TB, RJ, PMB/206520, 3/02/2006

INSTITUTE OF PHYSICS PUBLISHING PHYSICS IN MEDICINE AND BIOLOGY

Phys. Med. Biol. 51 (2006) 1–17 UNCORRECTED PROOF

Quantitative assessment of computed radiographyquality control parameters

O Rampado, P Isoardi and R Ropolo

Struttura Complessa Fisica Sanitaria, Azienda Ospedaliera San Giovanni Battista,Corso Bramante 88, 10126 Torino

E-mail: orampado@molinette.piemonte.it

Received 24 August 2005, in final form 12 January 2006Published DD MMM 2006Online at stacks.iop.org/PMB/51/1

AbstractQuality controls for testing the performance of computed radiography (CR)systems have been recommended by manufacturers and medical physicists’organizations. The purpose of this work was to develop a set of imageprocessing tools for quantitative assessment of computed radiography qualitycontrol parameters. Automatic image analysis consisted in detecting phantomdetails, defining regions of interest and acquiring measurements. Thetested performance characteristics included dark noise, uniformity, exposurecalibration, linearity, low-contrast and spatial resolution, spatial accuracy,laser beam function and erasure thoroughness. CR devices from two majormanufacturers were evaluated. We investigated several approaches to quantifythe detector response uniformity. We developed methods to characterize thespatial accuracy and resolution properties across the entire image area, based onthe Fourier analysis of the image of a fine wire mesh. The implemented methodswere sensitive to local blurring and allowed to detect a local distortion of 4%or greater in any part of an imaging plate. The obtained results showed thatthe developed image processing tools allow us to implement a quality controlprogram for CR with short processing time and with absence of subjectivity inthe evaluation of the parameters.

1. Introduction

Computed radiography (CR) is at this moment the most common digital radiography modalityin radiology departments, in place of conventional screen film systems. There are importantdifferences in the quality control approach between the traditional film screen radiologyand CR. In conventional radiography, the radiation detector and the display device of theradiograph are the same object. With the CR technology, the detector is a photostimulablephosphor screen, usually indicated as an imaging plate (IP). Digital image data are extracted

0031-9155/06/000001+17$30.00 © 2006 IOP Publishing Ltd Printed in the UK 1

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Table 1. CR systems and IP evaluated in this study.

Manufacturer CR devices Phosphor screen

Kodak CR 800, 900 & 950 GP 25 & HRPhilips (Fuji) AC-PCR 5000 ST-VN

from the exposed IP and used to provide the image on a monitor or film. Therefore, CR andgenerally digital radiography allow us to distinguish between the quality analysis of detectorsand the analysis of the display system.

A quality control program to inspect CR performance includes a set of exposures with testobjects and an analysis of the obtained digital image data. Several manufacturers establishedguidelines for acceptance testing (Kodak 2001, AGFA 1999), but there are no industrystandards for specifying the performance of these devices and this causes a lack of uniformityin measurement procedures among different manufacturers. Preliminary guidelines of the TaskGroup No. 10 of the American Association of Physicists in Medicine (Seibert et al, Sameiet al 2001) provide a first comprehensive standardized testing protocol and recommend uniformquantitative criteria for the satisfactory performance of CR devices. A limitation of theseguidelines lies on the fact that many of the evaluation procedures are not fully quantitativeor can be influenced by the subjectivity of the examiner, such as the evaluations of limitingresolution and noise performance. In a typical radiology department with a CR system theremay be hundreds of IPs. Some quality tests have to be made on every IP and this may cause aconsiderable increase in the quality control workload for medical imaging physicists.

The aim of this work was to develop a set of software processing tools in order to make acomplete quantitative assessment of computed radiography quality control parameters. Thesetools should reduce the time needed to perform the CR quality tests and avoid any subjectiveinfluence in quality parameters evaluation.

2. Materials and methods

As listed in table 1, four CR devices (one for each model) in use at two different radiologydepartments from two different CR manufacturers were evaluated. Test images in DICOMformat were sent from the CR devices to our workstation (HP d530 2.8 GHz Pentium IV)through the local network. Table 2 shows the definitions of the quantities of interest for CRcharacteristics evaluated in this work. The image processing tools were developed as plugins(Java classes) of the public domain Java program ImageJ (Wayne Rusband, National Instituteof Health, USA, http://rsb.info.nih.gov/ij/). The freeware java editor Jcreator (Xinox Software,Delft, Netherlands) was used to edit the plugins, and they were compiled using ImageJ on awindows 98 platform but, because of Java properties, they are also usable on other platforms.All image processing procedures were automatically performed on groups of images, openingevery image sequentially and writing an output table containing the measurements. Every platewas identified by the PLATE ID number extracted from the DICOM header. The performancecharacteristics indicated in table 2 correspond to those defined in the summary presentation ofAAPM TG10 tests proposed by Samei et al 2001, and we used the same exposure conditions.Differences from the TG10 tests only concerned the test image processing and the objectiveindicators. The dark noise and the uniformity tests were applied to all the screens, whereasthe other tests were applied to one screen for each type and size. The dosimeter used toverify exposure was a solid state detector Unfors Mult-O-Meter (Unfors Instruments, Billdal,Sweden).

Quantitative assessment of computed radiography quality control parameters 3

Table 2. Objective indicators of CR performance characteristics.

Performance Characteristic Objective Indicators

Dark noise Average signal and its standard deviation within 80% of the image areaa

Exposure index valueb

Uniformity Signal standard deviation within 80% of the image areaa

Maximum difference between quadrants average pixel valuesb

Differential and integral uniformity evaluated on a 1 cm × 1 cm ROI matrixc

Exposure calibration Exposure indicator response normalized to a 1 mR entrance exposurea

Linearity and autoranging Slope of the system response (expressed in terms of logarithm of exposure)versus logarithm of actual exposurea

Noise and low-contrast Correlation coefficient of the linear fit to logarithm of pixel value standard deviationresolution versus logarithm of actual exposurea

Number of phantom details with a contrast noise ratio above a specified thresholdc

Limiting resolution Modulation transfer function valuesa

Spatial accuracy Difference between measured and actual distances in the orthogonal directionsa

Laser beam function Jitter dimension in pixelsa

Resolution Uniformity Differences between subarrays Fourier spectrum peak amplitudec

Spatial accuracy uniformity Differences between subarrays Fourier spectrum peak positionc

Erasure thoroughness Average signal and standard deviation within 80% of the reread unexposed imagea

Contrast-noise-ratio of the supposed ghost imagec

a Definitions according to AAPM TG10 (Samei et al 2001).b Definitions according to Kodak Guidelines (Kodak 2001).c Definitions introduced in this study.

2.1. Dark noise, exposure calibration and linearity

CR systems have a wide latitude response and the ability of these devices to accommodatea wide exposure range provides a relatively consistent appearance, even in the case of underor over exposed images. In order to avoid image repetitions, technologists may tend towardsoverexposure (Willis and Slovis 2005), so a dose feedback is absolutely necessary for dosemanagement in CR. An indicator of the average incident exposure on the imaging plate isprovided by CR systems, in order to verify proper radiological techniques. Each manufacturerhas a specific method for providing this exposure indicator, but the relationship between itsvalue and the exposure value is known. For Kodak IP GP25, the exposure index is proportionalto the logarithm of the exposure E in mR (Goldman 2004):

EIGP = 1000 log(E) + 2000 (1)

and the same relationship exists between the pixel value of general purpose IP and the exposure.For Kodak IP HR the relationship is the (1), but the constant is 1700 rather than 2000. Forthe Fuji IP used in this study, the exposure indicator is called sensitivity (S) and it is inverselyproportional to the exposure E in mR:S = 200/E (Fuji 1993). The relationship betweenthe pixel value and the exposure depends on the exposure data recognizer (EDR) mode andprocessing menu selections. In the Semi EDR this relationship is (Tucker and Rezentes 1997):

PV = 512 − (1024/L) × [log E + log(S/200)] (2)

where L is the latitude set.Test images obtained from the reading of unexposed IPs (dark noise evaluation) or for

those exposed at different doses (system efficiency and linearity) are used to check whether theexposure indicator value is provided correctly. Its numerical value is present in the DICOMheader of the image file, so no post processing is needed to perform these tests. The developedplugin queried the DICOM header by string manipulation instructions. Groups and elements

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(a) (b) (c)

Figure 1. Schematic view of the three methods used to define uniformity parameters, withindication of the ROI and of the uniformity indices.

for the location of the exposure indicator in the DICOM header were ‘0018,1405 Relative x-rayExposure:’ for Kodak and ‘0018,6000 Sensitivity:’ for Fuji. An indication of the exposurelevel may also be obtained by calculating the average pixel value in a central region of interest(ROI) on the 80% of the plate area (PV80%). The dark noise evaluation is only affected by theCR reader and plate characteristics, because no exposure is involved in the test. The exposureindicator value obtained from the reading of the unexposed plate should not exceed a specifiedthreshold: PV80% > 744 for Fuji, EIGP < 80 and EIHR < 380 for Kodak (Samei et al 2001).

The exposure calibration and linearity are strongly dependent on the exposure conditions(additional filtration, distance, etc), and therefore it is very important to check the constancyof these parameters, in order to perform the test correctly. The linearity of the exposureindicator value is tested by exposing the same IP to approximately 0.1, 1 and 10 mR (1 mR =2.58 × 10−7 C kg−1) entrance exposures in a sequence of three exposure-reading cycles.

2.2. Uniformity

Uniformity of response is a fundamental parameter for detectors in all medical imaging fields.A uniform exposure should result in a uniform response of the CR system. Many definitions(Kodak 2001, Samei et al 2001, Masden 1997, AAPM 2005) have been proposed to establishuniformity indicators. In this study we used three different approaches (figure 1):

(1) After the image separation into four discrete quadrants, a uniformity index (Uquad) wasevaluated as the difference between the average values (PV i , i = 1, . . . 4) of the twoquadrants with the highest (MAX(PV i)) and lowest (MIN(PV i)) average pixel values(Kodak 2001). Only half of the actual quadrant area was used in the calculation of theaverage pixel value in each quadrant (figure 1(a)).

(2) Upvsd: standard deviation of pixel value within 80% of the image area (Samei et al 2001).(figure 1(b)).

(3) Uint and Udif: a matrix of adjacent square ROI (1 cm × 1 cm) was used to analysethe image area. Uniformity indicators used in this case were similar to those usedin nuclear medicine (Masden 1997) or for the quality assurance of monitors (AAPM2005). Differential uniformity here refers to the maximum percentage difference betweenadjacent ROI average pixel values (PV i+1 and PV i in the formula shown in figure 1(c)).Integral uniformity refers to the percentage difference between the maximum(MAX(PV i)) and the minimum (MIN(PV i) ROI average pixel value.

Being the pixel value defined differently by the two systems under test, in order to use theseindices for both systems, the pixel value was reassigned in terms of exposure before theanalysis, using inverse formulations of the relationship between pixel value and exposure:

PVexp = 10(PV −2000)/1000 (3)

Quantitative assessment of computed radiography quality control parameters 5

for Kodak GP (for Kodak IP HR the relationship is the same, but the constant is 1700 ratherthan 2000) and

PVexp = 10(512−PV )·(L/1024) + 200/S (4)

for Fuji. As a consequence, Uint and Udif are expressed as percentage values and Upvsd and Uquad

are expressed in mR units, as the exposure value calculated for every pixel. The contributionof the heel effect to the uniformity index values was evaluated comparing several imagesobtained with two sequential half-exposures between which the orientation of the cassettewas reversed, and with two sequential half-exposures without change of orientation. Thepercentage difference between the images (evaluated using the same ROI of the uniformityanalysis) resulted to be less than 2%.

Uniform exposures also provide a stringent test for many common image artefacts that canoccur in CR systems. Both AAPM and manufacturers guidelines state that all images shouldbe examined for banding, black or white spots and streaks. In computed radiography, as wellas in other imaging techniques, image artefacts are a source of ‘noise’ that can degrade thediagnostic quality. To investigate these artefacts we used an algorithm for automatic detection.In the adjacent ROI analysis, this algorithm detected those pixel with values that differed fromthe ROI mean by more than 3%, being about 1% the relative standard deviation. The imagewas then segmented into three levels, assigning white, grey or black colour to pixels below,within or above the range of the mean value ±3%. A median 2 × 2 filter was applied to thissegmented image in order to remove black or white isolated pixels, which are related to noisepeaks rather than to wide defects, and non-uniform regions were thus highlighted.

2.3. Noise and low-contrast resolution

Low-contrast resolution characterizes detectability of a low-contrast object, and is influencedby several factors, including the object size, contrast between the object and the background,image noise and the system’s MTF. Image noise is primarily determined by the dose settingof the x-ray tube, the detector efficiency and the reconstruction algorithm. In this study thenoise and low-contrast resolution properties of the CR system were tested by acquiring threeimages of a low-contrast phantom, using 0.1, 1 and 10 mR, at 70 kVp beam with 1 mm ofCu filtration. Test images were acquired turning off any vendor specific nonlinear imageprocessing. The phantom used was the TOR [CDR] (Faxil, Leeds, UK) that consists of a flatPerspex disc carrying four types of test pattern. The so-called low-contrast sensitivity testpattern, comprising 17 circular details (11 mm diameter) with x-ray contrast values specifiedby vendor in the range 0.075–0.002, was used in this study.

The noise is quantified by the standard deviation of the pixel value in a fixed smallregion of the image (PVSD). The logarithm of noise is linearly dependent on the logarithm ofexposure E (Christodoulou et al 2000):

log(PVSD) = a + b log E (5)

with a correlation coefficient >0.95.The evaluation of the minimum discernible contrast to characterize the low-contrast

resolution is generally performed in a subjective fashion on a test phantom with a low-contrast resolution pattern. In order to make a more objective analysis, we developed aspecific software tool, with an algorithm that automatically locates the contrast objects andperforms measurements. The automatic recognition of the phantom details can be performedin different ways. General registration methods to match the test image (independently ofthe phantom used) with a pre-processed template have been proposed (Kwan et al 2003),but the computational time necessary to perform them is quite long (several hours for each

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Figure 2. Regions of interest used on the TOR phantom image to calculate the contrast-to-noiseratio of the low-contrast objects.

registration). We developed a specific algorithm that recognizes geometrical reference linesand points present in the phantom, performed in the following steps:

– the coordinates of the centre of the phantom were located following the application of anedge detection method;

– the relative rotation of the phantom image was defined by the profile plot analysis of aproperly positioned circular line;

– for every contrast object, measurements of the mean and standard deviation of pixel valueswere performed in an internal circular ROI (7 mm diameter) and in a surrounding ROI(14 mm internal diameter and 17 mm external diameter), as shown in figure 2;

– the contrast-to-noise ratio was calculated for every object using the following relationship:

CNR = PVin − PVout√σ 2

in + σ 2out

. (6)

The number of details with a CNR value above a specified threshold (for each exposure level)could be used for constancy quality control.

2.4. Spatial resolution

Quality control guidelines suggest to qualitatively establish the limiting spatial resolutionwith an image of a line pair pattern device. The resolution of a CR system should be moreobjectively evaluated by measuring the frequency-dependent modulation transfer function(MTF). Several publications discuss details of different methods to perform this measure(Fujita et al 1992, Bradford et al 1999, Samei and Reimann 1997, Stierstorfer et al 1999). InOctober 2003, the International Electrotechnical Commission (IEC) published an internationalstandard (IEC 2003) containing specifications regarding measurements of the detector quantumefficiency (DQE) and of the MTF that should be used when manufacturers describe the imaging

Quantitative assessment of computed radiography quality control parameters 7

performance of an x-ray imaging device. The IEC standard provides many details on the objecttest and x-ray source, but it could be difficult to apply this exact procedure in the context ofa constancy control with limitations in time and instrumentation: we think that one of theother methods proposed and described could be implemented for this purpose. In the softwarewe developed, methods based on the image of a slit camera, an edge and a bar pattern wereimplemented following the instructions presented in the referenced literature (Bradford et al1999, Samei and Reimann 1997, Stierstorfer et al 1999). A slit camera with slit width 10 µm,slit length 10 mm, tantalum as slit material and slit thickness 1.5 mm (Model 07-624,GAMMEX-RMI, Middleton, WI) and a Huttner test pattern (Type 18, Faxil, Leeds, UK)with 26 line pair groups (1–20 cycles/mm) were used to perform the measurements. The edgeused to measure the edge spread function was the border of the Huttner test pattern, made ofa 30-µm-thick lead foil embedded in glass.

2.5. Spatial accuracy and laser beam function

Accurate imaging requires preserved geometric relationships. A metal rule or plate maybe used to obtain a test image that allows a comparison between actual and measureddimensions. Automatic edge detection was used to make measurements in different regions ofthe image.

The analysis of the metal object edge profile is also useful to verify the beam laser function.Object edges should be straight and continuous over the full IP length. Under or overshootof the scan lines in light to dark transitions along the object edge indicates a timing error,or laser beam modulation problem and results in occasional incorrect definition of the objectedge. This effect can be indicated with the term ‘jitter,, generally used for any distortion of asignal or image caused by poor synchronization. In order to make quantitative evaluation ofthe jitter present, the edge profile was analysed in the following way:

– two rectangular region of interest were automatically defined with a large portion (90%)of the edges inside (figure 3(a));

– for any horizontal row of the ROI indicated as 1 in the figure and for any vertical row ofROI 2, the position of the edge was determined by analysing the profile of pixel values,as the interpolated position of the midpoint between the left side and the right side of theprofile;

– a linear fit was calculated for the two series of edge position values, thus obtainingpositions of an ideal edge with no jitter (figure 3(b));

– the differences between the actual edge positions and the fitted position values wereplotted to check the presence of jitter above one pixel.

The number of these jitters was the quality parameter that we used.

2.6. Resolution and spatial accuracy uniformity

The previously described spatial accuracy and resolution tests only provide local information,whereas Samei et al 2001 suggest to characterize these properties across the entire image area,using a screen–film contact test tool (wire mesh test). In this way, it is possible to verifythe resolution and spatial accuracy uniformity over the total area of the phosphor receptor,checking the sharpness and distortion of the obtained image over the whole field of view. Theobject used to perform this test was a mammographic film/screen contact test tool Gammex157A (GAMMEX-RMI, Middleton, WI), a wire mesh encased in plastic with dimensions25.5 × 31.5 cm2. The distance between the centre of the wires was 0.63 mm, with a frequencyof 1.58 mm−1.

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(a)

(b)

Figure 3. (a) Test image obtained exposing a metal plate and indication of ROIs for jitter and laserbeam function analysis. (b) Plot of the edge position values and linear fit that represent the idealedge with no jitter.

The degree of regularity and resolution governing a uniform pattern in digital imageis most conveniently analysed not in the spatial domain, but in the frequency domain. Inorder to establish a quantitative criterion for this test we made a Fourier analysis of the testimage. Within the acquired digital image array, a set of subarrays (size 128 × 128 pixels)arranged contiguously on the entire image area was identified. Prior to Fourier transformation,the subarray data were modified by multiplying the subarray values by a Hamming windowfunction of the type commonly used in spectral estimation (Pratt 1991), in order to avoidaliasing from discontinuities at the edge of the subarray. The two-dimensional Fouriertransform of each subarray was then computed. Power spectra in the fast-scan and in theslow-scan directions were estimated by averaging the central row or column and ±1 rows orcolumns within each transformed subarray. These power spectra resulted to have a main peakat the pattern frequency and a second peak at the double of the pattern frequency (secondharmonic). Position and width at half amplitude of the main peak can be considered asindicators of absence of distortion. The position of the peak was determined with an accuracyof ±0.01 mm−1 using a Gaussian fit of the spectrum values near the maximum. The relativeamplitude of the two peaks can be considered as an indicator of sharpness. This relativeamplitude Ap was defined as follows:

Ap =∑+1

i=−1 (PS[fc + i] + PS[2fc + i])∑128i=2 PS[i]

(7)

Quantitative assessment of computed radiography quality control parameters 9

where PS[i] are the power spectrum values and fc is the position of the characteristic frequencyof the pattern. These indicators should have the same value for every subarray of the testimage.

In order to establish a range of performance levels of this test we used a set of referencetest images. The resolution and spatial accuracy uniformity of a CR system was first testedby repeating the MTF analysis and geometrical measurements several times with slit cameraand small metallic rules positioned in different parts of the imaging plate. This CR systemwas used to obtain a set of images of the wire mesh test. These images were modified withdigital image processing tools provided by the ImageJ software to simulate problems thatmight occur during the acquisition process. Specifically, we applied a scaling factor to the testimages in both scan directions, in order to simulate incorrect velocity in these two directions.In other cases, we applied Gaussian blurring filters with different kernels to simulate a lossof sharpness that might be experienced with a laser spot of increased cross-sectional diameter(Rowland 2002).

2.7. Erasure thoroughness

The plate, if improperly or insufficiently erased, can potentially give rise to image artefacts.The test of the erasure capability is performed by exposing an erased IP (unused for 1 hbefore the test) at high exposure levels (50 mR) with a centrally placed high-contrast testobject (a thick lead block), reading the plate and re-exposing the plate to a uniform incidentexposure of about 1 mR. The re-exposed image should be free from ghost artefacts. The ghostsignal is quantified in our software by the percentage difference between the average pixelvalue in the region previously occupied by the high-contrast object and in the surroundingarea.

3. Results and discussion

The image processing tools were used to analyse images obtained from quality controlsperformed on a total of 125 IPs (82 Kodak GP25, 4 Kodak HR and 39 Fuji ST-VN). KodakIPs were tested with all the three Kodak CR readers and Fuji IPs with the unique Philps CRreader. The uniformity test and the dark noise test were applied to all the IPs, the other testswere performed on a single IP for each type and size of the screen and for each CR reader.The number of test images required for each single reader and IP type and size was 34, andthe number of images processed in this study was 430, with an exposure and acquisition timeof 8 h and a total processing time of 2 h.

3.1. Dark noise, exposure calibration and linearity

The exposure indicator values, obtained on flat field images exposed at a given exposure level,were correct according to the tolerance specified in quality control protocols for the 90% ofthe IPs which we analysed. 13 IPs (8 Kodak and 5 Fuji) had exposure indicator values outof the tolerance. The 8 Kodak IPs had all the same size (24 × 30) and also the 5 Fuji IPs(18 × 24). The CR reader had a guided procedure to re-calibrate the exposure indicator, whichinvolved the readout of an IP exposed to a known value (e.g. 20 mR for Kodak) for every IPsize and type. We found that the calibration date of the IPs with the exposure indication outof tolerance was older (more than 3 years) than the calibration date for the other IPs (about1.5 year). After a re-calibration of the system all IPs had correct exposure indicator values.Also, the linearity was good for all the IPs and CR systems, with a minimum correlationcoefficient value of 0.9994 between the exposure calculated from exposure indicator and the

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Figure 4. Box and Whisker plots of different uniformity indices for the IP with apparent defects(group Y) and without (group N).

actual one. We found dark noise values above the specified tolerance for the Kodak CR 900reader only, with EI values ranging between —110 and 140, the specified tolerance being of80. We discussed these data with the manufacturer technician and hence supposed that theproblem might have been related to the calibration of the laser-photomultiplier chain, or tothe infiltration of ambient light inside the CR reader. These hypothesis were then discardedbecause a re-calibration of the laser-photomultiplier chain and the variation of the room light(also trying the total darkness) did not change obtained EI values. No other differences inimage quality indicators were found between this CR reader and the others.

3.2. Uniformity

The uniformity analysis has two purposes: the evaluation of a global uniformity index andthe detection and classification of artefacts. In order to study the information related to thedifferent uniformity indices previously defined, the IPs were divided into two groups: IPswith artefacts (group Y) and IPs without artefacts (group N). 22 IPs (16 Kodak and 6 Fuji)were assigned to group Y, and 103 IPs (70 Kodak and 33 Fuji) to group N. This classificationwas made by using the algorithm previously described to highlight the presence of defects.According to Cesar et al 2001, the most frequent artefacts are white irregular lines, causedby cracks or scratches on the IP; white straight lines, caused by dirt on the light guide in theIP reader; wide bright areas, caused by damage to the IP protective layer. In our sample wefound two IPs with small white irregular lines, 18 IPs with wide bright areas and two IPs withboth these artefacts. The wide bright areas were often located near the corners of the IPs.

The distribution of results from the four uniformity indexes for the two groups is shownin figure 4. A Student t-test was applied to assess the significance of the differences betweenthe two groups, and we found p-values lower than 0.005 for all indices. This means thatall indices were influenced by the presence of artefacts, but from figure 4 it is easy to seethat sometimes the same index value was obtained for IPs with and without artefacts. As aconsequence it was difficult to define a threshold value for these indices which could be usedto automatically distinguish IPs with artefacts. Only for the Udif index the two distributionswere almost separated, and a reference value could be 5% because all IPs with Udif index >5%belonged to the Y group. Nevertheless, a visual inspection of the uniformity test image shouldalways be performed; in fact, we found, for example, that the two IPs with small irregular lines

Quantitative assessment of computed radiography quality control parameters 11

Figure 5. CNR values for different details of the TOR [CDR] phantom and for three exposurelevels.

had an Udif value lower than 5%. In our opinion, the segmentation of uniformity images witha repeatable criteria like the one used in this study allows us to perform the visual analysis ina more objective way.

The other indices had a more consistent overlapping between the two data distributions,and therefore it was more difficult to define a threshold value related to the presence ofartefacts. For Uquad and Upvsd, this overlapping was caused by the fact that often the artefactswere located totally or partially outside the ROI used for evaluation, or in some cases thespatial extension of the artefacts was small in respect to the ROI area. The evaluation of Uint orUquad allows us to investigate the overall uniformity, which is influenced, for example, by thepresence of a gradual variation between two opposite edges of the plate that is not perceivedas an artefact (and probably does not affect the Udif index).

22 IPs were analysed before and after the periodic cleaning procedure (performed everymonth). We found that Upvsd is the best index to use when checking the cleanliness statusof the IP, with an average difference in value for the same IP of 31% (range 25–41%). Theother uniformity indices here considered were not strongly influenced by the difference in PVdue to dirt on the IP because they were all based on ROI average values. All the IPs withoutartefacts had a Upvsd value lower than 0.5 mR; therefore a greater value could indicate that thecleaning procedure is not being performed with the correct periodicity or accuracy.

3.3. Noise and low-contrast resolution

CNR values in the range of different details and at different exposure values are shown infigure 5. In order to assess the uncertainty in the CNR evaluation, new test images wereproduced five times at each of the three exposure levels, using the same CR reader, but withdifferent IPs of the same type and size. The error bars in the figure indicate the standarddeviations for CNR values; with CNR values above 0.4 relative standard deviations were

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Figure 6. Presampled MTF of a Kodak 18 × 24 IP determined using the slit, the edge and the barpattern methods.

below 20% for exposures of 0.1 mR, and below 10% for other exposure levels. We used thisvalue of 0.4 as the threshold needed when checking the constancy of the number of detailswith a higher CNR, resulting in an average number of 6, 12 and 13 for the three exposurelevels. These values should be evaluated during the acceptance test and should be consideredas reference for constancy tests, with a tolerance of two visible details.

With regard to the assessment of contrast detectability, an important issue is the postprocessing of the produced test image. According to the proposed quality control protocols,the low-contrast phantom image should be analysed once the window width and level havebeen optimized. This introduces another subjective factor in the test procedure. The histogramanalysis on the CR reader modifies the pixel value according to the selected window widthand window level. An advantage of using the CNR to test the low-contrast resolution is thatadjustment of window and level does not affect CNR, because the average pixel values andstandard deviations are modified in a linear manner, given that the vendor-specific, nonlinearimage processing is disabled. Infact, a n-bit pixel value PV is modified by the application of anadjustment of window width (WW) and level (WL) according to the relationship (Passariello2000):

PVW = 2n

WW· (PV − WL) + 2n−1 = 2n

WW· PV − 2n

WW· WL + 2n−1 = αPV + β. (8)

As a consequence, the mean value and the standard variation of a ROI are modified in thefollowing manner:

PVW = αPV + β σW = ασ. (9)

These relationships may be used to verify that the CNR defined by (6) is not affected by theadjustment of the window width and level:

CNRW = PVW in − PVW out√σ 2

W in + σ 2W out

= αPVin + β − αPVout − β√α2σ 2

in + α2σ 2out

= α(PVin − PVout)

α

√σ 2

in + σ 2out

= CNR.

(10)

3.4. Spatial resolution

Figure 6 shows the measured presampled MTF in the fast-scan and in the slow-scan directionsfor a GP 18 × 24 plate using the slit, the edge and the bar pattern devices. The results obtainedfrom the three methods were similar. Within the frequency range of 1–4 cycles/mm, the MTF

Quantitative assessment of computed radiography quality control parameters 13

Table 3. Frequencies at 0.1 MTF for CR readers and IP types and sizes used in this study.

Frequency (lp mm−1)at 0.1 MTF

CR reader IP type Image size (cm2) Fast scan Slow scan

Kodak CR-800 GP 35 × 43 3.11 4.1GP 24 × 30 3.71 4.31HR 24 × 30 4.55 5.50GP 18 × 24 3.79 4.29HR 18 × 24 4.95 5.39

Kodak CR-900 GP 35 × 43 3.09 4.25GP 24 × 30 3.71 4.19HR 24 × 30 4.57 5.43GP 18 × 24 3.78 4.26HR 18 × 24 4.92 5.33

Kodak CR-950 GP 35 × 43 3.04 4.19GP 24 × 30 3.68 4.13HR 24 × 30 4.49 5.42GP 18 × 24 3.76 4.20HR 18 × 24 4.90 5.31

Philips AC-PCR 5000 ST-VN 35 × 43 3.21 3.25ST-VN 24 × 30 3.18 3.22ST-VN 18 × 24 3.43 4.02

values obtained with the three methods were compared at the cycles/mm values of the barpattern device (about every 0.25 cycles/mm), observing MTF differences less than 0.028, witha mean difference of 0.023.

Values obtained from MTF measurements in terms of frequency at 0.1 MTF are shown intable 3. Generally, there were little differences in the MTF values measured on the three Kodakreaders for each IP type and size, with maximum differences of 2% in the fast-scan directionand of 4% in the slow-scan direction. Similar to the Kodak plates, the Fuji MTF valuescorresponding to the slow-scan direction were higher than the fast-scan direction. However,there was a much larger difference between the slow-scan and fast-scan MTF values for theKodak plates.

The main goal of these MTF measurements was neither the evaluation nor the comparisonof the performance of digital radiographic systems, but rather the quality control of thereproducibility of the spatial resolution measurement. As a consequence, test images wereobtained without particular attention to the test device position and alignment. The angulationof the slit or the edge was in the range 5–8 and was then measured by the software in thepresampling process. Repetition of measurements on the same CR system gave results whichwere very similar with a maximum difference of 4%. We think that a tolerance of 10% (whichis more than two times the maximum difference between repeated measurements) could thusbe used in order to test the constancy of the spatial resolution, according to Seibert et al.

3.5. Spatial accuracy and laser beam function

Measurements of spatial accuracy and laser beam function were performed using the four CRreaders afore mentioned and one plate for each size and type. Differences between measuredand actual distances in the orthogonal directions were less then 2% for all the imaging platesanalysed. Figure 7 shows jitter values in pixels, evaluated as indicated in the material andmethods section (difference between the actual edge positions and the fitted position values).

14 O Rampado et al

Figure 7. Differences between the actual edge positions and the fitted position values.

Figure 8. Peak position values of the power spectrums obtained from a wire mesh test imageprocessed with several scaling factors. The two dotted lines define the range of the values obtainedfrom the original image (mean value ±3 σ ).

Maximum differences were less than 0.5 pixels. All plates analysed had no jitter above0.5 pixels.

3.6. Uniformity of resolution and uniformity of spatial accuracy

In order to determine the sensitivity of the measurements for uniformity of resolution and foruniformity of spatial accuracy, we modified images of the wire mesh object using rescalingand blurring digital image processing tools and applied the analyses to the modified images.

We investigated the effect of a scaling factor on the peak position of the power spectra.Theoretically, the peak position after the rescaling is simply obtained by the ratio of the oldpeak position and the scaling factor. For example, with a scaling factor of 0.5, the new peakposition will be twice the value of the old peak position. Figure 8 shows peak position valuesobtained from the Fourier analysis of adjacent subarrays, after the application of differentscaling factors. The error bars were defined as three times the standard deviation σ , becauseall the obtained values are comprised in the ±3 σ range. The two dotted lines define the rangeof the values obtained from the original image (1.58 ± 0.02). A local warping of ±4%, whichis not visible with a simple visual inspection, results in values exceeding the indicated range,and is thus detectable with this analysis. We repeated the analysis with partial scaling of theimages, and ROIs located in the re-scaled region show the peak value out of the established

Quantitative assessment of computed radiography quality control parameters 15

Figure 9. Peaks’ relative amplitude Ap and MTF values obtained from images test (wire meshand slit camera) after the application of Gaussian blurring filters with different kernels. The twodotted lines define the range of the values obtained from the original image (mean value ±3 σ ).The effect of the blurring filters on a detail of the slit camera image and of the wire mesh image isalso shown.

range. Depending on the pixel dimension, the 128 dimension of the subarray corresponds toa square ROI of 0.6–2 cm.

A gradual loss in resolution power was generated convolving the wire mesh test imageswith Gaussian blurring filters with several kernels of amplitude in the range 1–5 pixels. Thisshould simulate the effect of an increase in the cross-sectional diameter of the laser beam,the intensity of which has a Gaussian profile (Rowland 2002). The same processing tool wasapplied to the image of a slit camera, in order to evaluate the effect on MTF values. Figure 9shows the peaks’ relative amplitude Ap previously defined by equation (7), and MTF valuesat 1 and 2 lp mm−1 versus Gaussian blurring kernel. The error bars of Ap were defined asthree times the standard deviation and also in this case all the values obtained are comprisedin the ±3 σ range (corresponding to about 10% of Ap). A value of the peaks’ relativeamplitude out of this range means that there is a loss of resolution power similar to thatcaused by the application of a blurring filter with kernel greater than 1. The loss in MTFvalues at 1 lp lp mm−1 and 2 lp mm−1 for a kernel of 1 was respectively −10% and −30%.The analysis was repeated with partial blurring of the image and examples of results are shownin table 4. We found that a good criterion to apply in order to automatically find resolutionnon-uniformity could be to verify that the relative amplitude of the peak values are comprisedin the range Ap mean value ±0.07 (proposed tolerance range in the table).

3.7. Erasure thoroughness

The percentage difference between the average pixel value in the region previously occupiedby the high-contrasthigh-contrast object and the surrounding area was less then 0.9%, that isless then the typical standard deviation of 2% of the IP at this exposure level. The uniformity of

16 O Rampado et al

Table 4. Examples of Ap values obtained from the Fourier analysis after the application of aGaussian blurring filter with kernel = 2 on different portions of a wire mesh test image.

Ap valuesPercentage of imagearea with blurring (%) Mean Min Max Tolerance range

0 0.68 0.62 0.73 0.61–0.755 0.66 0.48 0.72 0.59–0.73

10 0.65 0.47 0.72 0.58–0.7220 0.64 0.49 0.72 0.57–0.7150 0.62 0.52 0.72 0.55–0.6975 0.58 0.47 0.72 0.51–0.65

response of the analysed IP is not affected by a previous high exposure with high-contrasthigh-contrast objects.

4. Conclusions

We have developed a package of software tools for the implementation of a complete setof quality tests on CR systems. We applied it to CR systems of two manufacturers. Theuniformity analysis with different approaches allowed us to understand more deeply thecorrelation between the presence of artefacts and the obtained parameter values. Upvsd andUquad are good indices in quantifying an overall uniformity of the IP, but their values are notcorrelated with the presence of the most frequent artefacts. The Udif index may be used toautomatically detect the presence of artefacts such as wide bright areas, but it is not sensitive tosmall irregular lines. The constancy of low-contrast resolution was verified using the contrast-to-noise ratio values obtained from an automatic ROI analysis, avoiding any subjectivity inthe evaluation procedure. The parameters defined to assess the spatial accuracy and resolutionuniformity proved to be suitable quantities to perform such tests. The analysis of the peakpositions of the power spectra allowed us to detect a local distortion of 4%, which is not visiblewith a simple visual inspection. The relative amplitude of the first and second harmonic peaksAp was found to be sensitive to a loss of resolution power similar to that caused by theapplication of a blurring filter with kernel greater than 1.

The time required to complete one set of measurement depends on the number and typesof IPs. As an example for a single CR reader with 39 IPs of three different sizes, the timerequired for exposure and acquisition was about 150 min and the processing time was lessthan 30 min. In conclusion, the implemented analysis algorithms allow us to perform a qualitycontrol for CR with a short processing time and with absence of subjectivity in the evaluationof the parameters.

Acknowledgments

The study was supported, in part, by grants from the project ‘Riduzione del rischio associatoall’esposizione a radiazioni ionizzanti per fini medici—Compagnia San Paolo di Torino’.

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Endnotes

(1) Author: Please update reference ‘Seibert et al’.(2) Author: Please check whether the year in reference ‘Stierstorfer and Spahn 1999’ is OK,

as set.

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