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A novel image optimization method for dual-energy computed tomography
Nuclear Instruments and Methods in Physics Research A 722 (2013) 34–42
Contents lists available at SciVerse ScienceDirect
Nuclear Instruments and Methods in Physics Research A journal homepage: www.elsevier.com/locate/nima
A novel image optimization method for dual-energy computed tomography Jia Hao a,b, Kejun Kang a,b, Li Zhang a,b,n, Zhiqiang Chen a,b a b
Department of Engineering Physics, Tsinghua University, Beijing 100084, China Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China
art ic l e i nf o
a b s t r a c t
Article history: Received 23 January 2013 Received in revised form 25 April 2013 Accepted 26 April 2013 Available online 6 May 2013
Dual-energy computed tomography (CT) has been widely used in explosive detection as it has the capability of accurate material discrimination. However, its reconstructions always suffer from severe noise and artifacts. In practical application, the computation of effective atomic number is much more sensitive to the noise and decomposition errors, which significantly degrades its image quality. In this paper we introduced a reference image based non-local image optimization method for dual-energy CT, based on the knowledge that dual-energy reconstructions have strictly the same structure and they are precisely registered. Reconstructed low-energy attenuation image from dual-energy CT serves as the reference image in the proposed method. The structure information was derived from the low-energy attenuation image using a non-local pixel similarity measurement, and weight average the effective atomic number image with the established weight relationship in the corresponding position. Experiments with a commercial dual-layer detector CT scanner demonstrated that the proposed strategy achieves much better results in noise and artifacts reduction than previous methods, and significantly improves the image quality. Introduction: We propose a reference image based non-local image optimization method for effective number image in dual-energy CT, which can significantly improve the visual quality and reduce the artifacts. & 2013 Elsevier B.V. All rights reserved.
Keywords: Dual-energy CT Image optimization Non-local Effective atomic number Electron density
1. Introduction An object's material type can be better determined by using both its density and effective atomic number. Conventional singleenergy CT images can only approximate the density measurements of scanned objects, while dual-energy CT provides more information. Thus, dual-energy CT has been widely used in medical and industrial area since it was first introduced by Alvarez and Macovski [1]. By scanning with two distinguished beam energies, it can provide atomic number measurements of scanned objects in addition to density measurements, which is more useful in material discrimination [2]. There have been a lot of researches on dual-energy CT, including the system optimization [3,4], dualenergy reconstruction and calibration algorithms [5,6], quantitative analysis and applications [7]. It has been demonstrated that using a conventional X-ray source with a broad energy spectrum, one can still separate the attenuation coefficient into the contributions from photoelectric effect and Compton scattering, which can
n Corresponding author at: Department of Engineering Physics, Tsinghua University, Beijing 100084, China. Tel.: +86 10 62780909x86201. E-mail address: [email protected].(L. Zhang)
0168-9002/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.nima.2013.04.073
be approximately modeled using a material's effective atomic number and electron density, and knowledge of the X-ray energy spectra [8]. With properly designed dual-energy reconstruction techniques, one can characterize any material in its image by its effective atomic number and effective density, and determine whether it is dangerous or not. In this case, most of the luggage screening CT systems adopt dual-energy technique. Different modalities of dual-energy data acquisition were researched within three years. Scanning the object twice is the simplest way [9]. Two acquisitions are performed successively, and the energy switch is realized by voltage tuning. Furthermore, a prototype of the modern rapid kVp switching scanner was likewise presented very early on [10]. In 2005, Siemens launched their CT SOMATOM Definition, which generates two spectra by installing two X-ray tubes into one CT system [11]. Temporal resolution and tissue characterization ability are greatly improved with this scanning mode. However, they need accurate image registration between the high and low energy images. The single exposure technique using an energy-selective detector avoids this disadvantage and eliminates misregistration artifacts [12]. Explosive detection dual-energy CT system always uses dual-layer detector (or ‘sandwich detector’), which is comprised of a thin top scintillator that is more sensitive to low energy photons and a bottom
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scintillator that absorbs the remaining photons. It is simple and effective, however suffers from poor energy separation and the noise is significantly enhanced in the projection data, which will also degrade the dual-energy image quality. In practical application, the dual-energy CT images also suffer from decomposition errors and artifacts, especially in the effective atomic number measurement. Although there have been a lot of methods researched to improve the dual-energy CT image quality, it is far from satisfaction. As we know, all the previous methods are unable to recover structure information and suppress artifacts in the effective atomic number image [13,14]. For the purpose of object segmentation and material discrimination, the CT images are usually used as a primary source, while the effective atomic number images are used as a secondary source. We propose a novel non-local image optimization method for dual-energy CT, which achieves much better results than previous ones both in noise reduction and artifact suppression. It is a postreconstruction processing method for the distribution image of effective atomic number. The reconstructed attenuation map serves as a reference image and its structure information is exacted by a non-local similarity computation scheme. The corresponding pixels in the effective atomic number image are weighted averaging with the weight coefficient derived from the reference image. Experiments with a commercial explosive detection dual-energy CT scanner validate our method. It utilizes single X-ray source, dual-layer detector configuration and acquires dualenergy projections simultaneously. The dual-energy reconstructions (attenuation image, electron density image and effective atomic image) are well matched. Using the proposed method, noise and artifacts in the effective atomic number image are effectively reduced. The image quality can meet the need for accurate material discrimination. This paper is organized as follows: First we review the dualenergy CT technique and introduce its implementations and reconstruction methods. Then, we introduced the non-local image optimization method in Section 3. Experiments and results are presented in Section 4, and we also compare our method with previous image processing methods. Finally, we present discussions and conclusions.
2. An overview of dual-energy CT 2.1. Dual-energy decomposition and reconstruction techniques The basic principles of dual-energy imaging are quite simple and well understood. It scans objects with low and high X-ray spectra, respectively, and then reconstructs the effective atomic number and electron density with a properly designed decomposition model. One of the widely used decomposition models is based on Compton scatter and the photoelectric effect. In the photon energy range most used in explosive detection, interactions between X-rays and scanned materials are dominated by Compton scatter and the photoelectric effect [15]. The Compton scatter and the photoelectric effect are both material and energy dependent; and each of them is modeled as the product of a material dependent coefficient and an energy dependent term. Another frequently used decomposition method is based on two basis materials, which assumes that the attenuation of materials can be expressed as a linear combination of two main functions, photoelectric and Compton functions or the attenuation of the two basis materials [16]. The decomposition computation can be conducted both in the projection domain (pre-reconstruction methods) or the image domain (post-reconstruction methods). Explosive detection dual-energy CT systems utilize X-ray tubes as radiation source, which have a broad spectrum. Generally
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speaking, the projection domain decomposition approach can avoid beam-hardening effects. In this case, most of the commercial systems use pre-reconstruction decomposition methods to obtain the distribution of effective atomic number and electron density of the scanned object. In this paper, all the experiments are based on basis material decomposition pre-reconstruction methods. Then we will make a brief introduction to the effective atomic number and electron density computation steps. As we have discussed above, the total mass attenuation coefficient of an object containing two constituent elements can be approximately expressed as a linear combination of two basis element's mass attenuation coefficients, as μðEÞ ¼ b1 μ1 ðEÞ þ b2 μ2 ðEÞ
ð1Þ
where μ1 ðEÞ and μ2 ðEÞ are the attenuation of the two basis materials under X-ray energy E, b1 and b2 are the decomposition coefficients of the two basis materials. In a conventional X-ray CT system, the primary spectrum from the X-ray tube is polychromatic and detectors integrate over energy. Suppose the lowenergy and high-energy projection data are p1 and p2, the dualenergy CT projection function is formulated as: Z Z ! ð2Þ p1 ¼ −ln D1 ðEÞexp ½− μðE; r ÞdldE Z p2 ¼ −ln
Z D2 ðEÞexp ½−
! μðE; r ÞdldE
ð3Þ
! here, μðE; r Þ is the linear attenuation coefficient under X-ray energy E. DðEÞ is the number of emitted photons in energy unit E, however in practical application it should be times by the energy response function of the detector. From Eq.(1), the dualenergy CT projection function can be rewritten as: Z Z p1 ¼ −ln D1 ðEÞexp ½− ½b1 μ1 ðEÞ þ b2 μ2 ðEÞdldE ð4Þ Z p2 ¼ −ln
Z D2 ðEÞexp ½−
½b1 μ1 ðEÞ þ b2 μ2 ðEÞdldE
ð5Þ
Solving this Eqs. (4) and (5), we can obtain the two basis material decomposition coefficient b1 and b2. However, it is not trivial because of the energy dependence of the attenuation and the polychromatic X-ray spectrum. Hence, a look-up table (LUT) method is always utilized to provide accurate results. After obtaining the two decomposition coefficients, the effective atomic number Z ef f and electron density ρe can be computed as: " # 3:5 1=3:5 b1 ρe1 Z 3:5 1 þ b2 ρe2 Z 2 ð6Þ Z ef f ¼ b1 ρe1 þ b2 ρe2 ρe ¼ b1 ρe1 þ b2 ρe2
ð7Þ
where ρe1 and ρe2 are the electron density values of the two basis materials, Z1 and Z2 are the effective atomic number of the two basis materials, respectively. Reconstructed Z ef f and ρe provide more information than attenuation coefficient in material discrimination. As we have discussed above, different images can be acquired simultaneously per dual-energy reconstruction, including highenergy and low-energy attenuation images, effective atomic number image and electron density image. The value distributions in the images are quite different because they represent different physical meanings. However, they should have strictly the same structures. In the sandwich detector dual-energy CT system, all the images are well registered. In Fig. 1, the four reconstructions from a dual-energy CT system are shown, in which (a) and (b) are the low-energy and high-energy attenuation coefficient images, respectively, (c) is the reconstructed effective atomic number image, and (d) is the distribution image of the electron density.
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Fig. 1. Reconstructed images from a sandwich detector dual-energy CT. (a) and (b) are the linear attenuation coefficient maps under the two spectra energy. (c) is the reconstructed images of effective atomic number and (d) is the distribution of electron density.
Fig. 2. Schematic diagram of the proposed reference based non-local image optimization strategy. (a) is the low-energy attenuation image from dual-energy CT. It serves as reference image in the proposed method. The weight coefficients are computed in this image to establish a relationship between different pixels; (b) is the effective atomic number image to be restored. Pixels in the corresponding position are weighted averaging with the calculated coefficients.
2.2. Artifacts and errors in dual-energy reconstruction The existing dual energy techniques are not accurate enough in material discrimination and the image quality is far from satisfaction. Ying et al. [17] has researched the limitations and problems of explosive detection dual-energy CT, including polynomial approximation errors, boundary constraints caused by truncation, X-ray spectral drift and so on. They will significantly degrade the image
quality and deteriorate the performance of material discrimination and explosive detection. In additional, the nonlinear dual energy decomposition also amplifies noise, so a small amount of scatter and noise may cause severe artifacts, such as a cupping effect and streaks artifacts. Given our experiences, the computation of effective atomic number is much more sensitive than electron density. Among the four reconstructed images from dual-energy CT, attenuation
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coefficient maps and electron density image obviously have higher SNR and better image quality, and the structure and detail information are well preserved. In the effective atomic number image (Fig. 1(c)), there are also some singularities and structure errors besides noise. The edge of the central cylinder phantom cannot be distinguished clearly. Thus, in commercial dual-energy CT system, image optimization and processing are necessary.
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image. It is also worth mentioning that the statistic value accuracy is not impacted by this method, because of the normalized weighted average is only a numerical redistribution step. The image singularities and structure errors are smoothed by the weighted processing, however, the mean value in a selected region maintains the same. In implementation the weight calculation area can be limited to a smaller window instead of the whole image for computational aspects.
3. Materials and methods In order to optimize the effective atomic number image, the structure information of attenuation image should be quite helpful as they have strictly the same edge and represent the same feature. Thus we adopt the attenuation map as a reference image and restore the effective atomic number image. The proposed method is motivated by non-local means which achieve state-ofthe-art in image noise reduction [18]. We design a non-local pixel similarity searching and weighted averaging strategy to recover the structure information and reduce the noise with the help of a high-quality reference image. This method is based on an assumption that if a pixel is similar with another pixel in the reference image, it should be similar with the pixel in corresponding position in the current image to be restored. The reference image is expressed as I ref , while the target image is expressed as I. The two images should be strictly registered. For a selected pixel i in the target image I, the pixel value is μðjÞ. Compute the weight function wði; jÞ in I ref pixel by pixel. The weight function is based on the similarity between pixel i and j, which is measured by the distance between two n n blocks Ni and Nj, whose centers locate at pixel i and j, respectively. The distance dði; jÞ between pixel i and j can be computed by a Gaussian-weighted Euclidean distance between the two blocks Ni and Nj: dði; jÞ ¼ ∥NðjÞ−NðiÞ∥22;a
4. Experiments and results 4.1. Experimental system We use a commercial explosive detection dual-energy CT developed by Nuctech Corporation. It is equipped with a duallayer detector acquires single X-ray source CT data using two scintillation layers on top of each other with which two energy datasets are acquired simultaneously (as shown in Fig. 3). In this system, the X-ray tube works at a high voltage of 160 kV. An external copper filter is inserted into the two detector layers to generate low and high energy spectra, respectively. A GOS detector array with 736 units is arranged on an arc with post-patient tungsten collimators to stop scattering. The top layer encountered by the X-ray photons absorbs most of the low-energy X-ray, while the bottom detector layer absorbs the high-energy X-ray photons. Low-energy and high-energy projection data are acquired separately from the two layers. In the dual-layer detector system, the scattered X-ray is mainly from the shielding, collimator and copper filter. It is a type of noise signal getting into the detector, and it does not have the image information of object. Experiment results show that the dual-energy reconstruction image quality is not obviously degraded by the scattered photon.
ð8Þ
In Eq. (8), a is the standard deviation of the Gaussian function. Based on the pixel distance, the weight coefficient can be formulated as: ! ∥NðjÞ−NðiÞ∥22;a dði; jÞ ¼ exp − wði; jÞ ¼ exp − 2 ð9Þ 2 h h where h is a smooth parameter. It depends on the noise variance in the reference image I ref and can be experimentally determined. For all the pixels in I ref , compute the weight coefficients between the selected pixel i and the other pixels. Then, in the target image to be restored average all the pixels use the weight coefficient in the corresponding pixel in the reference image. The final pixel value RðμðiÞÞafter optimization can be expressed as follows:
Fig. 3. A schematic illustration of the dual-layer detection system. Only a few detector elements in the CT system are shown here.
0.1
∑ wði; jÞμðjÞ RðμðiÞÞ ¼
j∈I ref
ð10Þ
0.08
In Eq. (10), the weight coefficient wði; jÞ is computed between pixels i and j in the reference image I ref , while the weighted average step is implemented in the target image to be optimized. ∑j∈Iref wði; jÞ is used as a normalizing factor to maintain the value accuracy in the final optimized image. When the reference image is the same with the target image, this image processing method falls back to the non-local means denoising method. The non-local searching and weighted average scheme is shown in Fig. 2. In dual-energy CT, the attenuation reconstruction can be served as reference image as it has better image quality and contrast resolution. The structures and features are strictly the same between the attenuation image and effective atomic number
0.06
∑ wði; jÞ j∈I ref
0.04
0.02
0
0
20
40
60
80
100
120
140
160
Fig. 4. The low-energy and high-energy spectrums used in the dual-layer CT system. They are generated by MCNP simulation.
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Fig. 5. Image comparison between the original reconstructed image and the processed image using the proposed method. (a) is the original effective atomic number image from the dual-energy CT system and (b) is the image after processing. The display greyscale is [0, 7].
Fig. 6. The value comparison along the profile shown in the left image.
Fig. 7. The effective atomic number image of a suitcase with a cylinder phantom in it. Metal artifact is severe in this image. (a) is the original image from the dual-energy CT reconstruction and (b) is the result after restoration. The value along the profiles will be compared in Fig. 7.
The initial low and high energy spectrum used in this system is estimated by MCNP simulations, as presented in Fig. 4. We must state that, the single source dual-layer detection system has its disadvantage. In order to make the energy separation appropriate for material discrimination, the high-spectra is significantly harder than the other dual-energy methods (such as dual kVp method).
In this condition, the detected signal in the bottom layer is relatively low, which will lead to a lower SNR in the high-energy reconstruction image. In this system the copper filter thickness was optimized for material discrimination in safety check. After dual-energy projection acquisition, electron density and effective atomic number are reconstructed with a basis material decomposition
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model in projection domain, which can avoid the beam-hardening effects. The X-ray spectra which govern the projection functions are estimated from transmission measurements, following the method introduced in Ref. [19]. After projection acquisition, dual-energy projection transformation is realized through the basis material model and the estimated spectra. A general fan-beam filtered back projection (FBP) algorithm is used to reconstruct the distribution of attenuation coefficients μðEL Þ and μðEH Þ at the two used X-ray spectra, effective atomic number Z ef f and electron density ρe . In this study, we use carbon and aluminum as basis materials in the decomposition, because most of the explosive material densities locate between the two basis materials.
Fig. 8. The value comparison along the profile presented in Fig. 7.
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The parameters in the proposed method are optimized by experiments. The smooth coefficient h is the most important one, which greatly impacts the restoration result. It depends on the noise variance in the reference image (in our study the low-energy attenuation map serves as reference image). The block dimension in the similarity computation is fixed to 5 5. And the searching window is limited to a 9 9 neighborhood square area to make the algorithm more efficient. 4.2. Experiment results A phantom consisting of three cylinders is scanned using the dual-layer detector CT system. Fig. 5(a) is the reconstructed effective atomic number image without any processing. It obviously suffers from severe noise and artifact. Fig. 5(b) is the restored image using the proposed strategy. Different materials and phantoms are well discriminated and the noise is significantly reduced. Most of the visible artifacts disappear after processing, and the SNR is improved more than 20 times than the original reconstruction. For quantitative validation, we compare the original image, optimized image with the ground-truth, as shown in Fig. 6. In the central cylinder object, the container wall and the containing items are made of different materials, and have different effective atomic numbers. In the original image it is difficult to be separated due to severe noise and artifacts, while after image optimization the two parts present clear contrast. In order to further study the proposed method, we use a suitcase which is always scanned in a luggage screening system to evaluate this method. It is more complicated than the previous
Fig. 9. Dual-energy CT imaging experiment using a real baggage. (a) is the DR image of the scanned baggage. The dashed line indicates the plane of the reconstruction. (b) and (c) are the low-energy and high-energy attenuation reconstruction images, respectively. The low-energy attenuation image serves as the reference image in the proposed method. (d) is the electron density map reconstructed using dual-energy decomposition method; (d) is original effective atomic number image without any processing; (e) is the effective atomic number image processed by the proposed method. The display greyscale of effective atomic number images is [0, 20], and that of the reconstructed attenuation map is [0, 0.3].
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Fig. 10. Comparison of the results using different methods. (a) is the low-energy attenuation image directly reconstructed from the projection data. It serves as the reference image in the proposed image optimization method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed method. The display greyscale of effective atomic number images is [0, 7], and that of the attenuation map is [0, 0.03].
phantoms. In this case, the reconstructions are also degraded by severe metal artifacts. The effective atomic number images before and after processing are compared in Fig. 7(a) and (b). It can be found that the image quality is much better with this strategy and the structure is well recovered from the noise and artifacts. The value accuracy after image optimization processing is also improved comparing with the ground-truth of the scanned phantom, as shown in Fig. 8. A more complex scan situation was tested, including many other materials with different densities, shapes and volumes. The dataset comes from a practical dual-energy CT scanning, and the reconstructions are shown in Fig. 9.
remain in the restored image. Our proposed methods performs superior than the previous methods in both noise reduction and artifact suppress, and the visual quality is significantly improved. In the partial enlarged image shown in Fig. 12 we can find that the structure is well recovered with the help of low-energy attenuation reference image. The container wall and items inside are clearly distinguished.
4.3. Comparison with previous methods
5.1. Why this algorithm works?
As we have introduced, the effective number image from duallayer CT suffers from severe artifact and errors besides noise. One of the advantages of our method is that: it uses the non-local structure information from a high-quality reference image (attenuation image in dual-energy CT), which is helpful for structure recovery and artifact reduction. However, the other post-reconstruction processing methods are both performed in the target image itself, and can only suppress the noise level. In order to further validate the proposed method, experiments were conducted using two other image optimization methods for comparison, including the total variation minimization (TVM) method and non-local means (NLM) method [18,20]. They are representative and popular in image noise reduction. The parameters in TVM and NLM methods are determined experimentally, to achieve the best compromise between visual quality and spatial resolution. The result comparisons are shown in Figs. 10 and 11, with different scanning objects. As expected, they can suppress the noise to certain extent, while the artifacts and structure errors still
The proposed image optimization method is a postreconstruction processing method for dual-energy CT reconstructions. It establishes a relationship between different images acquired simultaneously, based on the knowledge that all the reconstructed images should have strictly same structure and be precisely registered. The structure information is extracted using a non-local searching method from the high-quality attenuation coefficient image. Then average the effective atomic number with the calculated weight coefficients in different pixels. This method preserves the value accuracy due to the normalization weight, however recovers the structure information and significantly improves the visual quality, which is useful for accurate material discrimination. It differs from previous image restoration methods in that: this method provides a novel idea to extract the image structure information and establishes a relationship between the dual-energy reconstructions using a non-local searching method. The reference image plays an important role in the proposed method.
5. Discussion
J. Hao et al. / Nuclear Instruments and Methods in Physics Research A 722 (2013) 34–42
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Fig. 11. Comparison of the results using different methods with a suitcase scanned by commercial dual-energy CT. (a) is the low-energy attenuation image directly reconstructed from the projection data. It serves as the reference image in the proposed method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed method. The display greyscale of effective atomic number images is [0, 20], and that of the attenuation map is [0, 0.05].
However, this method should be carefully evaluated in some certain conditions. As the attenuation image and effective atomic number image have different physical meanings, there is no linear relationship between them. Sometimes a single energy attenuation map is unable to distinguish different materials. In this condition the attenuation map cannot serve as the reference image or it will lead to errors in the effective atomic number image using this method. But in our experiments, this method still works when we choose a proper reference image in the two reconstructed attenuation images. If the low-energy attenuation image is not suitable as reference, we can change it to the high-energy attenuation image. 5.2. Algorithm acceleration This method has been demonstrated state of the art in artifact reduction and structure recovery. However, the searching scheme is relatively slow than the previous local image processing methods. The high computational complexity is due to the cost of weights calculation for all pixels in the image. In our study, it takes about 20 s in processing a 512 512 image without any acceleration. There are two main ways to address computational time: the decrease of computations performed and the improvement of the implementation. A simple way to deal with the problem of the computational time is to share the operations on several processors via a cluster or a grid. The image can be divided into a series of sub-images, each of them being treated separately by one processor. Another way is using pixel pre-classification method, to reduce the number of pixels taken into account in the weighted average. The similar acceleration method has been applied in
medical image noise reduction [21]. After acceleration and algorithm optimization, the computation time can be reduced to 0.5 s in a personal computer system, which can satisfy the practical application in commercial systems.
6. Conclusion A novel non-local method for dual-energy CT image optimization was introduced in this paper. It uses the structure information from a high-quality reference image (here we use the attenuation coefficient image) and we design a non-local weighted average scheme for the effective atomic number image. Using this method, the image quality is significantly improved, which is quite useful and important for material discrimination. This strategy is simple but robust, and the relationship of structure information between different images is established. Experiments from a commercial dual-energy CT scanner have been conducted and the results demonstrate its state-of-the-art performance in dual-energy image processing. The effective atomic number image is significantly improved both in visual quality and noise reduction, which can lower the false alarm rate and improve detection accuracy. It should be stated that, this reference image based non-local processing method can also be extended to the other CT imaging applications, such as metal artifacts reduction, under-sampling artifacts reduction, et al., only when there is a structure matched high-quality reference image.
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Fig. 12. The partial enlarged images of Fig. 11. A cylinder phantom is scanned in the draw-box and the reconstruction is degraded by metal artifacts. (a) is the low-energy attenuation image directly reconstructed from the low-energy projection data. It serves as the reference image in the proposed method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed image optimization method. The display greyscale of effective atomic number images is [0, 20], and that of the attenuation map is [0, 0.05].
Acknowledgements This work is partly supported by National Key Technology R&D Program of the Ministry of Science and Technology (No. 2012BAI07B05), China. References [1] [2] [3] [4] [5] [6]
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Contents lists available at SciVerse ScienceDirect
Nuclear Instruments and Methods in Physics Research A journal homepage: www.elsevier.com/locate/nima
A novel image optimization method for dual-energy computed tomography Jia Hao a,b, Kejun Kang a,b, Li Zhang a,b,n, Zhiqiang Chen a,b a b
Department of Engineering Physics, Tsinghua University, Beijing 100084, China Key Laboratory of Particle & Radiation Imaging (Tsinghua University), Ministry of Education, Beijing 100084, China
art ic l e i nf o
a b s t r a c t
Article history: Received 23 January 2013 Received in revised form 25 April 2013 Accepted 26 April 2013 Available online 6 May 2013
Dual-energy computed tomography (CT) has been widely used in explosive detection as it has the capability of accurate material discrimination. However, its reconstructions always suffer from severe noise and artifacts. In practical application, the computation of effective atomic number is much more sensitive to the noise and decomposition errors, which significantly degrades its image quality. In this paper we introduced a reference image based non-local image optimization method for dual-energy CT, based on the knowledge that dual-energy reconstructions have strictly the same structure and they are precisely registered. Reconstructed low-energy attenuation image from dual-energy CT serves as the reference image in the proposed method. The structure information was derived from the low-energy attenuation image using a non-local pixel similarity measurement, and weight average the effective atomic number image with the established weight relationship in the corresponding position. Experiments with a commercial dual-layer detector CT scanner demonstrated that the proposed strategy achieves much better results in noise and artifacts reduction than previous methods, and significantly improves the image quality. Introduction: We propose a reference image based non-local image optimization method for effective number image in dual-energy CT, which can significantly improve the visual quality and reduce the artifacts. & 2013 Elsevier B.V. All rights reserved.
Keywords: Dual-energy CT Image optimization Non-local Effective atomic number Electron density
1. Introduction An object's material type can be better determined by using both its density and effective atomic number. Conventional singleenergy CT images can only approximate the density measurements of scanned objects, while dual-energy CT provides more information. Thus, dual-energy CT has been widely used in medical and industrial area since it was first introduced by Alvarez and Macovski [1]. By scanning with two distinguished beam energies, it can provide atomic number measurements of scanned objects in addition to density measurements, which is more useful in material discrimination [2]. There have been a lot of researches on dual-energy CT, including the system optimization [3,4], dualenergy reconstruction and calibration algorithms [5,6], quantitative analysis and applications [7]. It has been demonstrated that using a conventional X-ray source with a broad energy spectrum, one can still separate the attenuation coefficient into the contributions from photoelectric effect and Compton scattering, which can
n Corresponding author at: Department of Engineering Physics, Tsinghua University, Beijing 100084, China. Tel.: +86 10 62780909x86201. E-mail address: [email protected].(L. Zhang)
0168-9002/$ - see front matter & 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.nima.2013.04.073
be approximately modeled using a material's effective atomic number and electron density, and knowledge of the X-ray energy spectra [8]. With properly designed dual-energy reconstruction techniques, one can characterize any material in its image by its effective atomic number and effective density, and determine whether it is dangerous or not. In this case, most of the luggage screening CT systems adopt dual-energy technique. Different modalities of dual-energy data acquisition were researched within three years. Scanning the object twice is the simplest way [9]. Two acquisitions are performed successively, and the energy switch is realized by voltage tuning. Furthermore, a prototype of the modern rapid kVp switching scanner was likewise presented very early on [10]. In 2005, Siemens launched their CT SOMATOM Definition, which generates two spectra by installing two X-ray tubes into one CT system [11]. Temporal resolution and tissue characterization ability are greatly improved with this scanning mode. However, they need accurate image registration between the high and low energy images. The single exposure technique using an energy-selective detector avoids this disadvantage and eliminates misregistration artifacts [12]. Explosive detection dual-energy CT system always uses dual-layer detector (or ‘sandwich detector’), which is comprised of a thin top scintillator that is more sensitive to low energy photons and a bottom
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scintillator that absorbs the remaining photons. It is simple and effective, however suffers from poor energy separation and the noise is significantly enhanced in the projection data, which will also degrade the dual-energy image quality. In practical application, the dual-energy CT images also suffer from decomposition errors and artifacts, especially in the effective atomic number measurement. Although there have been a lot of methods researched to improve the dual-energy CT image quality, it is far from satisfaction. As we know, all the previous methods are unable to recover structure information and suppress artifacts in the effective atomic number image [13,14]. For the purpose of object segmentation and material discrimination, the CT images are usually used as a primary source, while the effective atomic number images are used as a secondary source. We propose a novel non-local image optimization method for dual-energy CT, which achieves much better results than previous ones both in noise reduction and artifact suppression. It is a postreconstruction processing method for the distribution image of effective atomic number. The reconstructed attenuation map serves as a reference image and its structure information is exacted by a non-local similarity computation scheme. The corresponding pixels in the effective atomic number image are weighted averaging with the weight coefficient derived from the reference image. Experiments with a commercial explosive detection dual-energy CT scanner validate our method. It utilizes single X-ray source, dual-layer detector configuration and acquires dualenergy projections simultaneously. The dual-energy reconstructions (attenuation image, electron density image and effective atomic image) are well matched. Using the proposed method, noise and artifacts in the effective atomic number image are effectively reduced. The image quality can meet the need for accurate material discrimination. This paper is organized as follows: First we review the dualenergy CT technique and introduce its implementations and reconstruction methods. Then, we introduced the non-local image optimization method in Section 3. Experiments and results are presented in Section 4, and we also compare our method with previous image processing methods. Finally, we present discussions and conclusions.
2. An overview of dual-energy CT 2.1. Dual-energy decomposition and reconstruction techniques The basic principles of dual-energy imaging are quite simple and well understood. It scans objects with low and high X-ray spectra, respectively, and then reconstructs the effective atomic number and electron density with a properly designed decomposition model. One of the widely used decomposition models is based on Compton scatter and the photoelectric effect. In the photon energy range most used in explosive detection, interactions between X-rays and scanned materials are dominated by Compton scatter and the photoelectric effect [15]. The Compton scatter and the photoelectric effect are both material and energy dependent; and each of them is modeled as the product of a material dependent coefficient and an energy dependent term. Another frequently used decomposition method is based on two basis materials, which assumes that the attenuation of materials can be expressed as a linear combination of two main functions, photoelectric and Compton functions or the attenuation of the two basis materials [16]. The decomposition computation can be conducted both in the projection domain (pre-reconstruction methods) or the image domain (post-reconstruction methods). Explosive detection dual-energy CT systems utilize X-ray tubes as radiation source, which have a broad spectrum. Generally
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speaking, the projection domain decomposition approach can avoid beam-hardening effects. In this case, most of the commercial systems use pre-reconstruction decomposition methods to obtain the distribution of effective atomic number and electron density of the scanned object. In this paper, all the experiments are based on basis material decomposition pre-reconstruction methods. Then we will make a brief introduction to the effective atomic number and electron density computation steps. As we have discussed above, the total mass attenuation coefficient of an object containing two constituent elements can be approximately expressed as a linear combination of two basis element's mass attenuation coefficients, as μðEÞ ¼ b1 μ1 ðEÞ þ b2 μ2 ðEÞ
ð1Þ
where μ1 ðEÞ and μ2 ðEÞ are the attenuation of the two basis materials under X-ray energy E, b1 and b2 are the decomposition coefficients of the two basis materials. In a conventional X-ray CT system, the primary spectrum from the X-ray tube is polychromatic and detectors integrate over energy. Suppose the lowenergy and high-energy projection data are p1 and p2, the dualenergy CT projection function is formulated as: Z Z ! ð2Þ p1 ¼ −ln D1 ðEÞexp ½− μðE; r ÞdldE Z p2 ¼ −ln
Z D2 ðEÞexp ½−
! μðE; r ÞdldE
ð3Þ
! here, μðE; r Þ is the linear attenuation coefficient under X-ray energy E. DðEÞ is the number of emitted photons in energy unit E, however in practical application it should be times by the energy response function of the detector. From Eq.(1), the dualenergy CT projection function can be rewritten as: Z Z p1 ¼ −ln D1 ðEÞexp ½− ½b1 μ1 ðEÞ þ b2 μ2 ðEÞdldE ð4Þ Z p2 ¼ −ln
Z D2 ðEÞexp ½−
½b1 μ1 ðEÞ þ b2 μ2 ðEÞdldE
ð5Þ
Solving this Eqs. (4) and (5), we can obtain the two basis material decomposition coefficient b1 and b2. However, it is not trivial because of the energy dependence of the attenuation and the polychromatic X-ray spectrum. Hence, a look-up table (LUT) method is always utilized to provide accurate results. After obtaining the two decomposition coefficients, the effective atomic number Z ef f and electron density ρe can be computed as: " # 3:5 1=3:5 b1 ρe1 Z 3:5 1 þ b2 ρe2 Z 2 ð6Þ Z ef f ¼ b1 ρe1 þ b2 ρe2 ρe ¼ b1 ρe1 þ b2 ρe2
ð7Þ
where ρe1 and ρe2 are the electron density values of the two basis materials, Z1 and Z2 are the effective atomic number of the two basis materials, respectively. Reconstructed Z ef f and ρe provide more information than attenuation coefficient in material discrimination. As we have discussed above, different images can be acquired simultaneously per dual-energy reconstruction, including highenergy and low-energy attenuation images, effective atomic number image and electron density image. The value distributions in the images are quite different because they represent different physical meanings. However, they should have strictly the same structures. In the sandwich detector dual-energy CT system, all the images are well registered. In Fig. 1, the four reconstructions from a dual-energy CT system are shown, in which (a) and (b) are the low-energy and high-energy attenuation coefficient images, respectively, (c) is the reconstructed effective atomic number image, and (d) is the distribution image of the electron density.
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Fig. 1. Reconstructed images from a sandwich detector dual-energy CT. (a) and (b) are the linear attenuation coefficient maps under the two spectra energy. (c) is the reconstructed images of effective atomic number and (d) is the distribution of electron density.
Fig. 2. Schematic diagram of the proposed reference based non-local image optimization strategy. (a) is the low-energy attenuation image from dual-energy CT. It serves as reference image in the proposed method. The weight coefficients are computed in this image to establish a relationship between different pixels; (b) is the effective atomic number image to be restored. Pixels in the corresponding position are weighted averaging with the calculated coefficients.
2.2. Artifacts and errors in dual-energy reconstruction The existing dual energy techniques are not accurate enough in material discrimination and the image quality is far from satisfaction. Ying et al. [17] has researched the limitations and problems of explosive detection dual-energy CT, including polynomial approximation errors, boundary constraints caused by truncation, X-ray spectral drift and so on. They will significantly degrade the image
quality and deteriorate the performance of material discrimination and explosive detection. In additional, the nonlinear dual energy decomposition also amplifies noise, so a small amount of scatter and noise may cause severe artifacts, such as a cupping effect and streaks artifacts. Given our experiences, the computation of effective atomic number is much more sensitive than electron density. Among the four reconstructed images from dual-energy CT, attenuation
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coefficient maps and electron density image obviously have higher SNR and better image quality, and the structure and detail information are well preserved. In the effective atomic number image (Fig. 1(c)), there are also some singularities and structure errors besides noise. The edge of the central cylinder phantom cannot be distinguished clearly. Thus, in commercial dual-energy CT system, image optimization and processing are necessary.
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image. It is also worth mentioning that the statistic value accuracy is not impacted by this method, because of the normalized weighted average is only a numerical redistribution step. The image singularities and structure errors are smoothed by the weighted processing, however, the mean value in a selected region maintains the same. In implementation the weight calculation area can be limited to a smaller window instead of the whole image for computational aspects.
3. Materials and methods In order to optimize the effective atomic number image, the structure information of attenuation image should be quite helpful as they have strictly the same edge and represent the same feature. Thus we adopt the attenuation map as a reference image and restore the effective atomic number image. The proposed method is motivated by non-local means which achieve state-ofthe-art in image noise reduction [18]. We design a non-local pixel similarity searching and weighted averaging strategy to recover the structure information and reduce the noise with the help of a high-quality reference image. This method is based on an assumption that if a pixel is similar with another pixel in the reference image, it should be similar with the pixel in corresponding position in the current image to be restored. The reference image is expressed as I ref , while the target image is expressed as I. The two images should be strictly registered. For a selected pixel i in the target image I, the pixel value is μðjÞ. Compute the weight function wði; jÞ in I ref pixel by pixel. The weight function is based on the similarity between pixel i and j, which is measured by the distance between two n n blocks Ni and Nj, whose centers locate at pixel i and j, respectively. The distance dði; jÞ between pixel i and j can be computed by a Gaussian-weighted Euclidean distance between the two blocks Ni and Nj: dði; jÞ ¼ ∥NðjÞ−NðiÞ∥22;a
4. Experiments and results 4.1. Experimental system We use a commercial explosive detection dual-energy CT developed by Nuctech Corporation. It is equipped with a duallayer detector acquires single X-ray source CT data using two scintillation layers on top of each other with which two energy datasets are acquired simultaneously (as shown in Fig. 3). In this system, the X-ray tube works at a high voltage of 160 kV. An external copper filter is inserted into the two detector layers to generate low and high energy spectra, respectively. A GOS detector array with 736 units is arranged on an arc with post-patient tungsten collimators to stop scattering. The top layer encountered by the X-ray photons absorbs most of the low-energy X-ray, while the bottom detector layer absorbs the high-energy X-ray photons. Low-energy and high-energy projection data are acquired separately from the two layers. In the dual-layer detector system, the scattered X-ray is mainly from the shielding, collimator and copper filter. It is a type of noise signal getting into the detector, and it does not have the image information of object. Experiment results show that the dual-energy reconstruction image quality is not obviously degraded by the scattered photon.
ð8Þ
In Eq. (8), a is the standard deviation of the Gaussian function. Based on the pixel distance, the weight coefficient can be formulated as: ! ∥NðjÞ−NðiÞ∥22;a dði; jÞ ¼ exp − wði; jÞ ¼ exp − 2 ð9Þ 2 h h where h is a smooth parameter. It depends on the noise variance in the reference image I ref and can be experimentally determined. For all the pixels in I ref , compute the weight coefficients between the selected pixel i and the other pixels. Then, in the target image to be restored average all the pixels use the weight coefficient in the corresponding pixel in the reference image. The final pixel value RðμðiÞÞafter optimization can be expressed as follows:
Fig. 3. A schematic illustration of the dual-layer detection system. Only a few detector elements in the CT system are shown here.
0.1
∑ wði; jÞμðjÞ RðμðiÞÞ ¼
j∈I ref
ð10Þ
0.08
In Eq. (10), the weight coefficient wði; jÞ is computed between pixels i and j in the reference image I ref , while the weighted average step is implemented in the target image to be optimized. ∑j∈Iref wði; jÞ is used as a normalizing factor to maintain the value accuracy in the final optimized image. When the reference image is the same with the target image, this image processing method falls back to the non-local means denoising method. The non-local searching and weighted average scheme is shown in Fig. 2. In dual-energy CT, the attenuation reconstruction can be served as reference image as it has better image quality and contrast resolution. The structures and features are strictly the same between the attenuation image and effective atomic number
0.06
∑ wði; jÞ j∈I ref
0.04
0.02
0
0
20
40
60
80
100
120
140
160
Fig. 4. The low-energy and high-energy spectrums used in the dual-layer CT system. They are generated by MCNP simulation.
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Fig. 5. Image comparison between the original reconstructed image and the processed image using the proposed method. (a) is the original effective atomic number image from the dual-energy CT system and (b) is the image after processing. The display greyscale is [0, 7].
Fig. 6. The value comparison along the profile shown in the left image.
Fig. 7. The effective atomic number image of a suitcase with a cylinder phantom in it. Metal artifact is severe in this image. (a) is the original image from the dual-energy CT reconstruction and (b) is the result after restoration. The value along the profiles will be compared in Fig. 7.
The initial low and high energy spectrum used in this system is estimated by MCNP simulations, as presented in Fig. 4. We must state that, the single source dual-layer detection system has its disadvantage. In order to make the energy separation appropriate for material discrimination, the high-spectra is significantly harder than the other dual-energy methods (such as dual kVp method).
In this condition, the detected signal in the bottom layer is relatively low, which will lead to a lower SNR in the high-energy reconstruction image. In this system the copper filter thickness was optimized for material discrimination in safety check. After dual-energy projection acquisition, electron density and effective atomic number are reconstructed with a basis material decomposition
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model in projection domain, which can avoid the beam-hardening effects. The X-ray spectra which govern the projection functions are estimated from transmission measurements, following the method introduced in Ref. [19]. After projection acquisition, dual-energy projection transformation is realized through the basis material model and the estimated spectra. A general fan-beam filtered back projection (FBP) algorithm is used to reconstruct the distribution of attenuation coefficients μðEL Þ and μðEH Þ at the two used X-ray spectra, effective atomic number Z ef f and electron density ρe . In this study, we use carbon and aluminum as basis materials in the decomposition, because most of the explosive material densities locate between the two basis materials.
Fig. 8. The value comparison along the profile presented in Fig. 7.
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The parameters in the proposed method are optimized by experiments. The smooth coefficient h is the most important one, which greatly impacts the restoration result. It depends on the noise variance in the reference image (in our study the low-energy attenuation map serves as reference image). The block dimension in the similarity computation is fixed to 5 5. And the searching window is limited to a 9 9 neighborhood square area to make the algorithm more efficient. 4.2. Experiment results A phantom consisting of three cylinders is scanned using the dual-layer detector CT system. Fig. 5(a) is the reconstructed effective atomic number image without any processing. It obviously suffers from severe noise and artifact. Fig. 5(b) is the restored image using the proposed strategy. Different materials and phantoms are well discriminated and the noise is significantly reduced. Most of the visible artifacts disappear after processing, and the SNR is improved more than 20 times than the original reconstruction. For quantitative validation, we compare the original image, optimized image with the ground-truth, as shown in Fig. 6. In the central cylinder object, the container wall and the containing items are made of different materials, and have different effective atomic numbers. In the original image it is difficult to be separated due to severe noise and artifacts, while after image optimization the two parts present clear contrast. In order to further study the proposed method, we use a suitcase which is always scanned in a luggage screening system to evaluate this method. It is more complicated than the previous
Fig. 9. Dual-energy CT imaging experiment using a real baggage. (a) is the DR image of the scanned baggage. The dashed line indicates the plane of the reconstruction. (b) and (c) are the low-energy and high-energy attenuation reconstruction images, respectively. The low-energy attenuation image serves as the reference image in the proposed method. (d) is the electron density map reconstructed using dual-energy decomposition method; (d) is original effective atomic number image without any processing; (e) is the effective atomic number image processed by the proposed method. The display greyscale of effective atomic number images is [0, 20], and that of the reconstructed attenuation map is [0, 0.3].
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Fig. 10. Comparison of the results using different methods. (a) is the low-energy attenuation image directly reconstructed from the projection data. It serves as the reference image in the proposed image optimization method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed method. The display greyscale of effective atomic number images is [0, 7], and that of the attenuation map is [0, 0.03].
phantoms. In this case, the reconstructions are also degraded by severe metal artifacts. The effective atomic number images before and after processing are compared in Fig. 7(a) and (b). It can be found that the image quality is much better with this strategy and the structure is well recovered from the noise and artifacts. The value accuracy after image optimization processing is also improved comparing with the ground-truth of the scanned phantom, as shown in Fig. 8. A more complex scan situation was tested, including many other materials with different densities, shapes and volumes. The dataset comes from a practical dual-energy CT scanning, and the reconstructions are shown in Fig. 9.
remain in the restored image. Our proposed methods performs superior than the previous methods in both noise reduction and artifact suppress, and the visual quality is significantly improved. In the partial enlarged image shown in Fig. 12 we can find that the structure is well recovered with the help of low-energy attenuation reference image. The container wall and items inside are clearly distinguished.
4.3. Comparison with previous methods
5.1. Why this algorithm works?
As we have introduced, the effective number image from duallayer CT suffers from severe artifact and errors besides noise. One of the advantages of our method is that: it uses the non-local structure information from a high-quality reference image (attenuation image in dual-energy CT), which is helpful for structure recovery and artifact reduction. However, the other post-reconstruction processing methods are both performed in the target image itself, and can only suppress the noise level. In order to further validate the proposed method, experiments were conducted using two other image optimization methods for comparison, including the total variation minimization (TVM) method and non-local means (NLM) method [18,20]. They are representative and popular in image noise reduction. The parameters in TVM and NLM methods are determined experimentally, to achieve the best compromise between visual quality and spatial resolution. The result comparisons are shown in Figs. 10 and 11, with different scanning objects. As expected, they can suppress the noise to certain extent, while the artifacts and structure errors still
The proposed image optimization method is a postreconstruction processing method for dual-energy CT reconstructions. It establishes a relationship between different images acquired simultaneously, based on the knowledge that all the reconstructed images should have strictly same structure and be precisely registered. The structure information is extracted using a non-local searching method from the high-quality attenuation coefficient image. Then average the effective atomic number with the calculated weight coefficients in different pixels. This method preserves the value accuracy due to the normalization weight, however recovers the structure information and significantly improves the visual quality, which is useful for accurate material discrimination. It differs from previous image restoration methods in that: this method provides a novel idea to extract the image structure information and establishes a relationship between the dual-energy reconstructions using a non-local searching method. The reference image plays an important role in the proposed method.
5. Discussion
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Fig. 11. Comparison of the results using different methods with a suitcase scanned by commercial dual-energy CT. (a) is the low-energy attenuation image directly reconstructed from the projection data. It serves as the reference image in the proposed method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed method. The display greyscale of effective atomic number images is [0, 20], and that of the attenuation map is [0, 0.05].
However, this method should be carefully evaluated in some certain conditions. As the attenuation image and effective atomic number image have different physical meanings, there is no linear relationship between them. Sometimes a single energy attenuation map is unable to distinguish different materials. In this condition the attenuation map cannot serve as the reference image or it will lead to errors in the effective atomic number image using this method. But in our experiments, this method still works when we choose a proper reference image in the two reconstructed attenuation images. If the low-energy attenuation image is not suitable as reference, we can change it to the high-energy attenuation image. 5.2. Algorithm acceleration This method has been demonstrated state of the art in artifact reduction and structure recovery. However, the searching scheme is relatively slow than the previous local image processing methods. The high computational complexity is due to the cost of weights calculation for all pixels in the image. In our study, it takes about 20 s in processing a 512 512 image without any acceleration. There are two main ways to address computational time: the decrease of computations performed and the improvement of the implementation. A simple way to deal with the problem of the computational time is to share the operations on several processors via a cluster or a grid. The image can be divided into a series of sub-images, each of them being treated separately by one processor. Another way is using pixel pre-classification method, to reduce the number of pixels taken into account in the weighted average. The similar acceleration method has been applied in
medical image noise reduction [21]. After acceleration and algorithm optimization, the computation time can be reduced to 0.5 s in a personal computer system, which can satisfy the practical application in commercial systems.
6. Conclusion A novel non-local method for dual-energy CT image optimization was introduced in this paper. It uses the structure information from a high-quality reference image (here we use the attenuation coefficient image) and we design a non-local weighted average scheme for the effective atomic number image. Using this method, the image quality is significantly improved, which is quite useful and important for material discrimination. This strategy is simple but robust, and the relationship of structure information between different images is established. Experiments from a commercial dual-energy CT scanner have been conducted and the results demonstrate its state-of-the-art performance in dual-energy image processing. The effective atomic number image is significantly improved both in visual quality and noise reduction, which can lower the false alarm rate and improve detection accuracy. It should be stated that, this reference image based non-local processing method can also be extended to the other CT imaging applications, such as metal artifacts reduction, under-sampling artifacts reduction, et al., only when there is a structure matched high-quality reference image.
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Fig. 12. The partial enlarged images of Fig. 11. A cylinder phantom is scanned in the draw-box and the reconstruction is degraded by metal artifacts. (a) is the low-energy attenuation image directly reconstructed from the low-energy projection data. It serves as the reference image in the proposed method; (b) is the original image of effective atomic number without any processing; (c) is the processed image with total variation minimization method; (d) is the processed image with non-local means denoising method; (e) is the result using the proposed image optimization method. The display greyscale of effective atomic number images is [0, 20], and that of the attenuation map is [0, 0.05].
Acknowledgements This work is partly supported by National Key Technology R&D Program of the Ministry of Science and Technology (No. 2012BAI07B05), China. References [1] [2] [3] [4] [5] [6]
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