Process Control for Computed Tomography using Digital Detector Arrays

David Allen FRY1, Christopher Jason STULL

1, Brandon Michael LATTIMORE

1

1Nondestructive Testing & Evaluation Group,

Los Alamos National Laboratory;

Los Alamos, New Mexico USA

Phone: +1 5056652916, e-mail: dafry@lanl.gov

Abstract

Los Alamos National Laboratory has recently completed the qualification of a computed

tomography process for the evaluation of small parts. As part of the qualification, ASTM

International Digital Radiography (DR) and Computed Tomography (CT) standards were used

to develop a process control program. ASTM E2737 and E2698 were used to create a series of

baseline tests for the individual DR images that make up a CT data set. Spatial resolution,

contrast sensitivity, detector offset, and lag are performed initially and then repeated on a

weekly basis to ensure the digital detector array is operating within tolerance. ASTM E1695

spatial resolution and contrast sensitivity tests are used to assess CT baseline and stability. The

complexity of these tests, in addition to the frequency at which they are to be executed,

necessitated the development of software to automatically analyze the process control data. To

this end, Graphical User Interfaces (GUIs) were written using the scripting language, Python. It

is the intent of the authors to make this software available to the ASTM community at a later

date.

Keywords: Computed tomography, digital radiography, process control

1. Introduction

Los Alamos National Laboratory (LANL) has been doing Research & Development on digital

radiography (DR) and computed tomography (CT) for many years. Recently, DR/CT

processes have needed to be qualified for production use. All system variables must be

addressed, established, and documented. Part of the qualification is performing a system

baseline characterization. Periodic stability tests comparing results to the baseline are then

performed to ensure the system is operating within acceptable limits.

LANL produces components for the United States government’s National Nuclear Security

Agency (NNSA). In the NNSA environment, the high level requirements document for

DR/CT is RMI T097 Qualification of Digital Radiographic Imaging Techniques [1]. The high

level requirements have been incorporated into a LANL Design Agency specification

SS6K0461 General Specifications for Digital Radiography of Los Alamos National

Laboratory-Designed Parts and Assemblies [2]. Both of these documents rely on

implementation of ASTM International standards E1695 [3] and E2737 [4] to satisfy

requirements. Table 1 compares the requirements and standards.

We aim to make these tests as easy and quick as possible. Currently we have agreed with the

Design Agency to perform weekly performance checks. For our 10X magnification CT system

based on DDA technology, we perform both DDA and CT checks. We have developed

automated and semi-automated methods for analysis of the performance test images.

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2. Terminology

CNR – Contrast to Noise Ratio

CR – Computed Radiography

CSa – achievable Contrast Sensitivity (see ASTM E2597 [5])

DDA – Digital Detector Array

DDL – Digital Driving Level

ERF – Edge Response Function

LSF – Line Spread Function

MTF – Modulation Transfer Function

PV – Pixel Value

SRb – Basic Spatial Resolution (see ASTM E2597)

iSRB - Interpolated Basic Spatial Resolution (see ASTM E2597)

SNR – Signal to Noise Ratio

Table 1

Requirement T097 SS6K0461 ASTM/DDA

Standard

Phantom ASTM/CT

Standard

Phantom Measure

Spatial

Resolution/

MTF/SRb

X X E2737 E2002 Gauge

or line pair

gauge

E1695 Uniform

Disk

iSee SRb

Python GUI CT

MTF

Contrast

Sensitivity/

CNR

X X E2737 Penetra-

meter/

Shim

E1695 Uniform

Disk

Python GUI

CNR

Python GUI CT

CDF

Signal-to-Noise

(SNR)

X X E2737 Penetra-

meter/

Shim

Python GUI or

iSee SNR

Lag X X E2737 None Python GUI

Lag

Dynamic Range X X E2737 Penetra-

meters/

Shims

Python GUI

CNR

Dimensional

Uniformity

X X E2445 CR Phantom iSee Distance

measure

Detector

Degradation

X X E2737 None Python GUI or

iSee

Display

Brightness

X X E2737 SMPTE RP133 Calibrated light

meter

Display Contrast X X E2737 SMPTE RP133 Visual – 0%

DDL against

5% DDL and

95% DDL

against 100%

DDL

Display Spatial

Resolution

X X E2737 SMPTE RP133 Visual –

alternating 1%

& and 100%

contrast lines

3. DDA Checks

ASTM International, E2698 Standard Practice for Radiological Examination Using Digital

Detector Arrays [6] calls for tests for monitoring the DDA performance over time shall be

performed in accordance with ASTM International, E2737 Standard Practice for Digital

Detector Array Performance Evaluation and Long-Term Stability.

3.1 Spatial Resolution

An image of the ASTM E2002 Gauge [7] with both vertical and horizontal orientations is

captured with the gauge directly on the DDA face. ISee! Professional software [8] contains a

modulation calculator based on an averaged line profile through the E2002 gauge – see Figures

1 and 2. The line pair gauge is captured and analyzed for the largest pair with < 20%

modulation (SRb) or interpolated to find the exact 20% modulation frequency (iSRb).

Figure 1: ISee! averaged line profile through ASTM E2002 duplex wire gauge (horizontal

profile shown)

Figure 2: ISee! measurement of modulation of a wire pair. Three measurement points are user

selected (white dashed lines) and the modulation reads in the upper right corner (arrow).

3.2 Contrast Sensitivity and Dynamic Range

Images of appropriate material and thickness hole-type penetrameters on a shims is captured

with the same parameters as production imaging for the thinnest and thickest sections to be

imaged. Our Python CNR routine is used to analyze the images. The user selects the

Figure 3: CNR determined from signal and noise inside and outside a penetrameter hole

penetrameter hole for which CNR is desired and the routine automatically calculates CNR

based on the region between two regions of interest around the hole with sides 2X and 4X the

hole diameter and the hole – see Figure 3. From CNR, achievable Contrast Sensitivity (CSa)

can be calculated.

CNR and/or CSa is calculated for both the thinnest and thickest regions of a given part, or an

acceptable range of part thickness is determined with minimum CNR/CSa.

3.3 Signal-to-Noise Ratio (SNR)

A graph of SNR vs. Exposure is generated from flat field images, see Fig. 4. A quick check of

just one point, such as 90% of saturation, on the line is sufficient for the periodic tests. The

slope of the line also gives the ASTM E2737 Efficiency of the detector.

Figure 4: SNR vs. square root of exposure

3.4 Lag

A graph of signal vs. time is generated from a series of images before and after the XGD is

shut down, see Fig. 5. The user selects a region of interest and the software builds a table of

average signal vs. time for a sequence of images taken with the CT acquisition software with

no object.

SNR = 1053.2*sqrt(E) - 9.139

SNR = 2256.4*sqrt(E) - 0.2905

0

50

100

150

200

250

0 0.05 0.1 0.15

SN

R

SQRT(mGy/frame)

SNR vs Exposure 1E33 Technique

1 frame

8 frame

Linear (1 frame)

Linear (8 frame)

Figure 5: Lag measurement

3.5 Dimensional Uniformity

An ASTM E2445 [9] or similar phantom, see Fig. 6, is measured to check dimensional features

are uniform. Capturing this phantom image obtains the E2002 gauges for the spatial resolution

check.

0

50

100

150

0 2 4 6 8 10 12 14

% o

f 1

st f

ram

e P

V

Time (seconds)

Shutdown 1E33 Technique (3.0 fps)

1E33 Shutdown

y = 5.0495x-0.512

0

2

4

6

8

0 5 10 15 20 25 30 35

% o

f 1

st f

ram

e

Frames after Transition

Shutdown 1E33 Technique (3.0 fps)

Shutdown

Power (Shutdown)

Figure 6: LANL CR Phantom used for dimensional uniformity

3.6 Detector Degradation

An Offset image is analyzed for signal level and SNR, and visually for any pattern. An

increase in average offset PV or decrease in SNR signals degradation.

4. CT Checks

ASTM E1570 Standard Practice for Computed Tomographic (CT) Examination [10] calls for

initial and periodic system performance measurement. These requirements can be satisfied

using the methods of ASTM 1695 Standard Test Method for Measurement of Computed

Tomography (CT) System Performance.

4.2 Contrast Resolution

An appropriate material and diameter uniform disk is scanned with the same parameters as

production imaging. For an individual reconstruction slice, extracted from this data set, the

user selects a circular region within the disk of material large enough to supply a statistically

significant sample of pixels, but small enough to avoid a common reconstruction artifact

referred to as “cupping.” Cupping is an artifact arising primarily from beam hardening (but

also from internal scatter), resulting in reduced attenuation values near the centers of uniform

cross-sections. These reduce attenuation values imply that the density in the interior region is

less than that of the exterior region which is, of course, not the case for uniform cross-sections.

Figure 7: Example tile selection from reconstruction slice of uniform disk.

After the circular region is selected, the software draws a square, inscribed within the circle is

drawn (see Figure 7). That square is then progressively subdivided into square tiles, until the

tile size (i.e. edge length of the tile, in pixels) is equal to one pixel. The image below presents

an example of this operation for an edge length of the initial square equal to 256 pixels.

Figure 8: Example tiling scheme

Having performed this operation, the following mathematical operations are conducted for

each set of tiles of a given size (e.g. for all 64 pixel by 64 pixel tiles, then for all 32 pixel by 32

pixel tiles, and so on). Example output from the Python-based GUI is given in Figure 9.

1) Calculate the mean attenuation of each of the tiles.

2) Calculate the standard deviation of the values computed in 1), in order to obtain the

standard error in the mean – note that this operation yields on values for each set of

tiles.

3) Express the standard error in the mean as a percentage of the mean of the values

computed in 1) and multiply by 3, which corresponds to a 50% false-negative rate

(i.e. the case of threshold detectability).

Figure 9: Contrast resolution plot from Python-based GUI

4.1 Spatial Resolution

As for the contrast resolution system test, the spatial resolution system test begins with a

reconstruction slice of a disk of uniform material. This system test, however, is concerned with

the edge response function, and so, the user draws two circles that bracket the edge of the

reconstruction slice of the disk; this is illustrated in the figure below. The pixels that fall

between those two circles are then sorted and binned, according to their distances from the

center of mass of the disk. These binned pixels are then averaged to obtain a single value for

each bin. The Edge Response Function (ERF) is then computed as a piecewise cubic

polynomial fitted to the averages computed previously using a set (determined by the number

of points used to fit the cubic polynomial) of consecutive bins; performing this fit for multiple

sets of consecutive bins yields the piecewise component.

It is clear that the calculation (or placement) of the center of mass of the disk is important, as

asymmetries can cause substantially erroneous results. Therefore, despite its apparent trivial

nature, it is worth pointing out specifically that care must be taken in calculating (or placing)

the center of mass.

Figure 10: Center of mass calculation (or placement) error.

Having computed the ERF, the Line Spread Function (LSF) and Modulation Transfer Function

(MTF) are reasonably straightforward to obtain. The procedure to compute the LSF begins by

fitting a piecewise cubic polynomial to the ERF, essentially smoothing the ERF. For each

piecewise fit, the derivative is evaluated at the center of the piecewise window. The ensemble

of these derivative evaluations is then normalized by the maximum to arrive at the LSF.

Finally, the MTF is calculated simply as the amplitude of the Fourier Transform, normalized to

unity at the zero frequency. Example output from the Python-based GUI is given in Figure 11.

Note the asymmetry

of the circular region

due to misplacement

of the center of mass.

Figure 11: Spatial resolution plots from Python-based GUI.

5. Display Checks

All display checks are based on a display of the SMPTE RP133 test pattern (Fig. 9) [11] per

the requirements of ASTM E2698:

“The image display shall meet the following requirements as a minimum. Alternate image

displays or requirements may be used with Cognizant Engineering Oraganization approval.

‚ The minimum brightness as measured off the image display screen at maximum Digital

Driving Level (DDL) shall be 250 cd/m2.‚ The minimum contrast as determined by the ratio of the screen brightness at the

maximum DDL compared to the screen brightness at the minimum DDL shall be 250:1.‚ The image display shall be capable of displaying linear patterns of alternating pixels at

full contrast in both the horizontal and vertical directions without aliasing.‚ The display shall be free of discernable geometric distortion.‚ The display shall be free of screen flicker, characterized by high frequency fluctuation

of high contrast image details.

‚ The image display shall be capable of displaying a 5 % DDL block against a 0 % DDL

background and simultaneously displaying a 95 % DDL block against a 100

%background in a manner clearly perceptible to the user.‚ The monitor shall be capable of discriminating the horizontal and vertical low contrast

(1 %) modulation patterns at the display center and each of the four corner locations.”

A calibrated brightness meter measures the 100% and 0% DDL blocks on the test pattern to get

the maximum brightness and contrast ratio.

Figure 12: SMPTE RP133 Test Pattern [12]

6. Future Work

We intend to continue the automation process in order to increase the speed to the test process

including generation of an automated report. Grouping the tests under one GUI will also help

the user complete the tests. Computed radiography (CR) tests can also be added to the suite

(see ASTM E2445). Many of the same or similar tests are shared between DDA and CR.

Acknowledgements

This article is a work performed under the auspices of the United States Department of Energy.

Los Alamos National Laboratory is operated by Los Alamos National Security, LLC for the

National Nuclear Security Administration of the U.S. Department of Energy under contract

DE-AC52-06NA25396. Approved for public release (LA-UR-15-23923); distribution is

unlimited.

References

1. National Nuclear Security Agency, T097 Qualification of Digital Radiographic Imaging

Techniques

2. Los Alamos National Laboratory, SS6K0461 General Specifications for Digital

Radiography of Los Alamos National Laboratory Designed Parts and Assemblies

3. ASTM International, E1695 Standard Test Method for Measurement of Computed

Tomography (CT) System Performance

4. ASTM International, E2737 Standard Practice for Digital Detector Array Performance

Evaluation and Long-Term Stability

5. ASTM International, E2597 Standard Practice for Manufacturing Characterization of

Digital Detector Arrays

6. ASTM International, E2698 Standard Practice for Radiological Examination Using Digital

Detector Arrays

7. ASTM International, E2002 Standard Practice for Determining Total Image Unsharpness in

Radiology

8. Vision in X Industrial Imaging, iSee! Professional User Manual

9. ASTM International, E2445 Standard Practice for Performance Evaluation and Long-Term

Stability of Computed Radiography Systems

10. ASTM International, E1570 Standard Practice for Computed Tomographic (CT)

Examination

11. The Society of Motion Picture and Television Engineers, RP133 Specifications for Medical

Diagnostic Imaging Test Pattern for Television Monitors and Hard-Copy Recording

Cameras

12. Rich Franzen's PNG Gallery, http://r0k.us/graphics/pngLibrary.html