Quantitative Methods of Brain Tissue Differential Pathlength Factor Based on GS-SVM

Bao Chu; Yao Huang; Jingshu Ni; Chijian Zhang; Zhongsheng Li; Yuanzhi Zhang; Meili Dong; Quanfu Wang; Xia Wang; Yikun Wang

doi:10.3788/CJL202249.0507303

Chinese Journal of Lasers, Volume. 49, Issue 5, 0507303(2022)

Quantitative Methods of Brain Tissue Differential Pathlength Factor Based on GS-SVM

Bao Chu^1,2, Yao Huang^2,3、*, Jingshu Ni^2,3, Chijian Zhang¹, Zhongsheng Li^2,3, Yuanzhi Zhang^2,3, Meili Dong^2,3, Quanfu Wang^2,3, Xia Wang^2,3, and Yikun Wang^2,3、**

Author Affiliations

¹College of Physics and Electronic Information, Anhui Normal University, Wuhu, Anhui 241000, China

²Anhui Provincial Engineering Laboratory for Medical Optical Diagnosis & Treatment Technology and Instrument, Anhui Institute of Optics and Fine Mechanics, Hefei Institute of Physical Science, Chinese Academy of Science, Hefei, Anhui 230026, China

³Anhui Provincial Engineering Technology Research Center for Biomedical Optical Instrument, Wanjiang Center for Development of Emerging Industrial Technology, Tongling, Anhui 244000, China

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Figures & Tables(7)

Fig. 1. Flow chart of brain tissue differential pathlength factor prediction model based on GS-SVM

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Fig. 2. SVM parameter selection diagram after grid optimization

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Fig. 3. Comparison of prediction results given by Monte Carlo simulation, GS-SVM and BP-ANN prediction models

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Fig. 4. Correlation analysis of prediction results given by two prediction models and Monte Carlo simulation. (a) GS-SVM prediction model; (b) BP-ANN prediction model

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Table 1. Thickness and optical parameters of certain adult brain tissue at 800 nm wavelength

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Table 1. Thickness and optical parameters of certain adult brain tissue at 800 nm wavelength

Brain tissue	Thickness d /cm	Scattering coefficient μ_s /cm^－1	Absorption coefficient μ_a /cm^－1	Anisotropy factor g	Refractive index n
Scalp and skull	1	17.5	0.17	0.90	1.4
Cerebrospinal fluid	0.2	0.1	0.01	0.92	1.4
Gray matter	0.4	22	0.36	0.89	1.4
White matter	20	91	0.14	0.90	1.4

Table 2. Proportion of principal component after dimensionality reduction
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Table 2. Proportion of principal component after dimensionality reduction
Component Proportion of principal component
PC1 67.7
PC2 10.7
PC3 7.9
PC4 5.1
PC5 5.0

Table 3. Comparison of experimental results between BP-ANN and GS-SVM prediction models
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Table 3. Comparison of experimental results between BP-ANN and GS-SVM prediction models
Prediction model MSE R²
GS-SVM 0.0268 0.97
BP-ANN 0.25 0.92

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Bao Chu, Yao Huang, Jingshu Ni, Chijian Zhang, Zhongsheng Li, Yuanzhi Zhang, Meili Dong, Quanfu Wang, Xia Wang, Yikun Wang. Quantitative Methods of Brain Tissue Differential Pathlength Factor Based on GS-SVM[J]. Chinese Journal of Lasers, 2022, 49(5): 0507303

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Paper Information

Received: Nov. 10, 2021

Accepted: Dec. 15, 2021

Published Online: Mar. 9, 2022

The Author Email: Huang Yao (yhuang@aiofm.ac.cn), Wang Yikun (wyk@aiofm.ac.cn)

DOI:10.3788/CJL202249.0507303

Topics

laser devices and laser physics

Lasers and Laser Optics

Laser physics

laser manufacturing

Instrumentation, Measurement and Metrology

Table 1. Thickness and optical parameters of certain adult brain tissue at 800 nm wavelength

Table 1. Thickness and optical parameters of certain adult brain tissue at 800 nm wavelength

Table 2. Proportion of principal component after dimensionality reduction

Table 2. Proportion of principal component after dimensionality reduction

Table 3. Comparison of experimental results between BP-ANN and GS-SVM prediction models

Table 3. Comparison of experimental results between BP-ANN and GS-SVM prediction models