Research Article  Open Access
Ming Chen, Gang Li, Weiwei Qi, Jing Zou, Yongguo Zheng, "Reconstruction Method for Nonconcyclic DualSource Circular ConeBeam CT with a Large Field of View", Mathematical Problems in Engineering, vol. 2013, Article ID 526247, 7 pages, 2013. https://doi.org/10.1155/2013/526247
Reconstruction Method for Nonconcyclic DualSource Circular ConeBeam CT with a Large Field of View
Abstract
In industrial computed tomography (CT), it is often required to inspect large objects whose size is beyond a reconstructed field of view (FOV). Some multiscan modes have been developed to acquire the complete CT projection data for a larger object using small panel detectors. In this paper, we give a nonconcyclic dualsource circular conebeam scanning geometry based on the idea of multiscan modes and propose a backprojectionfiltrationbased (BPF) reconstruction algorithm without data rebinning. Since the FOV calculated according to this nonconcyclic dualsource circular CT scanning geometry is larger than cardiac dualsource CT scanning geometry, our method can reconstruct larger horizontal slices (i.e., the slices perpendicular to rotation axis) than cardiac dualsource CT. The quality of CT images is expected to be superior to those obtained using larger panel detectors. The simulation results have indicated that CT images obtained by the proposed method are satisfying.
1. Introduction
In industrial CT, it is often required to inspect large objects whose size is beyond a field of view (FOV) calculated according to CT scanning geometry, where the maximum horizontal (i.e., perpendicular to rotation axis) FOV is usually fixed in CT system. When a slice of the inspected object cannot be completely covered within the FOV of the cone beam, complete projection data passing through the object cannot be obtained. However, conventional CT reconstruction algorithm, for example, filtered backprojection (FBP), requires the complete projection data. Therefore, those conventional scan modes and reconstruction methods can not be used. To solve this problem, several multiscan modes have been developed to acquire the complete projection data. Of them, the rotationtranslationtranslation (RTT) multiscan mode [1, 2] and rotationtranslation (RT) multiscan mode [3, 4] are typical. In fact, RT and RTT multiscan modes not only acquire completed projection data, but can also reduce the differences of the flux intensities detected by different detector cells.
For fanbeam RT multiscan modes, the reconstruction algorithm based on backprojection filtration (BPF) was proposed by Chen et al. [5, 6], which can exactly reconstruct the images and does not introduce data rebinning. BPF algorithm, as the basis of our work, was first proposed by Zou and Pan [7] to exactly reconstruct the images for helical cone beam CT. After that, an explicit BPF formula for 2D image reconstruction was proposed by Noo et al. [8] using the finite inversion formula of Hilbert transform.
In this work, we give a nonconcyclic dualsource circular cone beam scanning geometry based on the idea of RT multiscan scan mode and propose a BPFbased reconstruction formula. Since The FOV calculated according to this nonconcyclic dualsource circular CT scanning geometry is larger than cardiac dualsource CT scanning geometry, our method can reconstruct larger horizontal slices. The quality of CT images reconstructed is expected to be superior to those obtained using larger panel detectors. The simulation results have indicated that CT images obtained by the proposed algorithm are satisfying. Since we make use of the approximate idea of Feldkamp (FDK) algorithm [9] in reconstructing formula deduction, reconstruction images are satisfying for a small cone angle, which are usually better for static objects. For dynamic process, there are some results in [10, 11].
2. Nonconcyclic DualSource Circular Scan Mode with a Larger FOV
In this section, we give a nonconcyclic dualsource circular cone beam scan mode with a larger FOV based on RT dualscan modes.
As shown in Figure 1, there are two pairs of Xray sources and panel detectors. Let and denote the focuses of Xray sources. Let and denote panel detectors. In the nonconcyclic dualsource circular scan mode, two pairs of Xray sources and panel detectors are fixed, and the inspected object is placed on the turntable. Now, we give this nonconcyclic dualsource scanning geometry. Firstly, the center of the turntable is located in the cone beam formed by and . For some large inspected objects, one cone beam formed by and cannot completely cover its horizontal slice. So, secondly, we set and making , and , located at the same side of , and two pairs of cone beams are parallel, as shown in Figure 1. Now, we give some relevant parameters to describe this scanning geometry. Let denote the distance from to (equal to the distance from to ). Let denote the distance from to the virtual panel detectors which is through and parallel to panel detectors . Two pairs of panel detectors have the same size, where and denote the length and width of the panel detectors, respectively. To obtain sufficient projection data, the distance from to must be less than [6]. According to the location of the three points , , and , we know that distance from to is larger than the distance from to . So two Xray sources are nonconcyclic in this scanning geometry. The maximum horizontal FOV is in nonconcyclic scanning geometry [6], where is the distance from collimator of the detector to the crystal of the detector.
3. Reconstruction Formula
For the nonconcyclic dualsource circular scan mode above, we give the reconstruction formula in this section. We know that the key point to BPF algorithm is to obtain the Hilbert image from projections along some directions. The problem we face is how to weigh each set of the derivatives of two pairs of projection data to obtain two differentiated backprojection (DBP) images, and how to merge them into an entire DBP image that is related to the Hilbert image.
For the deduction of the reconstruction formula, we need give some denotations for the scan mode in Section 2. As shown in Figure 2, we establish 3D Cartesian coordinate system , where axis is the rotating axis. Like the deduction of many CT reconstruction formulas, we also adopt two virtual panel detectors whose centers are and , where the three points , , and are collinear. Let denote the projection point of to the upper side line of two virtual panel detectors, as shown in Figure 2. We need to establish rotating coordinate system in the deduction, and let denote the rotating angle that is a clockwise angle from axis to axis. We set the 2D coordinate system () in two virtual panel detectors, where the direction vector of in coordinate system is , and the direction vector of is the same direction with axis.
Let denote the density function of the inspected object where is a reconstructed point on the inspected object. Let denote the DBP image of , where is an angle measured from the axis anticlockwise in the plane . Let denote the projection data under Xray source , and denote the DBP image from . Let denote the distance between the points and , and must satisfy and [6] that indicates that the projection area of the inspected objects under two Xray sources is some what overlap. Let denote the projection address of in system under Xray source . Let above.
Now, using the idea of FDK algorithm, we deduct the formula of DBP image from the fanbeam DBP formula for RT multiscan modes [5, 6]. The formula deduction is divided into two kinds of situations: (i) is on the middle plane where ; (ii) is on the offmiddle plane where .
(i) If is on the middle plane, we may directly obtain the cone beam DBP image from the fanbeam DBP [6] as follows: where where where is a small positive number that is determined by the projection overlapping area, and is a mollification kernel function as follows:
(ii) If is on an offmiddle plane, for obtaining we need to define the oblique surface which is through the points , , and . Let denote the intersection point between axis and the oblique surface, and we know from Figure 2 above. Now, we establish the coordinate system and give the relevant parameters in the oblique surface. Let denote the coordinate system in the oblique surface, where , is the same direction with the vectors and , respectively. Let denote a vector from to and an angle variable in the oblique surface, respectively, and denote the distance from to in the oblique surface.
We know that Xray source is regarded as a fulcrum in FDK algorithm, and an offmiddle plane is approximately obtained by inclining the middle plane. Now, based on the idea of FDK algorithm, we give the steps of deduction of in the offmiddle plane as follows: (i) writing using the variables in the oblique surface; (ii) finding the relation between and , where is the rotation angle increment in the middle plane, and is the rotation angle increment in the oblique plane; (iii) finding the relation between and ; (iv) calculating ; (v) obtaining by accumulating all for the angle variable .
Without loss of generality, we give the deduction of referring to the steps above. From the formula (2), making use of the parameters in the oblique surface , we obtain. where . Obviously, is , when .
We can obtain the relation between and from Figure 2 as and the relation between and as Since is in the plane , we can obtain We easily calculate the distance from to the detectors in the oblique surface, Now, we substitute formulas (7), (8), (9), and (10) to (6) and obtain by accumulating all for the angle variable as where Similarly, we can obtain where where . When , the formulas (11) and (13) are (2) and (3), respectively.
We need two steps for reconstructing . Firstly, we need to obtain the Hilbert image of each layer according to the reference [8]. Using the relation of the Hilbert image and the DBP image, we can obtain from the formulas (1), (2), (3), (11), and (13), where is an arbitrary point on the inspected object. Secondly, making use of the virtual trajectories and virtual PIlines in [12, 13], we can obtain the following formula based on the finite inversion formula of Hilbert transform: where , , and the constants , , and relate to and . We can obtain from the integral of along the virtual PIlines [14]. However, since the PIlines are virtual except the middle plane, we cannot obtain an accurate . So the proposed reconstruction formula is approximated on the offmiddle planes.
4. Numerical Experiments
To validate our algorithm, we perform some numerical experiments with simulated data in this section. 3D SheppLogan phantom is used to get the simulated data. The parameters of the phantom in 3D Cartesian coordinate system are listed in Table 1, where is the center coordinate of an ellipsoid, and three variables and denote the length of three half axis of an ellipsoid, and is the rotating angle of an ellipsoid in the plane . The parameters of two pairs of Xray sources and panel detectors in the scanning geometry are the same as follows: , , and the length and width of the panel detectors , which is composed of 257 × 257 detector cells.

According to the parameters above, we can calculate the horizontal diameter of the maximum FOV formed by a set of Xray source and panel detector is 60.82 mm. From the parameters of 3D SheppLogan phantom in Table 1, we calculate the longest axis of ellipsoid along axis to be 97.9 mm which is greater than 60.82 mm. So we cannot reconstruct its CT image using Cardiac dualsource CT scanning geometry. For acquiring the complete CT data of the 3D SheppLogan phantom, we adapt the nonconcyclic dualsource circular cone beam scanning geometry with a larger FOV in Section 2. In this scanning geometry, we set and which satisfie the conditions above. Each panel detector takes 720 projections within the angle range from 0 to . Two digital radiography (DR) images under the 100th projection angle are shown in Figure 3. In CT image reconstruction, we use as the direction of Hilbert transform, and . We reconstruct the CT images from the formulas (15) and (16). Threeimage matrix of a horizontal slice is , as shown in Figure 4. Twoimage matrix of a slice perpendicular to axis is , as shown in Figure 5. Twoimage matrix of a slice perpendicular to axis is , as shown in Figure 6. For two threedimensional projection data whose size is , CT image reconstruction takes about 1200 seconds by using CPU and 17.778 seconds by using GPU, where the size of CT image is .
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5. Conclusion
In this work we give nonconcyclic dualsource circular cone beam scan mode with a larger FOV, and deduce the reconstruction formula. Using two pairs of projections obtained from this nonconcyclic dualsource scanning geometry, the FOV is enlarged effectively in the same equipment condition without data rebinning. It is because of the fact that for the real CT system, the flux output from Xray source is not isotropic, and then the data acquired in this scanning mode using small panel detectors are relative to more uniform intensity of the flux output than a large one. The experiment confirmed that our reconstruction method is effective, when a cone angle is small.
Acknowledgments
This work was supported in part by three grants from the National Natural Science Foundation of China (61201430, 61002041, and 61201431), International Scientific and Technological Cooperation Program of Shenzhen (Grant JC201105190923A), China Postdoctoral Science Foundation and Shandong province Postdoctoral Innovation Foundation.
References
 E. A. Sivers, “Use of multiple CT scans to accommodate large objects and stretch dynamic range of detectability,” Nuclear Instruments and Methods in Physics B, vol. 99, pp. 761–764, 1995. View at: Publisher Site  Google Scholar
 E. A. Sivers, W. A. Snyder, S. A. Ellingson et al., “CT Multiscan: using small area detectors to image large components,” Journal of Engineering for Gas Turbines and Power, vol. 118, pp. 711–716, 1996. View at: Publisher Site  Google Scholar
 Y. Q. Sun, Researches on several application problems in industrial CT [M.S. thesis], Capital Normal University, Beijing, China, 2006.
 F. Zhao, H. N. Lu, and C. L. Sun, “New scan mode for 2DCT and its reconstruction algorithm,” Optech, vol. 32, pp. 284–289, 2006. View at: Google Scholar
 M. Chen, H. T. Zhang, and P. Zhang, “BPFbased reconstruction algorithm for multiple rotation–translation scan mode,” Progress in Natural Science, vol. 18, pp. 209–216, 2008. View at: Publisher Site  Google Scholar
 M. Chen, H. T. Zhang, D. F. Chen, and P. Zhang, “Reconstruction algorithm for unilateral offcentered rotation translation multiscans,” NDT, China, vol. 31, pp. 29–34, 2009. View at: Google Scholar
 Y. Zou and X. Pan, “Exact image reconstruction on PIlines from minimum data in helical conebeam CT,” Physics in Medicine and Biology, vol. 49, pp. 941–959, 2004. View at: Publisher Site  Google Scholar
 F. Noo, R. Clackdoyle, and J. D. Pack, “A twostep Hilbert transform method for 2D image reconstruction,” Physics in Medicine and Biology, vol. 49, pp. 3903–3923, 2004. View at: Publisher Site  Google Scholar
 L. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical conebeam algorithm,” Journal of the Optical Society of America A, vol. 1, pp. 612–619, 1984. View at: Publisher Site  Google Scholar
 Y. W. Zhang, T. Y. Chai, Z. M. Li, and C. Y. Yang, “Modeling and monitoring of dynamic processes,” IEEE Transactions on Neural Networks and Learning System, vol. 23, pp. 277–284, 2012. View at: Publisher Site  Google Scholar
 Y. W. Zhang, H. Zhou, S. J. Qin, and T. Y. Chai, “Decentralized fault diagnosis of largescale processes using multiblock kernel partial least squares,” IEEE Transactions on Industrial Informatics, vol. 6, no. 1, pp. 3–10, 2010. View at: Publisher Site  Google Scholar
 Y. Zou, X. Pan, and E. Y. Sidky, “Image reconstruction in regionsofinterest from truncated projections in a reduced fanbeam scan,” Physics in Medicine and Biology, vol. 50, pp. 13–27, 2005. View at: Publisher Site  Google Scholar
 L. Yu, Y. Zou, E. Y. Sidky, C. A. Pelizzari, P. Munro, and X. Pan, “Region of interest reconstruction from truncated data in circular conebeam CT,” IEEE Transactions on Medical Imaging, vol. 25, no. 7, pp. 869–881, 2006. View at: Publisher Site  Google Scholar
 Y. Sidky Emil, Y. Zou, and X. Pan, “Minimum data image reconstruction algorithms with shiftinvariant filtering for helical, conebeam CT,” Physics in Medicine and Biology, vol. 50, pp. 1643–1657, 2005. View at: Publisher Site  Google Scholar
Copyright
Copyright © 2013 Ming Chen et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.