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A detector system for a high-energy phase-contrast human computed tomography experimental device
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
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Nuclear Inst. and Methods in Physics Research, A journal homepage: www.elsevier.com/locate/nima
Technical notes
A detector system for a high-energy phase-contrast human computed tomography experimental device Rongqi Sun a,b , Lian Chen b ,∗, Wenbin Wei c , Ge Jin a,b a
Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, 230026, China State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, 230026, China c National Synchrotron Radiation Laboratory, Hefei 230026, China b
ARTICLE
INFO
Keywords: Phase contrast Scintillator detector Computed tomography Read-out electronics
ABSTRACT A detector system is designed for a high-energy phase-contrast computed tomography (CT) experimental device with an X-ray tube voltage of 80 kV. The spot size of the X-ray tube is 0.4 mm*0.7 mm. The Gd2O2S scintillator detector is adopted in our design, which is characterized by a high detection efficiency and high light yield. This system contains 2304 channels and the corresponding high-density read-out electronics. Considering the influence of the large spot size of the X-ray tube source, a detector pixel size of 0.75 mm*1 mm is adopted to obtain sufficient spatial resolution. Because the light intensity fluctuation caused by the phase shift is very weak, a 20bit ADC is used to achieve sufficient efficient resolution (ER). The electronics test shows that the SNR can reach 72. Compared to a commercial detector board, the detector system has better resolution for the sample used in the phase-contrast imaging experiment.
1. Introduction Conventional X-ray imaging is a valuable tool in clinical medicine diagnosis and materials science. However, for those objects composed of different kinds of tissues with similar absorption cross sections, the imaging contrast is relatively weak. Since the last century, the X-ray phase-contrast imaging method characterized by high sensitivity has drawn increasing attention. Theoretically, when X-rays with energies greater than 10 keV pass through an object composed of low-Z elements, their phase-contrast cross section is 1000 to 100 000 times that of the absorption cross section [1]. As a consequence, the X-ray phase shift information can provide significantly greater imaging contrast. Over the last several decades, many approaches have been investigated to extract X-ray phase shift information [2,3]. Nevertheless, these methods, demanding the high coherence and high luminance of X-ray sources, are mainly available with a synchrotron radiation source or micro-focus X-ray generator. Therefore, none of them can allow for a wide range of biomedical or industry applications, where the X-ray imaging facility generally features a reasonably compact structure and the use of conventional incoherent hospital X-ray tube sources. In 2006, a three-grating differential phase-contrast (TGDPC) method based on a Talbot–Lau interferometer was developed [4]. This experiment makes it possible to perform the phase contrast efficiently with a conventional, low-brilliance X-ray source. Nevertheless, this method requires a very small grating period size; hence, it is not suitable for high-energy imaging, which is needed in clinical diagnoses. In 2009, an improved
TGDPC method based on geometric projection was proposed to solve this problem [5]. This method uses the geometric projection instead of the Talbot effect to generate self-imaging fringes; thus, the grating period can be much larger than the grating period with the Talbot–Lau interferometer method. In fact, the image formation of the TGDPC method is dependent on detecting these slight refraction angles when an X-ray passes through the objects. The higher X-ray energy yields smaller refraction angles, which means that the requirement for detection sensitivity becomes more stringent. To date, these existing phase-contrast computed tomography (CT) apparatuses suffer from a low tube voltage, usually below 70 kV, and a small field of view (FOV). These limitations make these devices available only in the local diagnosis of small objects [6–10]. Recently, a high-energy phase-contrast human CT based on a geometric projection method with a tube voltage of 80 kV was developed by the National Synchrotron Radiation Laboratory (NSRL). Because the goal of this device is to promote phase-contrast technology for the clinical application to the human body, its designed FOV is up to 666 mm*24 mm, which is the largest among these existing phasecontrast CT devices. The selected geometric parameters are illustrated in Fig. 1. To verify the technical feasibility, one CT experimental device is built. In this technical note, we design a detector system for this experimental device. To extract the phase shift information, one so-called phase stepping (PS) method is adopted. Along the transverse beam direction, the G1 grating is moved in several equidistant steps. Images
∗ Corresponding author. E-mail address: [email protected] (L. Chen).
https://doi.org/10.1016/j.nima.2019.162681 Received 22 March 2019; Received in revised form 27 August 2019; Accepted 1 September 2019 Available online 5 September 2019 0168-9002/© 2019 Elsevier B.V. All rights reserved.
R. Sun, L. Chen, W. Wei et al.
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
the refraction angle at the sampling point on the shifting curve will decrease. This means that the light intensity fluctuation caused by the phase shift is weaker in our device, being approximately as low as 0.005%, according to the physical simulation. Consequently, the efficient resolution should be better than log2 (1∕0.005%) = 14.3 bit. In our design, six detector modules are adopted to obtain a sufficient measurement FOV. The total number of channels is 2304. Restricted by the high density of channels, discrete components cannot be used to design the front-end readout electronics. To meet the needs of readout electronics for high integration and high resolution, eighteen DDC1128 analog-to-digital converters (ADCs) are adopted. One DDC1128 contains 128 separate channels that carry out the currentto-voltage integration. Each channel includes a dual switched current integrator and an ADC with 20-bit resolution. The dual switched integrator is used to implement the ping-pong operation to reduce dead time. When the previous conversion is finished, the integrator capacitors are charged to a reference voltage. As the edge of the conversion signal rises, a selected integrator starts to collect the charge from the input channel over a given period of time, which causes a decrease in the amplifier output voltage. When the edge of the conversion signal begins to fall, the amplifier output is digitized by the on-chip ADC. Under external trigger signal control, all channels work simultaneously. The maximum integration time of these integrators is 1 ms. Owing to the ping-pong mechanism, the read-out of the digital signal will not add extra time overhead. Thus, DDC1128 can satisfy the requirement that the image frame rate be at least 800 fps.
Fig. 1. The selected geometric parameters of the prototype.
are captured per step. Each pixel records an intensity oscillation, which is called a shifting curve [11]. During the phase-contrast imaging, both the bright field reference and the object PS scan are captured. Then, through the analysis of the bright field reference and the object PS scan result, the phase imaging formation can be performed. Considering the performance of the detector, 𝛥𝛩min , the sensitivity of a TGDPC device, is defined as the minimum resolvable refraction angular deviation, which can be expressed as follows [12]: 𝛥𝛩min (x, y) = Vr (x, y) =
P2 2 ∗ √ 2𝜋dN Vr (x, y)*SNR(x, y) N br1 (x, y) br0 (x, y)
(1)
The structure of the detector board is illustrated in Fig. 2. These analog signals of 2304 pixels are transmitted to the detector board via six high-density connectors. Every three DDC1128 are responsible for one detector module, which are cascaded up to each other by a daisy chain. Then, 6-channel serial data from eighteen ADCs are transmitted into a Xilinx K7 FPGA and converted into parallel data. An air scan algorithm integrated in the linear correction module is used to correct the channel nonuniformity caused by the diversity of each pixel crystal and the gain difference of each electronics channel [15]. The background data are used as the offset value. Under X-ray exposure, bright field data are captured in the absence of a sample. Then, a specific value is chosen as the reference value of calibration. The calibration coefficient for each channel can be obtained through the reference divided by the bright field value with offset correction. The response of the detector is usually linear; thus, the channel nonuniformity can be removed in this way. This correction algorithm is shown in Eq. (5).
(2)
where P2 is the spacing of the G2 grating; d is the distance between the G1 grating and the G2 grating; and Vr and SNR are the shifting curve visibility [12] and the signal-to-noise ratio of each pixel, respectively. N is the number of acquisition frames; and br0 and br1 are the absolute values of the zero and first-order diffraction coefficients, respectively. According to Eq. (3), a good sensitivity requires a sufficiently high SNR. For the relatively high-energy X-ray, a good detection efficiency can yield a better SNR and shorter exposure time. In our design, the Gd2O2S (GOS) scintillator detector is adopted, which is characterized by high density and high light yield, compared to the NaI and CsI crystal [13]. In addition, the pixel size of the detector is another parameter that should be considered. Since the FOV of the design goal is so large, it is impracticable to adopt a detector with a pixel size of dozens of microns or a few microns, which most existing phase-contrast CT devices are using. The spatial resolution of the device is affected by the pixel size, the spot size of the X-ray tube and the geometry parameters. The semiempirical formula of the spatial resolution 𝛿 is expressed as follows [14]: √ 𝛿 = (𝑆 ∗ (𝑀 − 1))2 + 𝑃 2 ∕𝑀 (3) 𝑀 = (𝑓0 + 𝑙 + 𝑑 + 𝑓2 )∕(𝑓0 + 𝑙 + 𝑓 )
( ) 𝐼𝑐𝑜𝑟𝑟 = 𝐼𝑟𝑎𝑤 − 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡 ∗
𝐺 𝐼𝑙 − 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡
(5)
where 𝐼𝑐𝑜𝑟𝑟 is the channel response with correction; 𝐼𝑟𝑎𝑤 is the response value without correction; 𝐼𝑙 is the bright field value; 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡 is the background value; and G is the reference value of calibration.
(4)
After linear correction, all data are converted into serial data, are cached into the buffer and then await the serial peripheral interface (SPI) for transmission to the data collection board (DCB).
where P is the transverse size of the detector pixel. S is the transverse size of the spot. M is the geometric magnification. The selected spatial resolution goal without the pixel offset method is 0.4 mm. The spot size of the X-ray tube is 0.4 mm*0.7 mm. Putting these parameters in Fig. 1 into Eq. (3), the transverse size of the detector pixel should be lower than 0.8 mm. Therefore, the Hamamatsu S12058(X) detector module, constructed of a 16*24 array of individual GOS pixels, is used in the experimental device. Its pixel size is 0.75 mm*1.0 mm, which is the smallest among the available commercial medical GOS scintillator detectors.
Compared to the previous version of the detector board [16], we have made some improvements. The S12058(X) module includes the A and B sides. To reduce electronic noise, the analog ground of the A and B sides is split and connected to the digital ground by two zero-ohm resistors near the ADC. The analog signal traces are placed on the PCB inner layer and kept isolated from each other by ground traces. The previous version adopts the solution of one detector board with one detector module, the width of which is limited to 17.8 mm. Therefore the above improvements are very difficult to achieve in the previous version. The test shows that the SNR of the current version is approximately twice as good as that of the previous one.
2. Design of the readout electronics Increasing the X-ray energy will reduce the shifting curve visibility and the refraction angle. Therefore, the product of the slope and 2
R. Sun, L. Chen, W. Wei et al.
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
Fig. 2. The design of the detector board.
3.2. SNR test As mentioned above, a high SNR can yield better detection sensitivity of the TGDPC device. An SNR test is implemented on the detector board with a CTR2150 tube-based X-ray source from Dunlee. The voltage and current of the X-ray tube source are set to 80 kV and 180 mA, respectively. The exposure time and the integration time are set to 8 s and 1 ms, respectively. In the X-ray exposure, one pseudo trigger is generated to capture data many times. For every channel, the FWHM of the Gaussian distribution fitted to the histogram is used to evaluate the noise level 𝛿𝑏 , while the peak position of the Gaussian fitted curve is considered as the signal value S. In the absence of Xray exposure, the detector system captures multiple background data. The background data B of every channel is the peak position of the Gaussian fitted curve of the background histogram. Therefore, the SNR is obtained according to Eq. (7).
Fig. 3. The noise of all channels.
3. The test result of the detector system
SNR =
3.1. Noise test
(7)
The test results of the SNR for every channel are illustrated in Fig. 4. We can see that the SNR of these channels located on the edge of the detectors is evidently lower than that of other channels. This is mainly caused by the edge effect due to the imperfections introduced in the processing of the detector parts [17]. The edge effect can be corrected by the linear correction module. The average SNR of all 2304 channels is 72. There is no available commercial detector board with a pixel size of 0.75 mm*1 mm. Thus, one commercial detector board MDBB-ST16_S from the Detection Technology Company is used to perform a performance comparison experiment, the detector pixel size of which is 1 mm*1.08 mm. The average SNR of the commercial detector board is 85. Since the SNR is inversely proportional to the pixel size, the efficient SNR of our detector board is 72*(1.08*1/(0.75*1))0.5 = 86. Therefore, the performance of our detector board remains consistent with that of MDBB-ST16_S.
One noise test is performed on the detector board. In the absence of an actual detector signal, a pseudo trigger is fed into the system to sample the baseline data many times. The Gaussian fitting is performed on the background data histogram for every channel. The full-width-athalf-maximum (FWHM) of the Gaussian fitting curve is used to evaluate the level of noise, as illustrated in Fig. 3. The efficient resolution(ER) of the detector system can be calculated approximately as follows: ER = N − log2 (𝜎)
𝑆 −𝐵 𝛿𝑏
(6)
where N is the resolution bits of the ADC and 𝜎 is the average noise level. The average noise of all channels is 23 ADC bins. Among these channels, the worst is 32 ADC bins, the ER of which can reach 14.6 bit. Hence, the ER performance of the detector system can satisfy the design requirement. 3
R. Sun, L. Chen, W. Wei et al.
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
Fig. 4. The SNR result of all 2304 channels.
Fig. 8. The 2-dimensional imaging results.
3.3. Test of linearity For X-ray imaging, poor linearity of the detector will cause a measurement error, thus decreasing the resolution. To test the detector linearity, the current of the X-ray is stepped up to obtain different incoming charge quantities. We use the peak position of the ADC output histograms as the pixel response. In Fig. 5(a), the fitted linear curve for one typical pixel is illustrated. The linear correlation coefficient histogram of all pixels is shown in Fig. 5(b). It is evident that most of the detector pixels have good linearity performance.
Fig. 5. The results of the linear test.
3.4. The results of the imaging test The setup of the experimental device is shown in Fig. 6. The periods of G0, G1 and G2 are 32.5 um, 26 um and 130 um, respectively. All tests are performed with a CTR2150 tube-based X-ray source whose voltage and current are set to 80 kV and 180 mA. Its focal spot size is 0.4 mm*0.7 mm. The experimentally measured shifting curve visibility is 11%. To evaluate the system performance, a sample is employed, as illustrated in Fig. 7. The sample is composed of a sealable tube, some water and a polypropylene rod with a density of 0.92 g/cm3 . The polypropylene rod is put into the tube, surrounded by the water in the lower part. Fig. 8 shows the 2-dimensional (2D) imaging results of the sample, performed on the experimental device. The PS algorithm has the capacity to separate the absorption and the phase information from the detector data simultaneously. Since the FOV of the detector system is limited, half of the sample can be captured only in the 2D (2-dimension) scan. The curve on the right side of Fig. 8 is the contour of the twelfth row of both the absorption imaging result and the phase-contrast imaging result. In the contour of our detector board, the positions indicated by the two red arrows are the boundaries of the rod-to-water and the water-to-tube walls, which are two clearly visible sags. The refraction angular signals at these two positions are 172 and 213 nano-radians, respectively. Thus, the rod and water can be distinguished in the 2D phase-contrast imaging by our detector board. However, no visible tag appears in the contour of the commercial board. This is because the maximum refraction angle signal detected
Fig. 6. The setup of the experimental device.
Fig. 7. The test sample.
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R. Sun, L. Chen, W. Wei et al.
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
Fig. 9. 16-slice phase-contrast CT of the sample.
References
by a pixel is inversely proportional to the square root of the pixel horizontal size [12]. Therefore, the refraction angle detected by the large pixels of the commercial board is small enough to be overwhelmed by ambient noise, such as a mechanical error or temperature fluctuation. Meanwhile, it is evident that absorption imaging cannot distinguish the water from the polypropylene, according to Fig. 8. A phase-contrast CT scan also was also performed on our detector board with the sample. Fig. 9(a) shows the 16-slice phase-contrast CT imaging result. The upper part of the rod, from the first layer to the eighth layer, is surrounded by air. The lower part is immersed in water. In Fig. 9(b), the X-axial contour of the fourteenth layer, looks like a saddle. We can see that there is a clear dividing line between the water and the polypropylene rod. Therefore, it is clear that this experimental device with our detector system can successfully distinguish between the water and polypropylene rod at present. In the next stage, we will use a CdZnTe detector to build the detector system. Meanwhile, the grating manufacturing process will also be improved to achieve better visibility. The visibility is now 11%. The goal is 20%. After these improvements, smaller density differences will be distinguishable.
[1] P. Zhu, et al., Low-dose, simple, and fast grating-based X-ray phase-contrast imaging, Proc. Natl. Acad. Sci. USA 107 (31) (2010) 13576–13581. [2] Atsushi Momose, et al., Phase contrast X-ray computed tomography for observing biological soft tissues, Nat. Med. 2 (4) (1996) 473–475. [3] S.W. Wilkins, et al., Phase-contrast imaging using polychromatic hard X-rays, Nature 384 (6607) (1996) 335–338. [4] F. Pfeiffer, T. Weitkamp, O. Bunk, C. David, Phase retrieval and differential phase-contrast imaging with low-brilliance X-ray sources, Nat. Phys. 2 (4) (2006) 258–261. [5] Zhi-Feng Huang, et al., Alternative method for differential phase-contrast imaging with weakly coherent hard X-rays, Phys. Rev. A 79 (1) (2009) 013815. [6] P.R.T. Munro, K. Ignatyev, R.D. Speller, et al., Phase and absorption retrieval using incoherent X-ray sources, Proc. Natl. Acad. Sci. 109 (35) (2012) 13922–13927. [7] J. Hammonds, R. Price, E. Donnelly, D. Pickens, Phase-contrast digital tomosynthesis, Med. Phys. 38 (5) (2011) 2353–2358. [8] Di Wu, et al., In-plane spatial resolution measurements of a phase-contrast tomosynthesis prototype, Proc. SPIE 8224 (3) (2012) 1–8. [9] Di Wu, et al., A phantom study to characterize the imaging quality of a phase-contrast tomosynthesis prototype, SPIE Bios Int. Soc. Opt. Photonics (2013). [10] Z. Huang, K. Kang, L. Zhang, Z. Chen, F. Ding, Z. Wang, Q. Fang, Differential phase-contrast imaging experimental system under the incoherent condition with conventional X-ray tubes, IEEE Trans. Nucl. Sci. 56 (3) (2009) 1438–1443. [11] T.J. Schröter, et al., Large-area full field X-ray differential phase-contrast imaging using 2d tiled gratings, J. Phys. D: Appl. Phys. 50 (22) (2017) 225401. [12] Atsushi Momose, Wataru Yashiro, Yoshihiro Takeda, Sensitivity of X-ray phase imaging based on talbot interferometry, Japan. J. Appl. Phys. 47 (10R) (2008) 8077. [13] S. David, et al., Evaluation of powder/granular Gd2O2S: Pr scintillator screens in single photon counting mode under 140 keV excitation, J. Instrum. 8 (01) (2013) P01006. [14] Gao Kun, Physical Scheme of Phase-Contrast CT Prototype[R], National Synchrotron Radiation Laboratory, 2017. [15] R.F. Hu, The Experiment Technology and Application Research of X-Ray Grating Phase Contrast Imaging (Ph.D. dissertation), USTC, 2017, (in Chinese). [16] Lian Chen, Rongqi Sun, Ge Jin, The detector system design for the gratingbased phase contrast imaging CT prototype, 2018, p. 03613, arXiv preprint arXiv:1806. [17] A. Shor, Y. Eisen, I. Mardor, Edge effects in pixelated CdZnTe gamma detectors, in: Nuclear Science Symposium Conference Record IEEE, 2004.
4. Conclusions A detector system for a high-energy TGDPC human CT experiment device is proposed. This system contains 6 detectors, including 2304 channels. The test results show that the detector system has a good SNR and linearity performance and can meet the requirement of this high-energy phase-contrast CT experimental device. Compared to the MDBB-ST16_S commercial detector boards, the 2D phase-contrast imaging result indicates that our detector system has a better resolution, owing to the small pixel size. The TGDPC device is easily interfered with by these environmental fluctuations because the angular signal is as weak as a negative seventh order of magnitude. To reduce the impact of ambient interference, we think that a small pixel size is better under the same noise conditions because the detected refraction angular signal is larger than that of a large pixel size. Acknowledgments This work was supported by the National Key Scientific Instrument and Equipment Development Project, China (Grant No. ZDYZ2014-2) and in part supported by the National Natural Science Foundation of China under Grant (Nos. 11875249).
5
Contents lists available at ScienceDirect
Nuclear Inst. and Methods in Physics Research, A journal homepage: www.elsevier.com/locate/nima
Technical notes
A detector system for a high-energy phase-contrast human computed tomography experimental device Rongqi Sun a,b , Lian Chen b ,∗, Wenbin Wei c , Ge Jin a,b a
Department of Engineering and Applied Physics, University of Science and Technology of China, Hefei, 230026, China State Key Laboratory of Particle Detection and Electronics, University of Science and Technology of China, Hefei, 230026, China c National Synchrotron Radiation Laboratory, Hefei 230026, China b
ARTICLE
INFO
Keywords: Phase contrast Scintillator detector Computed tomography Read-out electronics
ABSTRACT A detector system is designed for a high-energy phase-contrast computed tomography (CT) experimental device with an X-ray tube voltage of 80 kV. The spot size of the X-ray tube is 0.4 mm*0.7 mm. The Gd2O2S scintillator detector is adopted in our design, which is characterized by a high detection efficiency and high light yield. This system contains 2304 channels and the corresponding high-density read-out electronics. Considering the influence of the large spot size of the X-ray tube source, a detector pixel size of 0.75 mm*1 mm is adopted to obtain sufficient spatial resolution. Because the light intensity fluctuation caused by the phase shift is very weak, a 20bit ADC is used to achieve sufficient efficient resolution (ER). The electronics test shows that the SNR can reach 72. Compared to a commercial detector board, the detector system has better resolution for the sample used in the phase-contrast imaging experiment.
1. Introduction Conventional X-ray imaging is a valuable tool in clinical medicine diagnosis and materials science. However, for those objects composed of different kinds of tissues with similar absorption cross sections, the imaging contrast is relatively weak. Since the last century, the X-ray phase-contrast imaging method characterized by high sensitivity has drawn increasing attention. Theoretically, when X-rays with energies greater than 10 keV pass through an object composed of low-Z elements, their phase-contrast cross section is 1000 to 100 000 times that of the absorption cross section [1]. As a consequence, the X-ray phase shift information can provide significantly greater imaging contrast. Over the last several decades, many approaches have been investigated to extract X-ray phase shift information [2,3]. Nevertheless, these methods, demanding the high coherence and high luminance of X-ray sources, are mainly available with a synchrotron radiation source or micro-focus X-ray generator. Therefore, none of them can allow for a wide range of biomedical or industry applications, where the X-ray imaging facility generally features a reasonably compact structure and the use of conventional incoherent hospital X-ray tube sources. In 2006, a three-grating differential phase-contrast (TGDPC) method based on a Talbot–Lau interferometer was developed [4]. This experiment makes it possible to perform the phase contrast efficiently with a conventional, low-brilliance X-ray source. Nevertheless, this method requires a very small grating period size; hence, it is not suitable for high-energy imaging, which is needed in clinical diagnoses. In 2009, an improved
TGDPC method based on geometric projection was proposed to solve this problem [5]. This method uses the geometric projection instead of the Talbot effect to generate self-imaging fringes; thus, the grating period can be much larger than the grating period with the Talbot–Lau interferometer method. In fact, the image formation of the TGDPC method is dependent on detecting these slight refraction angles when an X-ray passes through the objects. The higher X-ray energy yields smaller refraction angles, which means that the requirement for detection sensitivity becomes more stringent. To date, these existing phase-contrast computed tomography (CT) apparatuses suffer from a low tube voltage, usually below 70 kV, and a small field of view (FOV). These limitations make these devices available only in the local diagnosis of small objects [6–10]. Recently, a high-energy phase-contrast human CT based on a geometric projection method with a tube voltage of 80 kV was developed by the National Synchrotron Radiation Laboratory (NSRL). Because the goal of this device is to promote phase-contrast technology for the clinical application to the human body, its designed FOV is up to 666 mm*24 mm, which is the largest among these existing phasecontrast CT devices. The selected geometric parameters are illustrated in Fig. 1. To verify the technical feasibility, one CT experimental device is built. In this technical note, we design a detector system for this experimental device. To extract the phase shift information, one so-called phase stepping (PS) method is adopted. Along the transverse beam direction, the G1 grating is moved in several equidistant steps. Images
∗ Corresponding author. E-mail address: [email protected] (L. Chen).
https://doi.org/10.1016/j.nima.2019.162681 Received 22 March 2019; Received in revised form 27 August 2019; Accepted 1 September 2019 Available online 5 September 2019 0168-9002/© 2019 Elsevier B.V. All rights reserved.
R. Sun, L. Chen, W. Wei et al.
Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
the refraction angle at the sampling point on the shifting curve will decrease. This means that the light intensity fluctuation caused by the phase shift is weaker in our device, being approximately as low as 0.005%, according to the physical simulation. Consequently, the efficient resolution should be better than log2 (1∕0.005%) = 14.3 bit. In our design, six detector modules are adopted to obtain a sufficient measurement FOV. The total number of channels is 2304. Restricted by the high density of channels, discrete components cannot be used to design the front-end readout electronics. To meet the needs of readout electronics for high integration and high resolution, eighteen DDC1128 analog-to-digital converters (ADCs) are adopted. One DDC1128 contains 128 separate channels that carry out the currentto-voltage integration. Each channel includes a dual switched current integrator and an ADC with 20-bit resolution. The dual switched integrator is used to implement the ping-pong operation to reduce dead time. When the previous conversion is finished, the integrator capacitors are charged to a reference voltage. As the edge of the conversion signal rises, a selected integrator starts to collect the charge from the input channel over a given period of time, which causes a decrease in the amplifier output voltage. When the edge of the conversion signal begins to fall, the amplifier output is digitized by the on-chip ADC. Under external trigger signal control, all channels work simultaneously. The maximum integration time of these integrators is 1 ms. Owing to the ping-pong mechanism, the read-out of the digital signal will not add extra time overhead. Thus, DDC1128 can satisfy the requirement that the image frame rate be at least 800 fps.
Fig. 1. The selected geometric parameters of the prototype.
are captured per step. Each pixel records an intensity oscillation, which is called a shifting curve [11]. During the phase-contrast imaging, both the bright field reference and the object PS scan are captured. Then, through the analysis of the bright field reference and the object PS scan result, the phase imaging formation can be performed. Considering the performance of the detector, 𝛥𝛩min , the sensitivity of a TGDPC device, is defined as the minimum resolvable refraction angular deviation, which can be expressed as follows [12]: 𝛥𝛩min (x, y) = Vr (x, y) =
P2 2 ∗ √ 2𝜋dN Vr (x, y)*SNR(x, y) N br1 (x, y) br0 (x, y)
(1)
The structure of the detector board is illustrated in Fig. 2. These analog signals of 2304 pixels are transmitted to the detector board via six high-density connectors. Every three DDC1128 are responsible for one detector module, which are cascaded up to each other by a daisy chain. Then, 6-channel serial data from eighteen ADCs are transmitted into a Xilinx K7 FPGA and converted into parallel data. An air scan algorithm integrated in the linear correction module is used to correct the channel nonuniformity caused by the diversity of each pixel crystal and the gain difference of each electronics channel [15]. The background data are used as the offset value. Under X-ray exposure, bright field data are captured in the absence of a sample. Then, a specific value is chosen as the reference value of calibration. The calibration coefficient for each channel can be obtained through the reference divided by the bright field value with offset correction. The response of the detector is usually linear; thus, the channel nonuniformity can be removed in this way. This correction algorithm is shown in Eq. (5).
(2)
where P2 is the spacing of the G2 grating; d is the distance between the G1 grating and the G2 grating; and Vr and SNR are the shifting curve visibility [12] and the signal-to-noise ratio of each pixel, respectively. N is the number of acquisition frames; and br0 and br1 are the absolute values of the zero and first-order diffraction coefficients, respectively. According to Eq. (3), a good sensitivity requires a sufficiently high SNR. For the relatively high-energy X-ray, a good detection efficiency can yield a better SNR and shorter exposure time. In our design, the Gd2O2S (GOS) scintillator detector is adopted, which is characterized by high density and high light yield, compared to the NaI and CsI crystal [13]. In addition, the pixel size of the detector is another parameter that should be considered. Since the FOV of the design goal is so large, it is impracticable to adopt a detector with a pixel size of dozens of microns or a few microns, which most existing phase-contrast CT devices are using. The spatial resolution of the device is affected by the pixel size, the spot size of the X-ray tube and the geometry parameters. The semiempirical formula of the spatial resolution 𝛿 is expressed as follows [14]: √ 𝛿 = (𝑆 ∗ (𝑀 − 1))2 + 𝑃 2 ∕𝑀 (3) 𝑀 = (𝑓0 + 𝑙 + 𝑑 + 𝑓2 )∕(𝑓0 + 𝑙 + 𝑓 )
( ) 𝐼𝑐𝑜𝑟𝑟 = 𝐼𝑟𝑎𝑤 − 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡 ∗
𝐺 𝐼𝑙 − 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡
(5)
where 𝐼𝑐𝑜𝑟𝑟 is the channel response with correction; 𝐼𝑟𝑎𝑤 is the response value without correction; 𝐼𝑙 is the bright field value; 𝐼𝑜𝑓 𝑓 𝑠𝑒𝑡 is the background value; and G is the reference value of calibration.
(4)
After linear correction, all data are converted into serial data, are cached into the buffer and then await the serial peripheral interface (SPI) for transmission to the data collection board (DCB).
where P is the transverse size of the detector pixel. S is the transverse size of the spot. M is the geometric magnification. The selected spatial resolution goal without the pixel offset method is 0.4 mm. The spot size of the X-ray tube is 0.4 mm*0.7 mm. Putting these parameters in Fig. 1 into Eq. (3), the transverse size of the detector pixel should be lower than 0.8 mm. Therefore, the Hamamatsu S12058(X) detector module, constructed of a 16*24 array of individual GOS pixels, is used in the experimental device. Its pixel size is 0.75 mm*1.0 mm, which is the smallest among the available commercial medical GOS scintillator detectors.
Compared to the previous version of the detector board [16], we have made some improvements. The S12058(X) module includes the A and B sides. To reduce electronic noise, the analog ground of the A and B sides is split and connected to the digital ground by two zero-ohm resistors near the ADC. The analog signal traces are placed on the PCB inner layer and kept isolated from each other by ground traces. The previous version adopts the solution of one detector board with one detector module, the width of which is limited to 17.8 mm. Therefore the above improvements are very difficult to achieve in the previous version. The test shows that the SNR of the current version is approximately twice as good as that of the previous one.
2. Design of the readout electronics Increasing the X-ray energy will reduce the shifting curve visibility and the refraction angle. Therefore, the product of the slope and 2
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Nuclear Inst. and Methods in Physics Research, A 946 (2019) 162681
Fig. 2. The design of the detector board.
3.2. SNR test As mentioned above, a high SNR can yield better detection sensitivity of the TGDPC device. An SNR test is implemented on the detector board with a CTR2150 tube-based X-ray source from Dunlee. The voltage and current of the X-ray tube source are set to 80 kV and 180 mA, respectively. The exposure time and the integration time are set to 8 s and 1 ms, respectively. In the X-ray exposure, one pseudo trigger is generated to capture data many times. For every channel, the FWHM of the Gaussian distribution fitted to the histogram is used to evaluate the noise level 𝛿𝑏 , while the peak position of the Gaussian fitted curve is considered as the signal value S. In the absence of Xray exposure, the detector system captures multiple background data. The background data B of every channel is the peak position of the Gaussian fitted curve of the background histogram. Therefore, the SNR is obtained according to Eq. (7).
Fig. 3. The noise of all channels.
3. The test result of the detector system
SNR =
3.1. Noise test
(7)
The test results of the SNR for every channel are illustrated in Fig. 4. We can see that the SNR of these channels located on the edge of the detectors is evidently lower than that of other channels. This is mainly caused by the edge effect due to the imperfections introduced in the processing of the detector parts [17]. The edge effect can be corrected by the linear correction module. The average SNR of all 2304 channels is 72. There is no available commercial detector board with a pixel size of 0.75 mm*1 mm. Thus, one commercial detector board MDBB-ST16_S from the Detection Technology Company is used to perform a performance comparison experiment, the detector pixel size of which is 1 mm*1.08 mm. The average SNR of the commercial detector board is 85. Since the SNR is inversely proportional to the pixel size, the efficient SNR of our detector board is 72*(1.08*1/(0.75*1))0.5 = 86. Therefore, the performance of our detector board remains consistent with that of MDBB-ST16_S.
One noise test is performed on the detector board. In the absence of an actual detector signal, a pseudo trigger is fed into the system to sample the baseline data many times. The Gaussian fitting is performed on the background data histogram for every channel. The full-width-athalf-maximum (FWHM) of the Gaussian fitting curve is used to evaluate the level of noise, as illustrated in Fig. 3. The efficient resolution(ER) of the detector system can be calculated approximately as follows: ER = N − log2 (𝜎)
𝑆 −𝐵 𝛿𝑏
(6)
where N is the resolution bits of the ADC and 𝜎 is the average noise level. The average noise of all channels is 23 ADC bins. Among these channels, the worst is 32 ADC bins, the ER of which can reach 14.6 bit. Hence, the ER performance of the detector system can satisfy the design requirement. 3
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Fig. 4. The SNR result of all 2304 channels.
Fig. 8. The 2-dimensional imaging results.
3.3. Test of linearity For X-ray imaging, poor linearity of the detector will cause a measurement error, thus decreasing the resolution. To test the detector linearity, the current of the X-ray is stepped up to obtain different incoming charge quantities. We use the peak position of the ADC output histograms as the pixel response. In Fig. 5(a), the fitted linear curve for one typical pixel is illustrated. The linear correlation coefficient histogram of all pixels is shown in Fig. 5(b). It is evident that most of the detector pixels have good linearity performance.
Fig. 5. The results of the linear test.
3.4. The results of the imaging test The setup of the experimental device is shown in Fig. 6. The periods of G0, G1 and G2 are 32.5 um, 26 um and 130 um, respectively. All tests are performed with a CTR2150 tube-based X-ray source whose voltage and current are set to 80 kV and 180 mA. Its focal spot size is 0.4 mm*0.7 mm. The experimentally measured shifting curve visibility is 11%. To evaluate the system performance, a sample is employed, as illustrated in Fig. 7. The sample is composed of a sealable tube, some water and a polypropylene rod with a density of 0.92 g/cm3 . The polypropylene rod is put into the tube, surrounded by the water in the lower part. Fig. 8 shows the 2-dimensional (2D) imaging results of the sample, performed on the experimental device. The PS algorithm has the capacity to separate the absorption and the phase information from the detector data simultaneously. Since the FOV of the detector system is limited, half of the sample can be captured only in the 2D (2-dimension) scan. The curve on the right side of Fig. 8 is the contour of the twelfth row of both the absorption imaging result and the phase-contrast imaging result. In the contour of our detector board, the positions indicated by the two red arrows are the boundaries of the rod-to-water and the water-to-tube walls, which are two clearly visible sags. The refraction angular signals at these two positions are 172 and 213 nano-radians, respectively. Thus, the rod and water can be distinguished in the 2D phase-contrast imaging by our detector board. However, no visible tag appears in the contour of the commercial board. This is because the maximum refraction angle signal detected
Fig. 6. The setup of the experimental device.
Fig. 7. The test sample.
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Fig. 9. 16-slice phase-contrast CT of the sample.
References
by a pixel is inversely proportional to the square root of the pixel horizontal size [12]. Therefore, the refraction angle detected by the large pixels of the commercial board is small enough to be overwhelmed by ambient noise, such as a mechanical error or temperature fluctuation. Meanwhile, it is evident that absorption imaging cannot distinguish the water from the polypropylene, according to Fig. 8. A phase-contrast CT scan also was also performed on our detector board with the sample. Fig. 9(a) shows the 16-slice phase-contrast CT imaging result. The upper part of the rod, from the first layer to the eighth layer, is surrounded by air. The lower part is immersed in water. In Fig. 9(b), the X-axial contour of the fourteenth layer, looks like a saddle. We can see that there is a clear dividing line between the water and the polypropylene rod. Therefore, it is clear that this experimental device with our detector system can successfully distinguish between the water and polypropylene rod at present. In the next stage, we will use a CdZnTe detector to build the detector system. Meanwhile, the grating manufacturing process will also be improved to achieve better visibility. The visibility is now 11%. The goal is 20%. After these improvements, smaller density differences will be distinguishable.
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4. Conclusions A detector system for a high-energy TGDPC human CT experiment device is proposed. This system contains 6 detectors, including 2304 channels. The test results show that the detector system has a good SNR and linearity performance and can meet the requirement of this high-energy phase-contrast CT experimental device. Compared to the MDBB-ST16_S commercial detector boards, the 2D phase-contrast imaging result indicates that our detector system has a better resolution, owing to the small pixel size. The TGDPC device is easily interfered with by these environmental fluctuations because the angular signal is as weak as a negative seventh order of magnitude. To reduce the impact of ambient interference, we think that a small pixel size is better under the same noise conditions because the detected refraction angular signal is larger than that of a large pixel size. Acknowledgments This work was supported by the National Key Scientific Instrument and Equipment Development Project, China (Grant No. ZDYZ2014-2) and in part supported by the National Natural Science Foundation of China under Grant (Nos. 11875249).
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