CHAPTER 5
Assessment of IMI prototype image quality:
Experimental results
In this chapter we report on the experimental results of the Transfer Func- tions and Thickness-Threshold Detail Detectability analyses for the IMI prototype.
Two international protocols have been followed to perform these mea- surements. The international protocol IEC62220–1-2 [38] defines stan- dards for the measurement procedures of MTF, NNPS, and DQE. The european protocol EUREF [17], defines instead a standard procedure for the image quality assessment by observers trough the determination of the Thickness-Threshold curve. These guidelines have been followed, whereas applicable, in characterizing our demonstrator.
We have discussed the experimental results and compared them with the theoretical model of the ideal SPC system (developed in Section 2.6).
Furthermore we have discussed the physical limiting factors of the system, showing that they are mainly related to the system engineering. Finally, new technological solutions have been proposed to further improve the image quality of the system.
5.1 Experimental Set-up
For the Transfer Functions and thickness-threshold measurements the x- ray tube was set at 28kV p and 50mAs. An anode/filter combination of Mo/Mo has been used, and a 4mm thick aluminum filter was added at the tube output. The added filter is used to simulate the breast, and so the incident spectrum on the detector as in clinical conditions. To simulate the x rays absorption of a standard breast (60mm thick and with 50% of glandular tissue) a slab 50mm thick of PMMA positioned on the detector surface is commonly used. Nevertheless, as suggested by the IEC protocol, an aluminum slab positioned at the tube output is preferable respect to the PMMA to avoid undesired scattered radiation. In Fig. 5.1 we show the simulated spectrum of the the x rays incident on the detector surface [36].
Figure 5.1: The mammographic spectrum obtained with 28 kV p and a 4mm thick added Al filter. The spectrum has been got by the IPEM catalogue, [36].
The source to detector distance was 50cm. The dose on the detector has been measured with a calibrated Radcal ionization chamber (6M ion chamber). In this exposure condition the air kerma1at the detector surface was (36± 1)µGy.
1Air kerma is a radiation quantity used to express the radiation concentration de- livered to a point. It is defined as the Kinetic Energy Released per unit MAss of air.
5.1. Experimental Set-up 97
This air kerma value corresponds to an average glandular dose (AGD) equal to 2.21mGy. The AGD represents the average dose absorbed by the glandular tissue of a standard breast and it is commonly used in mammog- raphy to quantify the hazard related to the x-ray radiation.
During the transfer function measurements, all the protective layers of the detection head were removed to avoid any measurement bias; this oper- ation has been easily peformred since being the system at the demonstrator stage, an engineered packaging has not yet been implemented.
The raw images acquired by the system have been weighted with a flat-field acquisition, as described in the section 3.4.3. The beam setup used in flat-field acquisitions is the same as that used to acquire the raw images (Mo/Mo 28 kVp and 4 mm thick Al filter), but twice the exposure has been used (72µGy instead of 36µGy). This flat-field acquisition is performed in scanning mode to compensate for the spatial nonhomogeneity of the irradiation field due to the heel effect of the x-ray generator [39].
The heel effect is a reduction in intensity of radiation in the exposure area correspondent to the anode end. Due to this effect, the area covered by a detector unit the irradiation field is not spatially uniform and the relative exposure among the pixels changes during the scanning. If the flat-field is not acquired in scanning mode, the corrected image will result affected by a fixed pattern noise.
Finally, the dead and noisy pixels removal procedure has been applied.
A summary of the experimental set-up is reported in table 5.1.
Normally a high statistics in flat-field is advisable to equalize the image, because the flat-field quantum noise will contribute to the noise of the corrected image. Unfortunately with the present two-rows demonstrator, the number of steps and shots necessary to cover the full (18× 24)cm2 area would cause an excessive tube heating and the flat-field image may be acquired with a maximum tube loading of 100mAs. The effect of the flat-field equalization on the image noise will be discussed later in this chapter.
Table 5.1: Parameters used in the transfer functions measurements.
Parameter Value
Tube Anode/Filter combination
Mo anode with 30 µm thick Mo filtra- tion
Tube settings 28 kVp and 50 mAs
Addedd filtration 4 mm thick aluminum, positioned close the tube output
Focus-detector dis- tance
50 mm Air kerma on detec-
tor surface
36 µGy Average Glandular
Dose (AGD) for the standard breast
2.21 mGy
Image processing Flat-field correction and
removal of dead and noisy pixels.
5.2 Presampling MTF of the IMI prototype
5.2.1 Materials and Methods
As discussed in section 2.3.1, to measure the presampling MTF (PMTF) of a digital system, the response to a narrow slit before the stage of sampling (presampling LSF or PLSF) should be measured. This measure is very hard or impossible to carry out. A more practicable way to estimate the PMTF is to measure a finely sampled (or oversampled) LSF (i. e. the LSF measured with a sampling aperture smaller than the physical pixel).
This measurement was described for the first time by Fujita et al., [40][41]. Here the PMTF is estimated as the Fourier transform of the finely sampled LSF obtained from an image of a slightly angulated slit.
In place of a narrow slit, an opaque object with a straight edge may be use to measure the PMTF. The system response to a straight edge is the edge spread function (ESF), which differentiated, gives the LSF. The use of the edge is preferable respect to the slit, because it is easier to produce, require less tube loading, and it is easier to align with the beam [42].
5.2. Presampling MTF of the IMI prototype 99
A picture of an edge, tilted of α degree respect to the y axis, is showed in Fig. 5.2. A group of N consecutive lines are used to reconstruct the oversampled ESF, where N is determined by the condition N = 1/tan(α).
N represents the number of lines needed to laterally shift the edge by one pixel in the x direction. In general N is not an integer number, so it has to be rounded to the nearest integer.
Figure 5.2: Scheme of an edge positioned on a detector matrix with a inter- pixel distance. The edge is tilted of α degree and N = 1/tan(α) is the number of rows needed to laterally shift the edge by one pixel.
Each image pixel has a different relative distance from the edge. The image pixels have to be rearranged by disposing them according to their distance from the edge. Referring to Fig. 5.3, the oversampled ESF is obtained by reprojecting the pixel data of the original edge image along the direction of the edge angle, into a one-dimensional array. The obtained ESF results sampled with a sampling aperture ∆x = a/N , where a is the inter-pixel distance of the physical detector.
One should note that if N = 1/tan(α) is not an integer number, the sampling grid is not uniform: the sampling distance is somewhat larger than a/N for the first N − 1 consecutive samples and smaller for the Nth
sample. Only if the edge position is shifted by exactly one pixel after N lines, the sampling distance in the oversampled profile is uniform and equal to a/N .
Since a lateral shift of the edge by exactly 1 pixel is hardly achievable with an integer number of lines, N is rounded to the closest integer. This approximation introduces a phase effect in the ESF determination and a systematic error in the measured PMTF that will depend by the relative position of the edge respect to the detector grid, as demonstrated by Buhr [43]. In this reference it has been shown that the maximum relative error on the PMTF, due to the phase effect, is expected at the Nyquist frequency and it is less or equal to 1/2N .
Figure 5.3: The oversampled ESF is obtained by reprojecting the pixel data of the original edge image into a one-dimensional array. It is clearly appreciable in the image that, if the number of rows used to recon- struct the oversampled ESF, N = 1/tan(α) is not an integer, the distance between the oversampled ESF values is not uniform.
To measure the PMTF for the IMI prototype we have used the tilted edge method, as recommended in the international IEC 62220–1-2 standard
5.2. Presampling MTF of the IMI prototype 101
[38]. The image of a 70µm thick tungsten edge has been acquired using the beam setup specified in the Section 5.1. The edge has been positioned approximately 15mm above the detector surface and tilted by about 3◦ with respect to the pixel columns.
The finely sampled edge spread function ESF is obtained from the edge image interlacing N = 19 lines across the edge, as previously specified.
In our setup the window size used to determine the PMTF is of 19× 128 pixels and the oversampled ESF total length is of 22.8mm across the edge. Multiple representations of the ESF have been obtained from 24 consecutive groups of N lines across the edge image, and they have been averaged to reduce the effect of the quantum noise in the resulting PMTF.
The oversampled LSF has been derived from the oversampled ESF by discrete differentiation. No smoothing and no fit have been performed neither on the ESF nor on the LSF. A fast Fourier transform has been applied to the LSF to obtain the PMTF, which has been normalized to the zero frequency value. Fig. 5.4 shows an image of the edge acquired by the IMI prototype (in a), the oversampled ESF (in b), and the resulting LSF (in c).
5.2.2 Results and Discussions
Figure 5.5 shows the presampling MTF as a function of the spatial fre- quency (dashed line).
The uncertainty on the experimental data of the PMTF is due both to the phase effect and to the quantum noise of the image. As discussed previously the maximum relative error due to the phase effect is equal to 1/2N ) Since we have used N = 19, the maximum expected relative error due to the phase effect is 2%.
However the uncertainty on the experimental PMTF has been esti- mated by performing eight independent measurements of the MTF. The error due to the phase effect has been taken into account slightly displacing the edge in each measurement. The maximum standard deviation over all
Figure 5.4: The procedure used for the PMTF measurement. a) The image of the tilted edge, acquired by the IMI prototype. b) The oversampled ESF, obtained from 24 consecutive groups of N lines across the edge image. c) The oversampled LSF.
5.2. Presampling MTF of the IMI prototype 103
the frequencies is reached at the Nyquist frequency, and it is equal to 3.5%
of the PMTF value at the same frequency.
Figure 5.5: Presampled MTF as functions of the spatial frequency (dashed line). The solid line represents the PMTF for an ideal system with the same pixel pitch.
We have compared the experimental results with the expected presam- pling MTF of an ideal SPC system with the same pixel pitch. In the section 2.6 it has been calculated that for an ideal SPC system the presampling MTF is the function sinc(πau), where a is the pixel pitch and u is the spatial frequency. In Fig. 5.5 the PMTF of the ideal system is also plotted (solid line).
The presampling MTF at the Nyquist frequency is 60%. The experi- mental curve is in good agreement with the ideal one, although in the low frequency range the values are lower than the ideal PMTF prediction. This effect may be due to an artifact known as low frequency drop [44]. It is due to slow variations in the tails of the ESF, which arise from the scattered radiation, because of the finite edge width, and from image inhomogeneity due to slight differences among the 18 detection units.
This artifact vanishes considering a shorter ESF. In Fig. 5.6 the ex- perimental PMTF (open circles), measured from an ESF 2mm long across
the edge, is plotted against the function sinc(πau) as a best data fit (solid line). From the fit we have obtained a value for a = 170± 5µm, in better agreement with the physical pixel pitch.
Figure 5.6: Presampled MTF measured from an ESF 2 mm long across the edge (open circles). Best data fit with the function sinc(πau), where a is the pixel aperture and u is the spatial frequency (solid line).
The experimental PMTF of the IMI prototype has been also measured with two different beams quality, different edge angles and different tube loadings. In all the cases we have obtained values that are compatible within the experimental errors, as expected. In the Fig. 5.7 we report the presampling MTF calculated with a different beam spectrum, obtained by using a 2mm thick Al filter instead of the 4mm thick one.
The experimental presampled MTF demonstrates that the ultimate limit to the spatial resolution of the IMI prototype is due to the sampling frequency of the read-out electronics (i.e., to the pixel size of the Medipix1 chip) and not to the capabilities of the GaAs crystals. Moreover by this measurement we can deduce that the pixels of the detector work as inde- pendent elements, and that the charge sharing or the fluorescence escape do not affect the resolution of the system, as predicted by the Monte Carlo
5.2. Presampling MTF of the IMI prototype 105
Figure 5.7: Presampled MTF measured by using two different spectra, obtained with a 2mm thick Al filter and a 4mm thick one. No differences are appreciable between the two MTFs. The theoretical MTF of the ideal SPC system is also shown (solid line).
simulations, reported in the Chapter 4.
From the MTF measurements, the GaAs detectors appear to be under- sampled, since the presampled MTF has very large values for frequencies above the Nyquist one. This means that in the final sampled image the high frequency content will be aliased, and this will introduce artifacts in the image. This effect can be perceived in the Fig. 5.8, which shows the image of two bar patterns. These phantoms have a set of lines of decreasing width. The aliasing results visible in the zone with the tiniest particulars.
The pixel size of the system presented in this work does not comply with the current mammographic standards (a pixel size smaller of 100µm is the current standard in digital mammography), but at the time when the demonstrator was designed the only available read-out electronics working in SPC mode was the Medipix1 chip, which has pixel size of 170µm. Al- though this pixel is bigger than in commercial systems, the measured MTF is comparable with the values obtained for several clinical mammographic systems (like GE Senographe and CR systems) that have smaller pixel
Figure 5.8: Image of two bar patterns acquired to verify the effect of the aliasing in the image, due to the undersampling of the system. The physical dimensions of the radiograph are (14 × 12)cm2. In some parts of the image it is possible to observe reconstruction errors (vertical misalignments of one or two pixels) due to the accuracy of the stepper motor, which is of the order of the pixel size.
sizes [45] [46] [47]. This is due to the high spatial resolution capabilities of GaAs detectors and to the SPC technology, where the threshold can be set high enough to detect only the signal on the pixel mostly interested by the event, disregarding the charge spread over the adjacent pixels, as discussed in Chapter 4.
Since the GaAs detectors are undersampled, hence the spatial resolu- tion of the imaging system could be improved if a read-out electronics with smaller pixel size is used. Today, Medipix2 and Medipix3 chips have been made available. They are based on the same technology as Medipix1, but have 55µm pixels [11] [37].
A small prototype of a GaAs pixel detector (with an active area of 1.9cm2), coupled with the Medipix2 readout, has been developed as re- ported in Ref. [48]. This device shows a spatial resolution better than Medipix1 based detectors but a complete characterization by means of the transfer functions has not yet been performed.
5.3. NPS of the IMI prototype 107
5.3 NPS of the IMI prototype
The digital NPS has been measured by using the Equation 2.25, derived in the section 2.4. To make the N P S independent of the system gain, the normalized noise power spectrum (NNPS) is used instead of the NPS. The NNPS is defined as the NPS divided by the mean signal squared. From the Eq. 2.25 the NNPS can be calculated at discrete values of the frequency as
N N P S(uk, vh) = a2 N2
1 I¯2
1 M
!M m=1
"
"
"
"F F Tk,h
#
Im(i, j)− ¯I$""""
2
(5.1) where I(i, j) indicates the image value at pixel (i, j), ¯I is the average signal value, a is the pixel size in mm, N is the ROI size in pixels, and M is the number of ROIs. The F F T represents the fast Fourier transform algorithm (MATLAB has been used to calculate the F F T ).
The NNPS was determined over 45 ROIs of 256× 256 pixels each, obtained from a set of independent flat-field images of about (45×45)mm2. All the images have been acquired with the same radiation quality and air kerma used for the MTF measurement (Mo/Mo 28kV p and 4 mm thick Al filter).
A one-dimensional cut through the two-dimensional NNPS has been obtained by averaging the central ±7 lines excluding the axis around the horizontal and vertical axes (for a total of 28 rows and columns).
Figure 5.9 shows the one-dimensional NNPS of the system as a func- tion of the spatial frequency. The uncertainty on the NNPS data has been estimated by considering the sampling standard deviation (as a function of the frequency), calculated in the ensamble of the M ROIs. The stan- dard deviation of the average has been calculated for each frequency. The maximum relative error is 4%.
The NNPS curve shows an almost constant trend over all the spatial frequency range investigated as predicted by the ideal SPC model (please refer to section 2.6). Since the NNPS and the autocovariance function are Fourier pairs, a flat NNPS implies a narrowly peaked autocovariance
Figure 5.9: Experimental NNPS as a function of the spatial frequency (open circles).
function. This indicates the absence of structured noise and of correlation among the pixels. Also this result is in accord with the Monte Carlo simulations of the radiation and charge transport, which have predicted the uncorrelation of the detector pixels.
We have finally compared the experimental NNPS with the theoretical model, expected for an ideal system with uncorrelated pixels and affected only by the quantum noise. A set of flat-field images have been simulated with a random number generator (supplied by MATLAB). The simulated images have pixel values independent and Poisson distributed, with average I, equal to the average of the experimental flat-field images.¯
The simulated images have been equalized and the N N P Ssim has been calculated as in the experimental case, using the same values of the ROI size (N = 256) and of the number of ROIs (M = 45). Fig. 5.10 shows the experimental NNPS (blue open circles) with the simulated one (red open circles).
5.4. DQE of the IMI prototype 109
0 0.5 1 1.5 2 2.5 3
2 4 6 8 10 12 14x 10−6
Spatial Frequency [cycles/mm]
NNPS
Experimental NNPS Simulated NNPS (Poisson distributed noise)
Figure 5.10: Experimental NNPS (blue circles), and simulated NNPS (red cir- cles).
5.4 DQE of the IMI prototype
The DQE of the IMI prototype has been evaluated by using the measured presampled MTF and the one-dimensional NNPS. In particular the DQE at the discrete frequency values uk has been calculated as
DQE(uk) = M T F (uk)
Q· NNP S(uk) (5.2)
where the presampled MTF has been evaluated at the frequency val- ues of the NNPS, and Q is the photon fluence on the detector surface (photons/mm2).
The Q value for the beam used in our experiment (Mo/Mo 28 kVp and 4mm of Al) has been calculated using the standard spectra reported in the IPEM REP78 [36], and corrected for the air kerma measurement as appropriate. The simulated photon fluence per air kerma unit at the detector surface is equal to 6304mmphotons2µGy.
Figure 5.11 shows the experimental DQE as a function of the spatial frequency (open circles). The experimental data have been fitted with the sinc(πau)2 function (solid line), which represents the expected shape
from the ideal SPC model. Since the relative uncertainties on MTF and on NNPS are less than 3.5% and 4%, respectively, for frequencies below the Nyquist one, and the uncertainty on the air kerma is 2%, the DQE uncertainty calculated from error propagation is less than 10%, as required by the IEC standard.
Figure 5.11: Experimental DQE as a function of the spatial frequency (open circles). The best fit of the DQE with a |sinc|2| function is repre- sented with a solid line.
The measured DQE decreases with frequency in agreement with the MTF. The DQE value at zero frequency, DQE(0), extrapolated from the fit is 46%.
The DQE was also investigated as a function of air kerma at the de- tector surface. Figure 5.12 presents results of the measured DQE(0) as a function of the air kerma in the range of (8–80)µGy. The beam quality and the flat-field correction are the same used in the previous measurements (the flat fields are acquired with twice the air kerma used for the image acquisition).
The DQE(0) of the system results to be independent of the dose inside the investigated range, which makes the system apt to applications where the dose must be kept as low as possible (such as in mammographic screen-
5.4. DQE of the IMI prototype 111
Figure 5.12: Experimental value of the DQE(0) measured as a function of the incident air kerma.
ing). In fact, in several integrating systems (like CsI and a-Se based flat panels) the DQE significantly drops for air kerma values below 50µGy [47].
This is probably due to the presence of additive noise from the readout electronics (see section 2.5). Being an additive noise source, the electronics noise contributes more at low signal levels.
The uniform behavior of the DQE(0) of our system with the dose, confirms the low electronics noise contribution and the good noise discrim- ination capabilities of the SPC readout.
5.4.1 Measurement of the detection efficiency
As pointed out in the section 2.6, for the ideal SPC detector the DQE at the zero frequency is expected to be equal to the quantum detection efficiency G (defined for a SPC system as the probability to detect an incoming radiation quanta). To compare the experimental measure with the expected one, the system detection efficiency has been experimentlly measured.
In our system the pixel counts represent the actual number of detected
photons since spurious events (due, for example, to noise or to charge sharing) are discriminated applying a threshold (as demonstrated by the MC simulations and by the MTF measurement). We can thus estimate the detection efficiency with the large area gain factor (defined in section 2.5 as the ratio between the average pixel count and the average number of photons incident on the pixel ¯d/ ¯qi).
The measured detection efficiency is 74%± 2%. This value is much higher than the experimental DQE(0). In the following we discuss as this gap is due to the quantum noise introduced in the image by the flat-field equalization.
5.4.2 Effect on the DQE of the flat-field equalization.
When the flat-field equalization is used to correct the row image, the noise of the equalized image is due to the quantum noise of both the raw image and the flat field. High statistics is advisable when acquiring the flat fields. The usual procedure of summing up several flat fields cannot be accomplished with the present two-row demonstrator since the number of steps and shots necessary to acquire the full 18×24cm2 image would cause an excessive tube heating. In this demonstrator the flat-field statistics is comparable with the raw image one and would thus affect the final image variance.
In order to better understand this effect on the DQE, we have character- ized a single detection unit working in stationary mode (without perform- ing the scanning). In this setup, the problem of tube heating is overcome so that flat fields with adequate statistics can be acquired. An image of 64× 64 pixels taken with single static detection unit has been equalized by means of different flat-field corrections with increasing statistics. Per- forming multiple acquisitions and summing them up, we have obtained a flat field with higher statistics.
Figure 5.13 shows the DQE(0) as a function of the total air kerma on the flat-field acquisitions, each one performed with the same radiation
5.4. DQE of the IMI prototype 113
quality and exposure as in the DQE measurement. We note that the DQE(0) value increases with the flat-field statistics.
Figure 5.13: Experimental value of the DQE(0) as a function of the total air kerma on the flat-field acquisition used to correct the raw image.
When the image is corrected using seven flat fields summed up, the DQE(0) is 74.6%± 1.5%. This is compatible with the system detection efficiency as expected from the ideal SPC system model.
Figure 5.14 reports the experimental DQE of the single stationary de- tection unit as a function of the spatial frequency, when the image is equal- ized with seven flat fields summed up. The characterization of the single stationary detection unit allows separating the factors due to scanning from the inherent performance of the detector. Comparing the DQE in Fig. 5.14 with the one of the whole scanning system in Fig. 5.11, we can see that both curves show the same behavior with the frequency; hence the scanning procedure scales the DQE down because of the lower statistics used in the flat-field equalization.
The problem of the low statistics in the full-image flat field acquisitions is related to tube overheating during the long scanning sequences. It can be overcome increasing the number of detector units (e.g., with 12 rows
Figure 5.14: Experimental DQE of a single detector unit, working in stationary mode, as a function of the spatial frequency (open circles) plotted with the fitting function (line).
of 9 detectors each, a whole image acquisition would require only 4 steps for a total of few seconds). The DQE of the system can be then improved until it reaches the measured detection efficiency, in accordance with the ideal SPC model.
A DQE(0) of 74% is higher than in most clinical mammographic sys- tems [45] [46] [47]. Even though data from literature are not exhaustive, the cited references report comparisons between several clinical systems made by means of the transfer function analysis. These works show that for indirect detection systems (as CsI scintillators coupled to flat panels or to CCDs) DQE(0) is equal to or lower than 50%. The DQE(0) of our system is comparable with flat panels based on a-Se, which show the best DQE (of about 65% at 0.2mm−1) and, most important, it is independent of dose as discussed previously. On the other hand, the DQE drops more rapidly with frequency because of the larger pixel size.
5.5. Measurement of the Thickness-Threshold curves 115
5.5 Measurement of the Thickness-Threshold curves
As described in section 2.7, the Contrast-Threshold analysis allows the determination of the minimum thickness and minimum area of the objects that are detectable by the imaging system at a given dose. This method is based on the visual determination by a set of experienced observers of the minimum detectable thickness of a circular detail as a function of its diameter.
The Thickness-Threshold Detail measurements have been performed following the EUREF Protocol [17]. Several images of a contrast detail phantom (CDMAM 3.4, [24]) have been acquired and presented to three independent observers for the scoring.
The phantom consists of an aluminium base, 0.05mm thick, with gold discs (99.99% pure gold) of different thicknesses (from 0.05 to 1.60µm) and diameters (from 0.10 to 2mm). The aluminum base is attached to a 5mm thick PMMA cover. To have an assembly equivalent to a 60mm thick breast, the CDMAM has been sandwiched between 2 PMMA layers 2cm thick. In Fig. 5.15 is showed a photo of the CDMAM phantom, in which are visible the circular gold details of different thickness and diameter.
The tube settings were Mo/Mo anode/filter combination, 28 kVp and 50 mAs to obtain an AGD, relative to a standard 60mm thick breast, of 2.21 mGy. This dose value complies with the EUREF standard, that suggests an achievable limit for the dose equal to 2.6 mGy. A tube exposure level of 36 mAs, corresponding to an AGD of 1.58 mGy, has been also used to study how the image quality varies with the dose.
Six images of the mammographic phantom have been acquired and presented to three experienced observers. In Fig. 5.16 a particular of the phantom, acquired with the IMI prototype is shown. The CDMAM presents a series of squares, each containing a detail of given thickness and area in the center of each square, and another identical dot at one
Figure 5.15: A Photo of the CDMAM phantom. The phantom has 16 column of details of the same size and increasing thickness, and 16 rows of details with same thickness and increasing size.
of the four corners of the square. The observers task is to state which corner a particular detail is in, for each of the sizes and contrasts. To perform the scoring of the observers, the indicated positions of the details are compared to the true disk positions in the phantom. If the observer identifies a particular dot as visible, but gets the wrong corner, the answer is considered incorrect.
For each of the six scored images, the threshold gold thickness (the just visible gold thickness) has been determined for 12 different detail diameters (from 2mm to 0.16mm). The evaluation of the scored images is performed as described in Ref. [49]. The results of all scored images have been analyzed and the average threshold gold thickness has been calculated.
The Thickness-Threshold curves have finally been obtained plotting the average minimum thickness detected by the three observers versus the detail diameter. This procedure has been repeated for the two exposure levels.
The Thickness-Threshold curves for the two exposure levels are plot-
5.5. Measurement of the Thickness-Threshold curves 117
Figure 5.16: A part of the CDMAM phantom (five columns and four rows) ac- quired with the IMI prototype. Each column contains gold details with the same diameter but different thicknesses (from 0.36µm to 0.16µm). The size of the details reported in this particular is in the range 2mm - 0.80mm
ted in Fig. 5.17. We have compared the experimental results with the limits suggested by the EUREF protocol. The EUREF Protocol requires to compare the minimum detectable thickness for five detail diameters (from 0.1 to 2 mm) with two reference limits (Achievable and Acceptable levels). The first one indicates the optimal value achievable for a digital mammographic system; the second one indicates the level that 90% of the screen-film systems would comply.
In Table 5.5 the values obtained for the details required by the protocols are reported together with the EUREF limits. The details smaller than 0.16mm have not been detected since the detail size is smaller than the detector pixel, and the correct visualization of these details is compromised by the presence into the images of bad pixels.
The Thickness-Threshold curves for the IMI system, using both dose levels, lie below the EUREF acceptable level for the details bigger than the pixel size. This indicates a level of image quality superior to the EU- REF acceptable level. Using the 2.21mGy dose level the image quality is
10−1 100 10−2
10−1 100
Detail diameter [mm]
Thckness Threshold [µ m]
IMI Prtptype (AGD = 2.21 mGy) IMI Prototype (AGD= 1.58 mGy) EUREF acceptable level EUREF achievable level
Figure 5.17: thickness-Threshold curves measured at two dose levels. The EU- REF achievable level (green triangles) and acceptable level (purple triangles) have been also plotted.
Table 5.2: Thickness-Threshold values of the IMI prototype calculated at five detail diameters, compared with the corresponding EUREF limit values.
Disk diameter (mm)
EUREF accept- able level (µm)
EUREF achiev- able level (µm)
IMI Thickness- Threshold (µm) AGD = 1.58mGy
IMI Thickness- Threshold (µm) AGD = 2.21mGy
2mm 0.069 0.038 0.0375 0.0362
1mm 0.091 0.056 0.0588 0.0475
0.50mm 0.15 0.103 0.1537 0.1312
0.25mm 0.352 0.244 0.3833 0.3417
0.1mm 1.68 1.10 - -
5.5. Measurement of the Thickness-Threshold curves 119
even better the EUREF achievable level for some of the bigger diameter details (from 2mm to 1mm). For example, considering details of 1.6mm in diameter, the minimum detectable thickness is equal to 35nm of gold, corresponding to an intrinsic contrast (for the used beam quality) equal to 0.49%.
Conclusions
In this thesis we have reported on the physical characterization and on the imaging performances of a mammographic demonstrator, based on GaAs pixel detectors and single photon counting electronics, developed in the framework of the “Imaging Mammografico Integrato” (IMI) project. This system (named IMI prototype) is composed of 18 detector units, each one formed by a GaAs pixellated detector coupled to the Medipix1 chip.
The system has been characterized and calibrated by setting the bias voltage of the detectors, the discrimination threshold of the chips and other configuration parameters aimed to optimize the detector response.
Moreover a set of preliminary measurements like the linearity with the dose, the stability of the response and the maximum counting-rate have been performed. Finally the image-acquisition procedure has been set-up and adjusted to acquire high quality images.
In order to assess the imaging capabilities of the IMI system, two methodologies have been followed. The first one is based on the exper- imental measurement (following the IEC 62220–1-2 protocol) of the trans- fer functions of the system: the presampling MTF, the NNPS and the DQE. The second approach (EUREF protocol) is based on the Contrast- Threshold (CT) analysis, that, unlike by the DQE, takes also into account the visual processes of the human observer.
For the CT measurements several images of a contrast detail phan- tom (CDMAM) have been acquired and presented to three independent observers for the scoring. The CDMAM phantom consists of a matrix of square cells with dots of different size and thickness. The observer’s task is the detection of the dots. The CDMAM images, acquired by the IMI system, has been evaluated and the minimum detectable thickness as a function of the detail size has been determined. The system complies with the EUREF acceptance limits in detecting detail of diameter ranging from 2 mm to 0.25 mm, but shows the best performance in the detection of de- tails of diameter larger than 0.5 mm. For instance, the threshold contrast for the detail of 1 mm in diameter is 0.67%.
As far as the transfer functions is concerned, the presampling MTF, the NPS and the DQE of the IMI prototype has been experimentally mea- sured following the IEC 62220–1-2 protocol whereas applicable. All the experimental results about the transfer functions agree with the behav- ior expected from a direct detection single photon counting system. The presampling MTF curve, measured with the method of the tilted edge, is comparable with the ideal case (the function sinc(παu) with α = 170µm).
This measurement has demonstrated that the ultimate limit to the spatial resolution is due to the pixel aperture. The signal sharing, due to charge sharing among adjacent pixels and to the fluorescent photons escaping, does not compromise the spatial resolution performances of our imaging system.
The effects of the signal sharing on the system image performances have been further investigated by means of a Monte Carlo simulation of the radiation transport. This simulation has showed that, although the fluorescence escaping and the charge diffusion degrade the spectrum of the energy deposited into a detector pixel, the SPC electronics efficiently discriminates the spurious events, so that the detector pixels result totally uncorrelated. This effect is in accord with the MTF measurement.
Furthermore the NNPS of the IMI prototype has been measured and it
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results almost uniform in the frequency range. This indicates the absence of structured noise and of correlation among pixels (a further proof that signal sharing does not affect image quality).
The experimental DQE(0) is 46%, and it is almost independent of the air kerma. We have shown how this value could be further improved, up to the ideal value, acting on the statistics in the flat-field equalization procedure. We have in fact shown, on a reduced-size image, how increasing the flat-field statistics brings the DQE(0) up to 74.6%± 1.5%, compatible with the measured detection efficiency and in accordance with the model of an ideal quantum limited SPC system. This value is considerably high in comparison with the conventional clinical mammographic systems.
As far as the detection efficiency is concerned, the measured value of 74%±2% is mainly due by the discrimination threshold, which discards low energy events (e.g., charge sharing or fluorescence escape). This effect has also been predicted by the Monte Carlo simulation of radiation transport.
Both the Contrast-Threshold and the Transfer-Functions analyses indi- cate that the ultimate limits of the IMI prototype are the low statistics on the flat-field acquisition and the pixel size, which does not complies with the mammographic requirements in the detection of details of the order of the pixel size. The problem of low statistics in full-image flat-field acqui- sitions, due to tube overheating during the long scanning sequences, can be overcome by increasing the number of detector units and implementing an efficient cooling system that keeps stable the detector response in the time necessary for the high statistics flat-field acquisition. As regards the pixel size, newly read-out electronics with smaller pixel dimensions can be used to optimize the spatial resolution of our system. The Medipix2 and Medipix3 chips are examples of such SPC read-out devices. They have a pixel pitch of 55µm and multiple energy thresholds. The Medipix3 chip, in particular, is being developed with a new front-end architecture aimed at eliminating the spectral distortion produced by charge sharing and fluorescence photons in high-Z semiconductor detectors.
Bibliography
[1] “Global Cancer Facts and Figures 2007” (unpublished). Technical report, American Cancer Society (www.cancer.org).
[2] The Swedish Organised Service, Screening Evaluation Group. “Re- duction in Breast Cancer Mortality from Organized Service Screening with Mammography: 1. Further Confirmation with Extended Data”.
Cancer Epidemiol Biomarkers Prev, 15:45–51, 2006.
[3] Martin J. Yaffe. “Digital Mammography”, in handbook of medical imaging, physics and psychophysics vol. 1. edited by J. Beutel, H. L.
Kundel, and R. L. Van Metter (SPIE, Washington), pages 329–372, 2000.
[4] V. N. Cooper, T. Oshiro, C. H. Cagnon, T. M. McLeod-Stockmann, and N. V. Bezrukiy. “Evaluation of detector dynamic range in the x-ray exposure domain in mammography: A comparison between film- screen and flat panel detector system”. Med. Phys., 30:2614–2621, 2003.
[5] E. D. Pisano, C. Gatsonis, E. Hendrick, M. Yaffe, J. K. Baum, S. Acharyya, E. F. Conant, L. L. Fajardo, L. Bassett, C. D’Orsi, R. Jong, and M. Rebner. “Diagnostic performance of digital versus film mammography for breast-cancer screening”. N. Engl. J. Med., 353:1773–1783, 2005.
[6] M. G. Bisogni, M. Campbell, M. Conti, P. Delogu, M. E. Fantacci, E. H. M. Heijne, P. Maestro, G. Magistrati, V. M. Marzulli, G. Med-
deler, B. Mikulec, E. Pernigotti, V. Rosso, C. Schwarz, W. Snoeys, S. Stumbo, and J. Watt. “Performance of a 4096-pixel photon count- ing chip”. Proc. SPIE, 3445:296–304, 1998.
[7] M. Bech, O. Bunk, C. David, P. Kraft, C. Bronnimann, E.F. Eiken- berry, and F. Pfeiffer. “X-ray imaging with the PILATUS 100k detec- tor ”. Applied Radiation and Isotopes, 66(4):474 – 478, 2008.
[8] P. Delpierre et al. “XPAD: A photons counting pixel detector for ma- terial sciences and small-animals imaging”. Nucl. Instrum. Methods Phys. Res., A 572:250–253, 2007.
[9] G. Blanchot, M. Chmeissani, A. Diaz, F. Diaz, J. Fern`andez, E. Gar- cia, J. Garcia, F. Kainberger, M. Lozano, M. Maiorino, R. Mart`ınez, J.P. Montagne, I. Moreno, G. Pellegrini, C. Puigdengoles, M. Sent`ıs, L. Teres, M. Tortajada, and M. Ull`an. “Dear-Mama: A photon count- ing x-ray imaging project for medical applications”. Nucl. Instrum.
Methods Phys. Res., A 569:136–139, 2006.
[10] M. Aslund, B. Cederstrom, and M. Danielsson. “Physical character- ization of a scanning photon counting digital mammography system based on Si-strip detectors”. Med. Phys., 34:1918–1925, 2007.
[11] X. Llopart, M. Campbell, R. Dinapoli, D. San Segundo, and E. Pernig- otti. “Medipix2: A 64-k pixel readout chip with 55 micron square el- ements working in single photon counting mode”. IEEE Trans. Nucl.
Sci., 49:2279–2283, 2002.
[12] A. Annovazzi et at. “A GaAs pixel detectors based digital mammo- graphic system: Performances and imaging tests results”. Nucl. In- strum. Methods Phys. Res., A 576:154–159, 2007.
[13] A. Stefanini, S. R. Amendolia, A. Annovazzi, P. Baldelli, A. Bigongiari, M. G. Bisogni, F. Catarsi, A. Cetronio, M. Chianella, M. N. Cinti, P. Delogu, M. E. Fantacci, D. Galimberti, M. Gam- baccini, C. Gilardoni, G. Iurlaro, C. Lanzieri, M. Meoni, M. Novelli, R. Pani, G. Passuello, R. Pellegrini, M. Pieracci, M. Quattrocchi, V. Rosso, and L. Venturelli. “An example of technological transfer to industry: The IMI project”. Nucl. Instrum. Methods Phys. Res., A 518:376–379, 2004.
Bibliography 127
[14] G. Bertuccio. “Prospect for energy resolving x-ray imaging with com- pound semiconductor pixel detectors”. Nucl. Instrum. Methods Phys.
Res. A, 546:232–241, 2005.
[15] C. M. Buttar. “ GaAs detectors: A review ”. Nucl. Instrum. Methods Phys. Res., A 395:1–8, 1997.
[16] Rose A. “Sensivity Performance of the Human Eye on an Absolute Scale”. J Opt. Soc. Am., 38:196–208, 1948.
[17] “ European Guidelines for Quality Assurance in Breast Cancer Screen- ing and Diagnosis”, 4th ed. edited by N. Perry, M. Broeders, C. de Wolf, S. T¨ornberg, R. Holland, L. von Karsa, and E. Puthaar Office for Official Publications of the European Communities, Luxembourg, 2006.
[18] Ian. A. Cunningham. “Applied linear-system theory”, in handbook of medical imaging, physics and psychophysics vol. 1. edited by J. Beutel, H. L. Kundel, and R. L. Van Metter SPIE, Bellingham, pages 82–156, 2000.
[19] J.G. Proakis and D.G. Manolakis. “ Digital Signal Processing (4th edition)”. Prentice-Hall, 1996.
[20] James T. Dobbins III. “Image quality metrics for digital systems”, in handbook of medical imaging, physics and psychophysics vol. 1. edited by J. Beutel, H. L. Kundel, and R. L. Van Metter (SPIE, Bellingham), pages 161–222, 2000.
[21] Shaw R. Dainty JC. “ Image Science”. Academic Press, New York, 1974.
[22] Shaw R. “The equivalent Quantum Efficiency of the Photographic Process”. J Photogr. Sc., 11:199–204, 1963.
[23] Rose A. “A Unified Approach to the Performance of Photographic Film, Television Pick-Up Tubes, and the Human Eye”. J Soc. Motion Pict. Telev. Eng., 47:273–294, 1946.
[24] “CDMAM Phantom”, nuclear associates model 18-227, cardinal health, radiation management services, 6045 cochran road, cleveland, ohio 44139-3303, usa.
[25] M. Novelli, S. R. Amendolia, M. G. Bisogni, M. Boscardin, G. F. Dalla Betta, P. Delogu, M. E. Fantacci, M. Quattrocchi, V. Rosso, A. Ste- fanini, L. Venturelli, and S. Zucca. “Semiconductor pixel detectors for digital mammography”. Nucl. Instrum. Methods Phys. Res., A 509:283–289, 2003.
[26] M. Novelli. “ Characterization of a single photon counting detector prototype for digital mammography”. PhD thesis, Applied Physics, Universit`a di Pisa, Ciclo II (unpublished), 2004.
[27] M. G. Bisogni, D. Bulajica, A. Cetronio, P. Delogu, M. E. Fantacci, C. Lanzieri, M. Novelli, M. Quattrocchi, V. Rosso, A. Stefanini, and L. Venturelli. “Interconnection techniques of GaAs pixel detector on silicon ASIC electronics”. Proceedings of the IEEE Nuclear Science Symposium Conference Records, Vol 7:4524–4527, 2004.
[28] SYLVIA mammographic unit by Gilardoni SpA, Apparecchiature ra- diologiche nucleari, Mandello sul Lario (Lc), Italy, www.gilardoni.it.
[29] S.R. Amendolia, M. G. Bisogni, P. Delogu, M. E. Fantacci, M. Nov- elli, P. Oliva, M. Quattrocchi, V. Rosso, A. Stefanini, and S. Zucca.
“Experimental study of Compton scattering reduction in digital mam- mographic imaging”. IEEE Trans. Nucl. Sci., 49:2361–2365, 2002.
[30] C. Carpentieri, C. Schwarza, J. Ludwiga, A. Ashfaqa, and M. Fieder- leb. “Absolute dose calibration of an X-ray system and dead time in- vestigations of photon-counting techniques”. Nucl. Instrum. Methods Phys. Res., A 487:71–77, 2002.
[31] “ X-ray data booklet”. Center for X-ray Optics and Ad- vanced Light Source, Lawrence Berkeley National Laboratory (http://xdb.lbl.gov/), 2001.
[32] “MCNP - A General Monte Carlo N-Particle Transport Code - Version 5 ”, Los Alamos National Laboratories (http://mcnp- green.lanl.gov/).
[33] A. Cola et al. “Microscopic modelling of semi-insulating GaAs detec- tors”. Nucl. Instr. and Meth., A 380:66–69, 1996.
[34] A.Cola et al. “An extended drift-diffusion model of semi-insulating n-GaAs Schottky barrier diodes”. Semicond. Sci. Technol., 12:1358–
1364, 1997.
Bibliography 129
[35] A. Cola et al. “Field-assisted capture of electrons in semi-insulating GaAs”. Journal of Applied Physics, 81:997–998, 1997.
[36] K. Cranley, B. J. Gilmore, G. W. A. Fogarty, and L. Desponds. “Re- port 78: Catalogue of Diagnostic X-ray Spectra and Other Data”.
IPEM, New York, 1997.
[37] R. Ballabriga, M. Campbell, E. H. M. Heijne, X. Llopart, and L. Tlus- tos. “The Medipix3 prototype: A pixel readout chip working in sin- gle photon counting mode with improved spectrometric performance”.
IEEE Trans. Nucl. Sci., 54:1824–1829, 2007.
[38] “ IEC 622220-1-2. Medical electrical equipment: Characteristics of digital x-ray imaging devices. Part 1-2: Determination of the detective quantum efficiency-Detectors used in mammography”. International Electrotechnical Commission, Geneva, Switzerland, 2007.
[39] H. E. Johns and J. R. Cunningham. “ The Physics of Radiology”, fourth edition. Charles C Thomas Publisher, 1983.
[40] Hiroshi Fujita, Kunio Doi, and Maryellen Lissak Giger. “Investigation of basic imaging properties in digital radiography. MTFs of TV digital imaging systems”. Medical Physics, 12(6):713–720, 1985.
[41] Hiroshi Fujita et al. “A Simple Method for Determining the Modu- lation Transfer Function in Digital Radiography”. IEEE Trans. on Med. Imag, 11(1):34–39, 1992.
[42] Ehsan Samei, Michael J. Flynn, and David A. Reimann. “A method for measuring the presampled MTF of digital radiographic systems using an edge test device”. Med. Phys., 25(1):102–113, 1998.
[43] E. Buhr, S. Gunther-Kohfahl, and U. Neitzel. “Accuracy of a simple method for deriving the presampled modulation transfer function of a digital radiographic system from an edge image”. Medical Physics, 30(9):2323–2331, 2003.
[44] U. Neitzel et al. “Determination of the detective quantum efficiency of a digital x-ray detector: comparison of three evaluations using a common image data set”. Med. Phys., 31:2205–2211, 2004.
[45] P. Monnin, D. Gutitierrez, S. Bulling, D. Guntern, and F. R. Verdun.
“A comparison of the performance of digital mammography system”.
Med. Phys., 34:906–914, 2007.
[46] B. Lazzari, G. Belli, C. Gori, and M. Rosselli Del Turco. “Physi- cal characteristics of five clinical systems for digital mammography”.
Med. Phys., 34:2730–2743, 2007.
[47] N. W. Marshall. “Early experience in the use of quantitative im- age quality measurements for the quality assurance of full field digital mammography x-ray systems”. Phys. Med. Biol., 52:5545–5568, 2007.
[48] A. Zwerger, A. Fauler, M. Fiederle, and K. Jakobs. “Medipix 2: Pro- cessing and measurements of GaAs pixel detectors”. Nucl. Instrum.
Methods Phys. Res., A 576:23–26, 2007.
[49] “Readout of Human Observers” (http://www.euref.org), unpublished.