Journal of Innovative Optical Health Sciences, 2017, 10 (3): 1750005, Published Online: Dec. 27, 2018   

Performance evaluation of the simplified spherical harmonics approximation for cone-beam X-ray luminescence computed tomography imaging

Author Affiliations
1 School of Information Sciences and Technology, Northwest University, Xi'an, Shannxi 710027, P. R. China
2 School of Physics and Information Technology, Shaanxi Normal University, Xi'an, Shannxi 710062, P. R. China
Abstract
As an emerging molecular imaging modality, cone-beam X-ray luminescence computed tomography (CB-XLCT) uses X-ray-excitable probes to produce near-infrared (NIR) luminescence and then reconstructs three-dimensional (3D) distribution of the probes from surface measurements. A proper photon-transportation model is critical to accuracy of XLCT. Here, we presented a systematic comparison between the common-used Monte Carlo model and simplified spherical harmonics (SPN). The performance of the two methods was evaluated over several main spectrums using a known XLCT material. We designed both a global measurement based on the cosine similarity and a locally-averaged relative error, to quantitatively assess these methods. The results show that the SP3 could reach a good balance between the modeling accuracy and computational e±ciency for all of the tested emission spectrums. Besides, the SP1 (which is equivalent to the diffusion equation (DE)) can be a reasonable alternative model for emission wavelength over 692 nm. In vivo experiment further demonstrates the reconstruction performance of the SP3 and DE. This study would provide a valuable guidance for modeling the photon-transportation in CB-XLCT.
References

[1] M. Ahmad, G. Pratx, M. Bazalova, X. Lei, “X-Ray luminescence and X-ray fluorescence computed tomography: New molecular imaging modalities, ” IEEE Access. 2 (2), 1051–1061 (2014).

[2] W. Cai, X. Chen, “Nanoplatforms for targeted molecular imaging in living subjects, ” Small 3 (11), 1840–1854 (2007).

[3] G. Pratx, C. M. Carpenter, C. Sun, L. Xing, “X-ray luminescence computed tomography via selective excitation: A feasibility study, ” IEEE Trans. Med. Imag. 29 (12), 1992–1999 (2010).

[4] X. Liu, Q. Liao, H. Wang, Z. Yan, “Excitation-resolved cone-beam X-ray luminescence tomography, ” J. Biomed. Opt. 20 (7), 70501 (2015).

[5] C. Li, M. Arnuflo, S. R. Cherry, “Numerical simulation of X-ray luminescence optical tomography for small-animal imaging, ” J. Biomed. Opt. 19 (4), 523–529 (2014).

[6] D. Chen, S. Zhu, X. Chen, T. Chao, X. Cao, F. Zhao, L. Huang, J. Liang, “Quantitative cone beam X-ray luminescence tomography/X-ray computed tomography imaging, ” Appl. Phys. Lett. 105 (19), 191104 (2014).

[7] L. Sudheendra, G. K. Das, C. Li, D. Stack, J. Cena, S. Cherry, I. M. Kennedy, “NaGdF4:Eu3 ++ nanoparticles for enhanced X-ray excited optical imaging, ” Chem. Mater. 26 (5), 1881–1888 (2014).

[8] Y. Osakada, G. Pratxd, C. Sund, M. Sakamoto, M. Ahmadd, O. Volotskovad, Q. Ongc, T. Teranishie, Y. Haradaa, L. Xing, B. Cuic, “Hard X-ray-induced optical luminescence via biomolecule-directed metal clusters, ” Chem. Commun. 50 (27), 3549–3551 (2014).

[9] X. Liu, Q. Liao, H. Wang, “Fast X-ray luminescence computed tomograph imaging, ” IEEE Trans. Biomed. Eng. 61 (6), 1621–1627 (2014).

[10] D. Wang, J. He, H. Qiao, X. Song, Y. Fan, D. Li, “High-performance fluorescence molecular tomography through shape-based reconstruction using spherical harmonics parameterization, ” PloS one 9 (4), e94317 (2014).

[11] J. Yu, J. Cheng, Y. Hou, X. He, “Sparse reconstruction for fluorescence molecular tomography via a fast iterative algorithm, ” J. Innov. Opt. Health Sci. 7 (3), 488 (2014). Link, ISI, Google Scholar

[12] X. He, J. Liang, X. Wang, J. Yu, X. Qu, X. Wang, Y. Hou, D. Chen, F. Liu, J. Tian, “Sparse reconstruction for quantitative bioluminescence tomography based on the incomplete variables truncated conjugate gradient method, ” Opt. Express. 18 (24), 24825–24841 (2010).

[13] J. Feng, K. Jia, G. Yan, S. Zhu, C. Qin, Y. Lv, J. Tian, “An optimal permissible source region strategy for multispectral bioluminescence tomography, ” Opt. Express 16 (20), 15640–15654 (2008).

[14] C. Qin, J. Feng, S. Zhu, X. Ma, J. Zhong, P. Wu, Z. Jin, J. Tian, “Recent advances in bioluminescence tomography: Methodology and system as well as application, ” Laser Photon. Rev. 8 (1), 94–114 (2014).

[15] C. Darne, Y. Lu, E. Sevick-Muraca, “Small animal fluorescence and bioluminescence tomography: A review of approaches, algorithms and technology update, ” Phys. Med. Bio. 59, R1–R64 (2014).

[16] S. Ren, X. Chen, H. Wang, X. Qu, G. Wang, J. Liang, J. Tian, “Molecular optical simulation environment (MOSE): A platform for the simulation of light propagation in turbid media, ” Plos One 8 (4), e61304 (2013).

[17] D. Yang, X. Chen, X. Cao, J. Wang, J. Liang, J. Tian, “Performance investigation of SP3 and diffusion approximation for three-dimensional whole-body optical imaging of small animals, ” Med. Bio. Eng. Comput. 53, 805–814 (2015).

[18] A. D. Klose, E. W. Larsen, “Light transport in biological tissue based on the simplified spherical harmonics equations, ” J. Comput. Phys. 220 (1), 441–470 (2006).

[19] X. He, H. Guo, J. Yu, X. Zhang, Y. Hou, “Effective and robust approach for fluorescence molecular tomography based on CoSaMP and SP3 model, ” J. Innov. Opt. Health Sci. 9 (6), 1650024 (2016). Link, ISI, Google Scholar

[20] D. Han, J. Tian, “Sparsity-promoting tomographic fluorescence imaging with simplified spherical harmonics approximation, ” IEEE Trans. Bio. Med. Eng. 57 (10), 2564–2567 (2010).

[21] A. D. Klose, B. J. Beattie, H. Dehghan, L. Vider, C. Le, V. Pronomarev, R. Blasberg, “In vivo bioluminescence tomography with a blocking-off finite-difference SP3 method and MRI/CT coregistration, ” Med. Phys. 37 (1), 329–337 (2010).

[22] D. Yang, X. Chen, Z. Peng, X. Wang, J. Ripoll, J. Wang, Liang, “Light transport in turbid media with non-scattering, low-scattering and high absorption heterogeneities based on hybrid simplified spherical harmonics with radiosity model, ” BioMed. Opt. Express 4 (10), 2209–2223 (2013).

[23] X. Chen, Q. Zhang, D. Yang, J. Liang, “Hybrid radiosity-SP3 equation based bioluminescence tomography reconstruction for turbid medium with low- and non-scattering regions, ” J. Appl. Phys. 115 (2), 024702-1-8 (2014).

[24] W. Cong, G. Wang, D. Kumar, Y. Liu, M. Jiang, L. Wang, E. Hoffman, G. Mclennan, P. Mcracy, J. Zabner, A. Cong, “Practical reconstruction method for bioluminescence tomography,” Opt. Express 13 (18), 6756–6771 (2005).

[25] Y. Deng, Z. Luo, X. Jiang, W. Xie, Q. Luo, “Accurate quantification of fluorescent targets within turbid media based on a decoupled fluorescence Monte Carlo model, ” Opt. Lett. 40 (13), 3129–3132 (2015).

[26] B. Dogdas, D. Stout, A. F. Chatziioannou, R. M. Leahy, “Digimouse: A 3D whole body mouse atlas from CT and cryosection data, ” Phys. Med. Biol. 52 (3), 577–587 (2007).

[27] G. Alexandrakis, F. Rannou, A. Chatziioannou, “Tomographic bioluminescence imaging by use of a combined optical-PET (OPET) system: A computer simulation feasibility study, ” Phys. Med. Biol. 50 (17), 4225–4241 (2005).

[28] D. Hyde, R. Schulz, D. Brooks, E. Miller, V. Ntziachristos, “Performance dependence of hybrid X-ray computed tomography/fluorescence molecular tomography on the optical forward problem, ” J. Opt. Soc. Am. A 26 (4), 919–923 (2009).

[29] L. A. Feldkamp, L. C. Davis, J. W. Kress, “Practical cone-beam algorithm, ” J. Opt. Soc. Am. A 1 (6), 612–619 (1984).

[30] H. Yi, D. Chen, W. Li, S. Zhu, X. Wang, J. Liang, J. Tian, “Reconstruction algorithms based on l1-norm and l2-norm for two imaging models of fluorescence molecular tomography: A comparative study, ” J. Biomed. Opt. 18 (5), 467–472 (2013).

[31] D. Chen, S. Zhu, H. Yi, X. Zhang, D. Chen, J. Liang, J. Tian, “Cone beam X-ray luminescence computed tomography: A feasibility study, ” Med. Phys. 40 (40), 031111 (2013).

Haibo Zhang, Guohua Geng, Yanrong Chen, Fengjun Zhao, Yuqing Hou, Huangjian Yi, Shunli Zhang, Jingjing Yu, Xiaowei He. Performance evaluation of the simplified spherical harmonics approximation for cone-beam X-ray luminescence computed tomography imaging[J]. Journal of Innovative Optical Health Sciences, 2017, 10(3): 1750005.

本文已被 1 篇论文引用
被引统计数据来源于中国光学期刊网
引用该论文: TXT   |   EndNote

相关论文

加载中...

关于本站 Cookie 的使用提示

中国光学期刊网使用基于 cookie 的技术来更好地为您提供各项服务,点击此处了解我们的隐私策略。 如您需继续使用本网站,请您授权我们使用本地 cookie 来保存部分信息。
全站搜索
您最值得信赖的光电行业旗舰网络服务平台!