last layers when a 10 mm focal aperture is used, and the differences were within the statistical error. From these data, Q: and f3 were 1.66 and 2.14. These values were used for reconstruction. 452

Figure 2: Raw data from a single cord phantom. Attenuation (shadow) artifacts are visible in the images corresponding to the depth below the cord. Notice the unusual brightness fluctuation between images due to the variations in detector efficiencies and other data acquisition variables. Figure 3: Normalized images: The brightness anomaly shown in Figure 2 is removed. 453

,... I, "!! I.' Figure 4: Reconstructed images: The normalized data are used for reconstruction. The shadows shown in Figure 3 are disappeared. The reconstructed results are shown in Fig. 4. The attenuation artifacts shown in the raw data are almost gone, although some vestiges remain. A 3-D representation of the reconstruction is shown in Fig. 5. CONCLUSION We have developed a semi-empirical methodology to reconstruct data from a real x-ray backscatter inspection system with a particular application to the reconstruction of the SRD material. The methodology was successfully demonstrated by applying the algorithm to the data obtained from ComScan. One of the main difficulties of the reconstruction of the SRD is that the radius of cord is comparable to the size of a pixel. Considering that the size of the cord was close to the resolution of the scanner, we think that the result is quite remarkable. Figure 5: 3-D representation of the reconstructed results visualizing only the cord. 454

ACKNOWLEDGEMENTS This work is supported by the Office of Naval Research Postdoctoral Fellowship, Exploratory Development Programs and the Naval Sea Systems Command. REFERENCES 1. D.G. Costello, J.A. Stokes, and A.P. Trippe, "Theory ard Applications of Collimated Photon Scattering," 14th Symposium on Nondestructive Evaluation, San Antonio, TX, 1983 2. H. Strecker, "Scatter Imaging of Aluminum Castings Using an X-ray Fan Beam and a Pinhole Camera," Materials Evaluation, vol. 40, 1982 3. B.C. Towe and A.M. Jacobs, "X-ray Backscatter Imaging," IEEE Transactions on Biomedical Engineering, Vol. BME-28, No.9, 1981 4. E.C. Greenawald, J.B. Nagode, C.F. Poranski, and Y.S. Ham "In - Situ NDE of Navy sonar Domes Via X-ray Backscatter Tomography," Review of Progress in Quantitative NDE, Vol. 14, p.881, 1995 5. G. Harding and J. Kosanetzky, "Scattered X-ray Beam Nondestructive Testing," Nucl. Instr. and Meth. in Phys. Res., Vol. A280, pp 517-528, 1989 6. E.C. Greenawald, Y.S. Ham, and C.F. Poranski, "Artifacts in X-ray Backscatter Tomography of Non-Homogeneous Objects," Review of Progress in Quantitative NDE, Vol. 13, 1994 7. YS. Ham, E.C. Greenawald, and C.F. Poranski, "Reconstruction Method for X-ray Backscatter Tomography," Journal of the Canadian Society of Nondestructive Testing, 1995 (To be published) 455