|
Special Issue:
SPECIAL TOPIC — Celebrating the 100th Anniversary of Physics Discipline of Northwest University
|
| SPECIAL TOPIC—Celebrating the 100th Anniversary of Physics Discipline of Northwest University |
Prev
Next
|
|
|
Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform |
| Yun-He Zhang(张云鹤)1, Huan-Zheng Zhu(朱桓正)2, Yong-Jiang Dong(董泳江)2, Jia Zeng(曾佳)2,†, Xin-Peng Han(韩新鹏)3, Ivan A. Bratchenko4, Fu-Rong Zhang(张富荣)1, Si-Yuan Xu(许思源)1, and Shuang Wang(王爽)1,‡ |
1 Institute of Photonics and Photon-Technology, Northwest University, Xi'an, Shaanxi 710127, China;
2 Huawei Technologies Co., Ltd, Shenzhen, Guangdong 518129, China;
3 Cardiopulmonary Disease Department, Xi'an International Medical Center Hospital, Xi'an 710100, China;
4 Laser and Biotechnical Systems Department, Samara National Research University, Samara 443086, Russia |
|
|
|
|
Abstract As one type of spatially offset Raman spectroscopy (SORS), inverse SORS is particularly suited to in vivo biomedical measurements due to its ring-shaped illumination scheme. To explain inhomogeneous Raman scattering during in vivo inverse SORS measurements, the light-tissue interactions when excitation and regenerated Raman photons propagate in skin tissue were studied using Monte Carlo simulation. An eight-layered skin model was first built based on the latest transmission parameters. Then, an open-source platform, Monte Carlo eXtreme (MCX), was adapted to study the distribution of 785 nm excitation photons inside the model with an inverse spatially shifted annular beam. The excitation photons were converted to emission photons by an inverse distribution method based on excitation flux with spatial offsets Δs of 1 mm, 2 mm, 3 mm and 5 mm. The intrinsic Raman spectra from separated skin layers were measured by continuous linear scanning to improve the simulation accuracy. The obtained results explain why the spectral detection depth gradually increases with increasing spatial offset, and address how the intrinsic Raman spectrum from deep skin layers is distorted by the reabsorption and scattering of the superficial tissue constituents. Meanwhile, it is demonstrated that the spectral contribution from subcutaneous fat will be improved when the offset increases to 5 mm, and the highest detection efficiency for dermal layer spectral detection could be achieved when Δs = 2 mm. Reasonably good matching between the calculated spectrum and the measured in vivo inverse SORS was achieved, thus demonstrating great utility of our modeling method and an approach to help understand the clinical measurements.
|
Received: 16 May 2023
Revised: 20 July 2023
Accepted manuscript online: 11 August 2023
|
|
PACS:
|
87.10.Rt
|
(Monte Carlo simulations)
|
| |
87.16.A-
|
(Theory, modeling, and simulations)
|
| |
87.50.sg
|
(Biophysical mechanisms of interaction)
|
| |
87.64.kp
|
(Raman)
|
|
| Fund: Project supported by the National Natural Science Foundation of China (Grant No. 61911530695) and the Key Research and Development Project of Shaanxi Province, China (Grant No. 2023-YBSF-671). |
Corresponding Authors:
Jia Zeng, Shuang Wang
E-mail: j.zeng@huawei.com;swang@nwu.edu.cn
|
Cite this article:
Yun-He Zhang(张云鹤), Huan-Zheng Zhu(朱桓正), Yong-Jiang Dong(董泳江), Jia Zeng(曾佳), Xin-Peng Han(韩新鹏), Ivan A. Bratchenko, Fu-Rong Zhang(张富荣), Si-Yuan Xu(许思源), and Shuang Wang(王爽) Reconstructing in vivo spatially offset Raman spectroscopy of human skin tissue using a GPU-accelerated Monte Carlo platform 2023 Chin. Phys. B 32 118702
|
[1] Guicheteau J and Hopkins R 2018 Trends Analyt. Chem. 103 209
[4] Matousek P 2006 Appl. Spectrosc. 60 1341
[5] Demers J L H, Esmonde-White F W L, Esmonde-White K A, Morris M D and Pogue B W 2015 Biomed. Opt. Express 6 793
[6] Wang L H, Jacques S L and Zheng L Q 1995 Comput. Meth. Prog. Bio. 47 131
[7] Zhu C G and Liu Q 2013 J. Biomed. Opt. 18 050902
[8] Periyasamy V and Pramanik M 2017 IEEE Rev. Biomed. Eng. 10 122
[9] Deng Y and Meglinski I 2010 Acta Phys. Sin. 59 1396 (in Chinese)
[10] Zang P and Wang H J 2010 Chin. Phys. Lett. 27 038701
[11] Ge C Y, Wu Z S, Bai J and Gong L 2017 Chin. Phys. B 26 064201
[12] Alerstam E, Svensson T and Andersson-Engels S 2008 J. Biomed. Opt. 13 60504
[13] Fang Q Q and Boas D A 2009 Opt. Express 17 20178
[14] Vishwanath K, Pogue B and Mycek M A 2002 Phys. Med. Biol. 47 3387
[15] Keller M D, Wilson R H, Mycek M A and Mahadevan-Jansen A 2010 Appl. Spectris 64 607
[16] Vishwanath K and Mycek M A 2004 Opt. Lett. 29 1512
[17] Vishwanath K and Mycek M A 2005 Opt. Express 13 7466
[18] Wilson R H, Dooley K A, Morris M D and Mycek M A 2021 Anal. Chem. 93 6755
[20] Shimojo Y, Nishimura T, Hazama H, Ozawa T and Awazu K 2020 Biomed. Opt. Express 25
[21] Mishchenko M I and Tuchin V 2006 J. Biomed. Opt. 11 064026
[23] Yu L, Nina-Paravecino F, Kaeli D K and Fang Q 2018 J. Biomed. Opt. 23 010504
[24] Song D L, Qin J, Chen Y S, Wang H F, Ning T, Wang S and Li J 2021 J. Raman Spectrosc. 52 1428
[25] Ning T, Li H P, Chen Y S, Zhang B P, Zhang F R and Wang S 2021 Vib. Spectrosc. 115 103260
[26] Li J, Li J, Qin J, Zeng H S and Wang S 2020 Spectrochim. Acta A Mol. Biomol. 239 118372
[27] Song D L, Chen T M, Wang S, Chen S L, Li H P, Yu F, Zhang J Y and Zhang Z 2020 Analyst 145 626
[28] Wang S, Zhao J H, Lui H, He Q L, Bai J T and Zeng H S 2014 J. Biophoton. 7 703
[29] Wang S, Zhao J H, Lui H, He Q L and Zeng H S 2011 J. Photochem. Photobiol. B 105 183
[30] Meglinski V I and Matcher S J 2002 Physiol. Meas. 23 741
[31] Meglinski V I and Matcher S J 2003 Comput Meth. Prog. Bio. 70 179
[32] Chen R, Huang Z W, Lui H, Hamzavi I, McLean D I, Xie S S and Zeng H S 2007 J. Photochem. Photobiol. B 86 219
[33] Zeng H S, MacAulay C, McLean D I and Palcic B 1997 J. Photochem. Photobiol. B 38 234
[34] Fang Q Q and Boas D A 2009 Opt. Express 17 20178
[35] Dumont A P, Fang Q Q and Patil C A 2021 J. Biophotonics 14 e202000377
[36] Wang S, Zhao J H, Lui H, He Q L and Zeng H S 2010 Spectroscopy 24 577
[37] Fang Q Q and Yan S J 2014 J. Biophoton. 7 703
[39] Wang H Q, Huang N Y, Zhao J H, Lui H, Korbelik M and Zeng H S 2011 J. Raman Spectrosc. 42 160
[40] Huang Z W, McWilliams A, H. Lui, McLean D I, Lam S and Zeng H S 2003 Int. J. Cancer 107 1047
[41] Schulz H and Baranska M 2007 Vib. Spectrosc. 43 13
[42] Huang Z W, McWilliams A, Lam S, English J, McLean D I, Lui H and Zeng H S 2003 Int. J. Oncol. 23 649
[43] Lakshmi R J, Kartha V B, Murali Krishna C, R. Solomon J G, Ullas G and Uma Devi P 2002 Radiat. Res. 157 175
[44] Bhattacharjee T, Kumar P, Maru G, Ingle A and Krishna C M 2014 Lasers Med Sci. 29 325
[45] Malini R, Venkatakrishna K, Kurien J, Pai K M, Rao L, Kartha V B and Krishna C M 2006 Biopolymers 81 179
[46] Krasnikov I, Suhr C, Seteikin A, Meinhardt-Wollweber M and Roth B 2019 J. Opt. Soc. Am. A 36 877
[47] Dumont A P and Patil C A 2018 Biomedical Vibrational Spectroscopy 2018:Advances in Research and Industry, January 27-28,2018, San Francisco, United States, p. 22 |
| No Suggested Reading articles found! |
|
|
Viewed |
|
|
|
Full text
|
|
|
|
|
Abstract
|
|
|
|
|
Cited |
|
|
|
|
Altmetric
|
|
blogs
Facebook pages
Wikipedia page
Google+ users
|
Online attention
Altmetric calculates a score based on the online attention an article receives. Each coloured thread in the circle represents a different type of online attention. The number in the centre is the Altmetric score. Social media and mainstream news media are the main sources that calculate the score. Reference managers such as Mendeley are also tracked but do not contribute to the score. Older articles often score higher because they have had more time to get noticed. To account for this, Altmetric has included the context data for other articles of a similar age.
View more on Altmetrics
|
|
|
