• Chinese Journal of Lasers
  • Vol. 45, Issue 6, 0607001 (2018)
Zhiya Chen1, Ying Ji1、*, Wenbo Tang1, Mingming Zhang2, Yuanyuan Xu1, and Yawei Wang1
Author Affiliations
  • 1 Faculty of Science, Jiangsu University, Zhenjiang, Jiangsu 212013, China
  • 2 School of Mechanical Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China
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    DOI: 10.3788/CJL201845.0607001 Cite this Article Set citation alerts
    Zhiya Chen, Ying Ji, Wenbo Tang, Mingming Zhang, Yuanyuan Xu, Yawei Wang. Optical Phase Characterization Method for Dynamic Characteristics of Neuronal Discharge[J]. Chinese Journal of Lasers, 2018, 45(6): 0607001 Copy Citation Text show less
    (a) Practical morphology[15] and (b) 3D simplified model of neurons
    Fig. 1. (a) Practical morphology[15] and (b) 3D simplified model of neurons
    (a) 2D and (b) 3D unwrapping phases of neuron model along z axis
    Fig. 2. (a) 2D and (b) 3D unwrapping phases of neuron model along z axis
    Phase of neuron. (a) Gradient distribution; (b) absolute value of gradient; (c) horizontal gradient curve
    Fig. 3. Phase of neuron. (a) Gradient distribution; (b) absolute value of gradient; (c) horizontal gradient curve
    Refractive index distribution through live neuron
    Fig. 4. Refractive index distribution through live neuron
    (a)-(h) Phases with neuron refractive indexes in range of 1.356-1.367; (i) relationship between phase value of monitoring point and refractive index
    Fig. 5. (a)-(h) Phases with neuron refractive indexes in range of 1.356-1.367; (i) relationship between phase value of monitoring point and refractive index
    (a) Model, (b) wrapped phase, and (c) unwrapped phase of neuron before compensation; (d) model, (e) wrapped phase, and (f) unwrapped phase of neuron after compensation
    Fig. 6. (a) Model, (b) wrapped phase, and (c) unwrapped phase of neuron before compensation; (d) model, (e) wrapped phase, and (f) unwrapped phase of neuron after compensation
    Nucleus and mitochondria in sample. (a) Unwrapped phase; (b) optical thickness; (c) physical thickness; (d) verification of predicted result
    Fig. 7. Nucleus and mitochondria in sample. (a) Unwrapped phase; (b) optical thickness; (c) physical thickness; (d) verification of predicted result
    Nucleus and mitochondria of neuronal substructure. (a)-(h) Phases with refractive index variation of cytoplasm; (i) relationship between phase value of monitoring point and difference of refractive index
    Fig. 8. Nucleus and mitochondria of neuronal substructure. (a)-(h) Phases with refractive index variation of cytoplasm; (i) relationship between phase value of monitoring point and difference of refractive index
    CorrespondingpointDistance betweentwo points /μmParameter
    ①-①34.92-
    ②-②29.9230 μm (Soma diameter)
    ③-③0.901 μm (Length of mitochondriain x direction)
    ④-④7.918 μm (Nucleus diameter)
    ⑤-⑤2.002 μm (Dendritic diameter)
    ⑥-⑥1.001 μm (Axon diameter)
    Table 1. Distance between phase gradient mutation points of neuron model in incident direction
    Zhiya Chen, Ying Ji, Wenbo Tang, Mingming Zhang, Yuanyuan Xu, Yawei Wang. Optical Phase Characterization Method for Dynamic Characteristics of Neuronal Discharge[J]. Chinese Journal of Lasers, 2018, 45(6): 0607001
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