全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

Evaluation of Algebraic Iterative Image Reconstruction Methods for Tetrahedron Beam Computed Tomography Systems

DOI: 10.1155/2013/609704

Full-Text   Cite this paper   Add to My Lib

Abstract:

Tetrahedron beam computed tomography (TBCT) performs volumetric imaging using a stack of fan beams generated by a multiple pixel X-ray source. While the TBCT system was designed to overcome the scatter and detector issues faced by cone beam computed tomography (CBCT), it still suffers the same large cone angle artifacts as CBCT due to the use of approximate reconstruction algorithms. It has been shown that iterative reconstruction algorithms are better able to model irregular system geometries and that algebraic iterative algorithms in particular have been able to reduce cone artifacts appearing at large cone angles. In this paper, the SART algorithm is modified for the use with the different TBCT geometries and is tested using both simulated projection data and data acquired using the TBCT benchtop system. The modified SART reconstruction algorithms were able to mitigate the effects of using data generated at large cone angles and were also able to reconstruct CT images without the introduction of artifacts due to either the longitudinal or transverse truncation in the data sets. Algebraic iterative reconstruction can be especially useful for dual-source dual-detector TBCT, wherein the cone angle is the largest in the center of the field of view. 1. Introduction Image-guided radiation therapy (IGRT) is essential to ensure proper dose delivery to the target while sparing the surrounding tissue [1, 2]. Cone beam CT (CBCT) is a popular online imaging modality used for LINAC-based IGRT [3, 4]. Although CBCT is convenient to use, the performance of CBCT systems is less than ideal. The image quality for the CBCT is significantly degraded due to excessive scattered photons [5–8] as well as suboptimal performance of the flat panel detector [9]. These issues limit the use of CBCT for certain advanced radiation therapy techniques such as online adaptive radiotherapy [8, 10]. It is also well known that at large cone angles, there are artifacts caused by using approximate reconstruction methods that appear in CBCT reconstructions [11], but this issue has largely been ignored in IGRT because the scatter and detector issues are the dominant factors in the degradation of CBCT image quality. Tetrahedron beam computed tomography (TBCT) is a novel volumetric CT modality that overcomes the scatter and detector problems of CBCT [12, 13]. A TBCT system is composed of a minimum of one linear source array with one linear detector array positioned opposite and orthogonal to it. In TBCT, scattered photons are largely rejected due to the fan-beam geometry of the system. A TBCT

References

[1]  M. W. K. Kan, L. H. T. Leung, W. Wong, and N. Lam, “Radiation dose from cone beam computed tomography for image-guided radiation therapy,” International Journal of Radiation Oncology Biology Physics, vol. 70, no. 1, pp. 272–279, 2008.
[2]  J. A. Purdy, “Dose to normal tissues outside the radiation therapy patient's treated volume: a review of different radiation therapy techniques,” Health Physics, vol. 95, no. 5, pp. 666–676, 2008.
[3]  R. R. Allison, H. A. Gay, H. C. Mota, and C. H. Sibata, “Image-guided radiation therapy: current and future directions,” Future Oncology, vol. 2, no. 4, pp. 477–492, 2006.
[4]  D. A. Jaffray, J. H. Siewerdsen, J. W. Wong, and A. A. Martinez, “Flat-panel cone-beam computed tomography for image-guided radiation therapy,” International Journal of Radiation Oncology Biology Physics, vol. 53, no. 5, pp. 1337–1349, 2002.
[5]  H. Kanamori, N. Nakamori, K. Inoue, and E. Takenaka, “Effects of scattered X-rays on CT images,” Physics in Medicine and Biology, vol. 30, no. 3, pp. 239–249, 1985.
[6]  J. H. Siewerdsen and D. A. Jaffray, “Cone-beam computed tomography with a flat-panel imager: magnitude and effects of X-ray scatter,” Medical Physics, vol. 28, no. 2, pp. 220–231, 2001.
[7]  R. Ning, X. Tang, and D. Conover, “X-ray scatter correction algorithm for cone beam CT imaging,” Medical Physics, vol. 31, no. 5, pp. 1195–1202, 2004.
[8]  L. Zhu, Y. Xie, J. Wang, and L. Xing, “Scatter correction for cone-beam CT in radiation therapy,” Medical Physics, vol. 36, no. 6, pp. 2258–2268, 2009.
[9]  T. G. Flohr, S. Schaller, K. Stierstorfer, H. Bruder, B. M. Ohnesorge, and U. J. Schoepf, “Multi-detector row CT systems and image-reconstruction techniques,” Radiology, vol. 235, no. 3, pp. 756–773, 2005.
[10]  T. Zhang, Y. Chi, E. Meldolesi, and D. Yan, “Automatic delineation of on-line head-and-neck computed tomography images: toward on-line adaptive radiotherapy,” International Journal of Radiation Oncology Biology Physics, vol. 68, no. 2, pp. 522–530, 2007.
[11]  C. Maa?, F. Dennerlein, F. Noo, and M. Kachelrie?, “Comparing short scan CT reconstruction algorithms regarding cone-beam artifact performance,” in Proceedings of the Nuclear Science Symposium Conference Record (NSS/MIC '10), pp. 2188–2193, IEEE, November 2010.
[12]  T. Zhang, D. Schulze, X. Xu, and J. Kim, “Tetrahedron beam computed tomography (TBCT): a new design of volumetric CT system,” Physics in Medicine and Biology, vol. 54, no. 11, pp. 3365–3378, 2009.
[13]  X. Xu, J. Kim, P. Laganis, D. Schulze, Y. Liang, and T. Zhang, “A tetrahedron beam computed tomography benchtop system with a multiple pixel field emission X-ray tube,” Medical Physics, vol. 38, no. 10, pp. 5500–5509, 2011.
[14]  H. K. Tuy, “Scatter correction for cone-beam CT in radiation therapy,” SIAM Journal on Applied Mathematics, vol. 43, no. 3, pp. 546–552, 1983.
[15]  X. Tang, J. Hsieh, A. Hagiwara, R. A. Nilsen, J. B. Thibault, and E. Drapkin, “A three-dimensional weighted cone beam filtered backprojection (CB-FBP) algorithm for image reconstruction in volumetric CT under a circular source trajectory,” Physics in Medicine and Biology, vol. 50, no. 16, pp. 3889–3905, 2005.
[16]  I. A. Feldkamp, L. C. Davis, and J. W. Kress, “Practical cone-beam algorithm,” Journal of the Optical Society of America A, vol. 1, no. 6, pp. 612–619, 1984.
[17]  A. K. Hara, R. G. Paden, A. C. Silva, J. L. Kujak, H. J. Lawder, and W. Pavlicek, “Iterative reconstruction technique for reducing body radiation dose at CT: Feasibility Study,” American Journal of Roentgenology, vol. 193, no. 3, pp. 764–771, 2009.
[18]  A. C. Martinsen, H. K. Saether, P. K. Hol, D. R. Olsen, and P. Skaane, “Iterative reconstruction reduces abdominal CT dose,” European Journal of Radiology, vol. 81, no. 7, pp. 1483–1487, 2012.
[19]  J. S. Liow, S. C. Strother, K. Rehm, and D. A. Rottenberg, “Improved resolution for PET volume imaging through three-dimensional iterative reconstruction,” Journal of Nuclear Medicine, vol. 38, no. 10, pp. 1623–1631, 1997.
[20]  J. B. Thibault, K. D. Sauer, C. A. Bouman, and J. Hsieh, “A three-dimensional statistical approach to improved image quality for multislice helical CT,” Medical Physics, vol. 34, no. 11, pp. 4526–4544, 2007.
[21]  K. Mueller and R. Yagel, “Rapid 3-D cone-beam reconstruction with the simultaneous algebraic reconstruction technique (SART) using 2-D texture mapping hardware,” IEEE Transactions on Medical Imaging, vol. 19, no. 12, pp. 1227–1237, 2000.
[22]  B. Chiang, S. Nakanishi, A. A. Zamyatin, and D. Shi, “Cone beam artifact reduction in circular computed tomography,” in Proceedings of the Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC '11), pp. 4143–4144, IEEE, 2011.
[23]  R. Gordon, R. Bender, and G. T. Herman, “Algebraic Reconstruction Techniques (ART) for three-dimensional electron microscopy and X-ray photography,” Journal of Theoretical Biology, vol. 29, no. 3, pp. 471–481, 1970.
[24]  A. H. Andersen and A. C. Kak, “Simultaneous algebraic reconstruction technique (SART): a superior implementation of the art algorithm,” Ultrasonic Imaging, vol. 6, no. 1, pp. 81–94, 1984.
[25]  B. De Man and S. Basu, “Distance-driven projection and backprojection in three dimensions,” Physics in Medicine and Biology, vol. 49, no. 11, pp. 2463–2475, 2004.
[26]  M. Jiang and G. Wang, “Convergence of the Simultaneous Algebraic Reconstruction Technique (SART),” IEEE Transactions on Image Processing, vol. 12, no. 8, pp. 957–961, 2003.
[27]  H. Guan and R. Gordon, “A projection access order for speedy convergence of ART (algebraic reconstruction technique): a multilevel scheme for computed tomography,” Physics in Medicine and Biology, vol. 39, no. 11, pp. 2005–2022, 1994.
[28]  F. Xu, W. Xu, M. Jones et al., “On the efficiency of iterative ordered subset reconstruction algorithms for acceleration on GPUs,” Computer Methods and Programs in Biomedicine, vol. 98, no. 3, pp. 261–270, 2010.
[29]  W. M. Pang, J. Qin, Y. Lu, Y. Xie, C. K. Chui, and P. A. Heng, “Accelerating simultaneous algebraic reconstruction technique with motion compensation using CUDA-enabled GPU,” International Journal of Computer Assisted Radiology and Surgery, vol. 6, no. 2, pp. 187–199, 2011.
[30]  H. Guan and R. Gordon, “Computed tomography using algebraic reconstruction techniques (ARTs) with different projection access schemes: a comparison study under practical situations,” Physics in Medicine and Biology, vol. 41, no. 9, pp. 1727–1743, 1996.
[31]  A. Kak and M. Slaney, Principles of Computerized Tomographic Imaging, IEEE Press, 1988.
[32]  H. Kudo, F. Noo, and M. Defrise, “Cone-beam filtered-backprojection algorithm for truncated helical data,” Physics in Medicine and Biology, vol. 43, no. 10, pp. 2885–2909, 1998.
[33]  A. H. Andersen, “Algebraic reconstruction in CT from limited views,” IEEE Transactions on Medical Imaging, vol. 8, no. 1, pp. 50–55, 1989.
[34]  W. Zbijewski and F. J. Beekman, “Characterization and suppression of edge and aliasing artefacts in iterative X-ray CT reconstruction,” Physics in Medicine and Biology, vol. 49, no. 1, pp. 145–157, 2004.
[35]  W. Zbijewski and F. J. Beekman, “Comparison of methods for suppressing edge and aliasing artefacts in iterative X-ray CT reconstruction,” Physics in Medicine and Biology, vol. 51, no. 7, pp. 1877–1889, 2006.
[36]  S. Matej and R. M. Lewitt, “Practical considerations for 3-D image reconstruction using spherically symmetric volume elements,” IEEE Transactions on Medical Imaging, vol. 15, no. 1, pp. 68–78, 1996.
[37]  F. Xu and K. Mueller, “Accelerating popular tomographic reconstruction algorithms on commodity PC graphics hardware,” IEEE Transactions on Nuclear Science, vol. 52, no. 3, pp. 654–663, 2005.
[38]  B. Keck, H. Hofmann, H. Scherl, M. Kowarschik, and J. Hornegger, “GPU-accelerated SART reconstruction using the CUDA programming environment,” in Medical Imaging 2009: Physics of Medical Imaging, E. Samei and J. Hsieh, Eds., vol. 7258 of Proceedings of SPIE, February 2009.
[39]  C. Mora, M. J. Rodríguez-álvarez, and J. V. Romero, “New pixellation scheme for CT algebraic reconstruction to exploit matrix symmetries,” Computers and Mathematics with Applications, vol. 56, no. 3, pp. 715–726, 2008.

Full-Text

comments powered by Disqus

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133

WeChat 1538708413