Chinese Journal of Lasers, Volume. 51, Issue 9, 0907009(2024)

Research Progress in Near Infrared Spectral Tomography for Breast

Chengpu Wei1, Jinchao Feng1,2, Yaxuan Li1, Ting Hu1, Zhonghua Sun1,2, Kebin Jia1,2, and Zhe Li1,2、*
Author Affiliations
  • 1Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
  • 2Beijing Laboratory of Advanced Information Networks, Beijing 100876, China
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    Figures & Tables(13)
    Basic principle of NIRST. (a) Schematic of NIRST; (b) light-breast tissue interaction; (c) absorption spectra of four main chromophores
    Schematic diagram of three measurement modes commonly used in NIRST. (a) CW; (b) FD; (c) TD
    Representative CW systems. (a) CW system developed by Hielscher’s research group[31]; (b) CW system developed by PHILIPS company[32]; (c) Comfortscan system developed by DOBI Global company[34]
    Representative FD systems. (a) FD system developed by Paulsen’s research group[35]; (b) FD system developed by Yodh’s research group[36]
    TD system with 7 different wavelengths[39]. (a) Experimental setup; (b) physical photo of system
    Hybrid FD-CW system. (a) System with 9 wavelengths[44]; (b) system with 12 wavelengths[46-47]; (c) system developed at University of Pennsylvania[48]
    Multi-modality system for breast imaging. (a)(b)DBT/ NIRST system and interface detail[56,63-64]; (c) dual-mode system that fuses ultrasound and NIRST and its probe detail[61-62]
    Representative MRI/NIRST system and commonly used interface. (a) MRI/NIRST system with 9 wavelengths[68]; (b) circular interface[69]; (c) parallel board interface[76]; (d) triangular interface[68]; (e) strip interface[77]
    Multimodal fusion method. (a) Comparison of different prior fusion methods[75]; (b) experimental result of fusion of MRI and NIRST systems based on deep learning method[82]
    Representative results of breast tumor diagnosis using multimodality NIRST system. (a) DBT/NIRST[56]; (b) US/NIRST[60]; (c) MRI/NIRST[67]
    Efficacy monitoring of neoadjuvant chemotherapy utilizing NIRST system. (a) NAC prediction using NIRST system[46];
    • Table 1. Comparison of current commonly used breast imaging techniques and NIRST[24]

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      Table 1. Comparison of current commonly used breast imaging techniques and NIRST[24]

      CharacteristicX-rayUSMRINIRST
      CostLowLowHighLow
      Ionizing radiationYesNoNoNo
      Ease of operationLowHighHighLow
      PortableNoYesNoYes
      Agent requirementNoNot necessaryGenerally necessaryNot necessary
      Affected by breast densityYesYesNoYes
      Spatial resolutionVery highHighHigh (<1 mm)Low
      Degree of function fittingGoodExcellentExcellent
      Soft tissue contrastPoorGoodExcellentExcellent
      Maximum imaging depthExcellentGoodNo limit/ExcellentGood (~10 cm)
      Data acquisitionFastFastSlowFast
    • Table 2. Comparison of NIRST measurement modes

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      Table 2. Comparison of NIRST measurement modes

      FactorCWFDTDFD-CWTD-CW
      Light sourceSemiconductor laser/LED/laser diodeSolid-state laser/laser diodePulsed diode laserLaser diodePulsed diode laser/laser source
      DetectorCCD/PD/PMT/SiPM/APDPMT/SiPM/APDPMTPMT/PD/CCDPMT/CCD
      System costCheapModerateExpensiveModerateExpensive
      ComplexityLowModerateHighModerateHigh
      MeasurementAmplitude dataAmplitude and phase dataLight fluxAmplitude and phase dataLight flux/amplitude data
      Optical property parameterAbsorption coefficientAbsorption & scattering coefficientsAbsorption & scattering coefficientsAbsorption & scattering coefficientsAbsorption & scattering coefficients
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    Chengpu Wei, Jinchao Feng, Yaxuan Li, Ting Hu, Zhonghua Sun, Kebin Jia, Zhe Li. Research Progress in Near Infrared Spectral Tomography for Breast[J]. Chinese Journal of Lasers, 2024, 51(9): 0907009

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    Paper Information

    Category: biomedical photonics and laser medicine

    Received: Nov. 30, 2023

    Accepted: Jan. 29, 2024

    Published Online: Apr. 26, 2024

    The Author Email: Li Zhe (lizhe1023@bjut.edu.cn)

    DOI:10.3788/CJL231455

    CSTR:32183.14.CJL231455

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